From 5cff4ab2fa28cfcd6ecc030d23c23bebe2590f04 Mon Sep 17 00:00:00 2001 From: Dan McPherson Date: Wed, 30 Oct 2024 19:52:16 -0400 Subject: [PATCH] Add facilities for unit and functional tests There was one unit test already existing and this commit adds a couple of starter unit tests for mt_bench and mmlu. Signed-off-by: Dan McPherson --- .github/workflows/test.yml | 119 +++ .spellcheck-en-custom.txt | 4 + Makefile | 6 + README.md | 91 +- coverage-py3-unitcov.xml | 859 ++++++++++++++++++ durations/py3-unitcov.html | 770 ++++++++++++++++ .../mt_bench_branch/main/question.jsonl | 384 ++++++++ .../instructlab/granite-7b-lab.jsonl | 384 ++++++++ .../mt_bench_branch/main/question.jsonl | 67 ++ .../prometheus-8x7b-v2.0.jsonl | 67 ++ scripts/functional-tests.sh | 62 ++ {tests => scripts}/test_branch_gen_answers.py | 0 scripts/test_branch_generator.py | 35 + .../test_branch_judge_answers.py | 0 {tests => scripts}/test_gen_answers.py | 0 {tests => scripts}/test_judge_answers.py | 0 scripts/test_mmlu.py | 21 + .../test_mmlu_branch.py | 6 +- src/instructlab/eval/mmlu.py | 2 +- src/instructlab/eval/mt_bench.py | 6 +- tests/test_mmlu.py | 88 +- tests/test_mt_bench.py | 103 +++ tox.ini | 45 +- 23 files changed, 3085 insertions(+), 34 deletions(-) create mode 100644 .github/workflows/test.yml create mode 100644 coverage-py3-unitcov.xml create mode 100644 durations/py3-unitcov.html create mode 100644 eval_output/mt_bench_branch/main/question.jsonl create mode 100644 eval_output/mt_bench_branch/main/reference_answer/instructlab/granite-7b-lab.jsonl create mode 100644 mt_bench_branch_generator/mt_bench_branch/main/question.jsonl create mode 100644 mt_bench_branch_generator/mt_bench_branch/main/reference_answer/prometheus-eval/prometheus-8x7b-v2.0.jsonl create mode 100755 scripts/functional-tests.sh rename {tests => scripts}/test_branch_gen_answers.py (100%) create mode 100755 scripts/test_branch_generator.py rename {tests => scripts}/test_branch_judge_answers.py (100%) rename {tests => scripts}/test_gen_answers.py (100%) rename {tests => scripts}/test_judge_answers.py (100%) create mode 100755 scripts/test_mmlu.py rename tests/test_mmlubranch.py => scripts/test_mmlu_branch.py (76%) mode change 100755 => 100644 tests/test_mmlu.py create mode 100644 tests/test_mt_bench.py diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml new file mode 100644 index 0000000..448a0b7 --- /dev/null +++ b/.github/workflows/test.yml @@ -0,0 +1,119 @@ +# SPDX-License-Identifier: Apache-2.0 + +name: Test + +on: + workflow_dispatch: + push: + branches: + - "main" + - "release-**" + paths: + - '**.py' + - 'pyproject.toml' + - 'requirements**.txt' + - 'tox.ini' + - 'scripts/*.sh' # Used by this workflow + - '.github/workflows/test.yml' # This workflow + pull_request: + branches: + - "main" + - "release-**" + paths: + - '**.py' + - 'pyproject.toml' + - 'requirements**.txt' + - 'tox.ini' + - 'scripts/*.sh' # Used by this workflow + - '.github/workflows/test.yml' # This workflow + +env: + LC_ALL: en_US.UTF-8 + +defaults: + run: + shell: bash + +permissions: + contents: read + +jobs: + test: + name: "test: ${{ matrix.python }} on ${{ matrix.platform }}" + runs-on: "${{ matrix.platform }}" + strategy: + matrix: + python: + - "3.10" + - "3.11" + platform: + - "ubuntu-latest" + include: + - python: "3.11" + platform: "macos-latest" + steps: + - name: "Harden Runner" + uses: step-security/harden-runner@91182cccc01eb5e619899d80e4e971d6181294a7 # v2.10.1 + with: + egress-policy: audit # TODO: change to 'egress-policy: block' after couple of runs + + - name: Checkout + uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 + with: + # https://github.com/actions/checkout/issues/249 + fetch-depth: 0 + + - name: Free disk space + if: matrix.platform != 'macos-latest' + uses: ./.github/actions/free-disk-space + + - name: Install the expect package + if: startsWith(matrix.platform, 'ubuntu') + run: | + sudo apt-get install -y expect + + - name: Install tools on MacOS + if: startsWith(matrix.platform, 'macos') + run: | + brew install expect coreutils bash + + - name: Setup Python ${{ matrix.python }} + uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0 + with: + python-version: ${{ matrix.python }} + cache: pip + cache-dependency-path: | + **/pyproject.toml + **/requirements*.txt + + - name: Remove llama-cpp-python from cache + run: | + pip cache remove llama_cpp_python + + - name: Cache huggingface + uses: actions/cache@6849a6489940f00c2f30c0fb92c6274307ccb58a # v4.1.2 + with: + path: ~/.cache/huggingface + # config contains DEFAULT_MODEL + key: huggingface-${{ hashFiles('src/instructlab/configuration.py') }} + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install tox tox-gh>=1.2 + + - name: Run unit and functional tests with tox + run: | + tox + + - name: Remove llama-cpp-python from cache + if: always() + run: | + pip cache remove llama_cpp_python + + test-workflow-complete: + needs: ["test"] + runs-on: ubuntu-latest + steps: + - name: Test Workflow Complete + run: echo "Test Workflow Complete" diff --git a/.spellcheck-en-custom.txt b/.spellcheck-en-custom.txt index 33a582f..5967218 100644 --- a/.spellcheck-en-custom.txt +++ b/.spellcheck-en-custom.txt @@ -6,6 +6,8 @@ Backport backported benchmarking codebase +cli +dev dr eval gpt @@ -16,9 +18,11 @@ jsonl justfile MMLU openai +pre SDG Tatsu tl TODO +tox venv vllm diff --git a/Makefile b/Makefile index 3315b72..6155b53 100644 --- a/Makefile +++ b/Makefile @@ -54,3 +54,9 @@ spellcheck-sort: .spellcheck-en-custom.txt ## Sort spellcheck directory .PHONY: verify verify: check-tox ## Run linting, typing, and formatting checks via tox tox p -e fastlint,mypy,ruff + +##@ Development + +.PHONY: tests +tests: check-tox ## Run unit and type checks + tox -e py3-unit,mypy diff --git a/README.md b/README.md index f96bd6f..e72b41a 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,7 @@ # eval ![Lint](https://github.com/instructlab/eval/actions/workflows/lint.yml/badge.svg?branch=main) +![Tests](https://github.com/instructlab/eval/actions/workflows/test.yml/badge.svg?branch=main) ![Build](https://github.com/instructlab/eval/actions/workflows/pypi.yaml/badge.svg?branch=main) ![Release](https://img.shields.io/github/v/release/instructlab/eval) ![License](https://img.shields.io/github/license/instructlab/eval) @@ -77,20 +78,32 @@ MMLU Branch is an adaptation of MMLU that is designed to test custom knowledge t A teacher model is used to generate new multiple choice questions based on the knowledge document included in the taxonomy Git branch. A “task” is then constructed that references the newly generated answer choices. These tasks are then used to score the model’s grasp on new knowledge the same way MMLU works. Generation of these tasks are done as part of the [InstructLab SDG](https://github.com/instructlab/sdg) library. -## MT-Bench / MT-Bench Branch Testing Steps +## Development > **⚠️ Note:** Must use Python version 3.10 or later. +### Set up your dev environment + +The following tools are required: + +- [`git`](https://git-scm.com) +- [`python`](https://www.python.org) (v3.10 or v3.11) +- [`pip`](https://pypi.org/project/pip/) (v23.0+) +- [`bash`](https://www.gnu.org/software/bash/) (v5+, for functional tests) + +#### Optional: Use [cloud-instance.sh](https://github.com/instructlab/instructlab/tree/main/scripts/infra) to launch and setup an instance + ```shell -# Optional: Use cloud-instance.sh (https://github.com/instructlab/instructlab/tree/main/scripts/infra) to launch and setup the instance -scripts/infra/cloud-instance.sh ec2 launch -t g5.4xlarge +scripts/infra/cloud-instance.sh ec2 launch -t g6.2xlarge scripts/infra/cloud-instance.sh ec2 setup-rh-devenv scripts/infra/cloud-instance.sh ec2 install-rh-nvidia-drivers scripts/infra/cloud-instance.sh ec2 ssh sudo reboot scripts/infra/cloud-instance.sh ec2 ssh +``` +#### Regardless of how you setup your instance -# Regardless of how you setup your instance +```shell git clone https://github.com/instructlab/taxonomy.git && pushd taxonomy && git branch rc && popd git clone --bare https://github.com/instructlab/eval.git && git clone eval.git/ && cd eval && git remote add syncrepo ../eval.git python3 -m venv venv @@ -99,6 +112,68 @@ pip install -r requirements.txt pip install -r requirements-dev.txt pip install -e . pip install vllm +``` + +### Testing + +Before pushing changes to GitHub, you need to run the tests as shown below. They can be run individually as shown in each sub-section +or can be run with the one command: + +```shell +tox +``` + +#### Unit tests + +Unit tests are enforced by the CI system using [`pytest`](https://docs.pytest.org/). When making changes, run these tests before pushing the changes to avoid CI issues. + +Running unit tests can be done with: + +```shell +tox -e py3-unit +``` + +By default, all tests found within the `tests` directory are run. However, specific unit tests can run by passing filenames, classes and/or methods to `pytest` using tox positional arguments. The following example invokes a single test method `test_mt_bench` that is declared in the `tests/test_mt_bench.py` file: + +```shell +tox -e py3-unit -- tests/test_mt_bench.py::test_mt_bench +``` + +#### Functional tests + +Functional tests are enforced by the CI system. When making changes, run the tests before pushing the changes to avoid CI issues. + +Running functional tests can be done with: + +```shell +tox -e py3-functional +``` + +#### Coding style + +Cli follows the python [`pep8`](https://peps.python.org/pep-0008/) coding style. The coding style is enforced by the CI system, and your PR will fail until the style has been applied correctly. + +We use [pre-commit](https://pre-commit.com/) to enforce coding style using [`black`](https://github.com/psf/black), and [`isort`](https://pycqa.github.io/isort/). + +You can invoke formatting with: + +```shell +tox -e ruff +``` + +In addition, we use [`pylint`](https://www.pylint.org) to perform static code analysis of the code. + +You can invoke the linting with the following command + +```shell +tox -e lint +``` + +### MT-Bench / MT-Bench Branch Example Usage + +Launch vllm serving granite-7b-lab + +```shell python -m vllm.entrypoints.openai.api_server --model instructlab/granite-7b-lab --tensor-parallel-size 1 ``` @@ -107,8 +182,8 @@ In another shell window ```shell export INSTRUCTLAB_EVAL_FIRST_N_QUESTIONS=10 # Optional if you want to shorten run times # Commands relative to eval directory -python3 tests/test_gen_answers.py -python3 tests/test_branch_gen_answers.py +python3 scripts/test_gen_answers.py +python3 scripts/test_branch_gen_answers.py ``` Example output tree @@ -139,8 +214,8 @@ eval_output/ ``` ```shell -python3 tests/test_judge_answers.py -python3 tests/test_branch_judge_answers.py +python3 scripts/test_judge_answers.py +python3 scripts/test_branch_judge_answers.py ``` Example output tree diff --git a/coverage-py3-unitcov.xml b/coverage-py3-unitcov.xml new file mode 100644 index 0000000..fa0fed7 --- /dev/null +++ b/coverage-py3-unitcov.xml @@ -0,0 +1,859 @@ + + + + + + /Users/dmcphers/Code/eval + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/durations/py3-unitcov.html b/durations/py3-unitcov.html new file mode 100644 index 0000000..5dc7b3d --- /dev/null +++ b/durations/py3-unitcov.html @@ -0,0 +1,770 @@ + + + + + py3-unitcov.html + + + +

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+ \ No newline at end of file diff --git a/eval_output/mt_bench_branch/main/question.jsonl b/eval_output/mt_bench_branch/main/question.jsonl new file mode 100644 index 0000000..6cbc3cc --- /dev/null +++ b/eval_output/mt_bench_branch/main/question.jsonl @@ -0,0 +1,384 @@ +{"qna_file": "foundational_skills/reasoning/temporal_reasoning/qna.yaml", "question_id": "186038087183832915305325427194848080180", "category": "taxonomy", "turns": ["Consider I went to a party. I reached before John. David arrived after. Joe reached before me. John arrived after David. Who reached first and who reached last?\n"], "reference": ["Hello! I'm here to help you understand the order of arrival at a party. Let's analyze the situation step by step:\n\n1. Joe reached first (before you).\n2. You arrived next.\n3. David arrived after you.\n4. John reached last (after David).\n\nBased on this information, Joe was the first one to arrive at the party, followed by you, then David, and finally John.\n"]} +{"qna_file": "foundational_skills/reasoning/temporal_reasoning/qna.yaml", "question_id": "215948836162377945439927729315702901781", "category": "taxonomy", "turns": ["At a celebration, Peter came in before Olivia. Sophia arrived after. Emma got there before Peter. Olivia arrived after Sophia. Who reached first, and who reached last?"], "reference": ["Hello! I'm here to help you understand the order of arrival at a celebration. Let's analyze the situation step by step:\n\n1. Emma reached first (before Peter).\n2. Peter arrived next.\n3. Sophia arrived after Peter.\n4. Olivia reached last (after Sophia).\n\nBased on this information, Emma was the first one to arrive at the celebration, followed by Peter, then Sophia, and finally Olivia.\n"]} +{"qna_file": "foundational_skills/reasoning/temporal_reasoning/qna.yaml", "question_id": "239330987684746270140406408262195855542", "category": "taxonomy", "turns": ["In a photography competition, James captured a photo before Sarah. Alex's entry came after. Emily took a photo before James. Sarah submitted her photo after Alex. Whose photo was captured first, and whose photo was captured last?\n"], "reference": ["Hello! I'm here to help you understand the order of photos in a photography competition. Let's analyze the situation step by step:\n\n1. Emily captured her photo first (before James).\n2. James captured a photo next.\n3. Alex entered with a photo after James.\n4. Sarah submitted her photo last (after Alex).\n\nBased on this information, Emily's photo was captured first, followed by Jame's entry, then Alex's photo, and finally Sarah's submission.\n\n"]} +{"qna_file": "foundational_skills/reasoning/mathematical_reasoning/qna.yaml", "question_id": "242886683457958175188717141363968452444", "category": "taxonomy", "turns": ["What is heavier? A kilo of feathers, or a pound of hammers?\n"], "reference": ["A kilo of feathers is heavier than a pound of hammers. 1 kilo is equal to ~2.2046 pounds, so no matter what you are weighing 1 kilo will be heavier than 1 pound\n"]} +{"qna_file": "foundational_skills/reasoning/mathematical_reasoning/qna.yaml", "question_id": "233229534488351620628102146533821087135", "category": "taxonomy", "turns": ["10 servers require 1 administrator to manage.\nHow many servers can I manage with 10 administrators?"], "reference": ["100 servers. If 1 Administrator can manage 10 servers, and we increase that number by 10, we can assume that 10 administrators could handle 10 times the number of servers 10x10=100 \n"]} +{"qna_file": "foundational_skills/reasoning/mathematical_reasoning/qna.yaml", "question_id": "238611654959537552555222005657320456359", "category": "taxonomy", "turns": ["I have a server with 2 sockets, with 16 cores each. How many OpenShift subscriptions do I need to purchase, with either 2 socket pair or 2 core pair licensing model?\n\n"], "reference": ["You need either 1 x 2 socket subscription, or 16 x 2-core subscriptions.\n"]} +{"qna_file": "foundational_skills/reasoning/theory_of_mind/qna.yaml", "question_id": "251187812289715631442369307574029139622", "category": "taxonomy", "turns": ["Read the scenario and answer the following question: Scenario: \"The morning of the high school dance Sarah placed her high heel shoes under her dress and then went shopping. That afternoon, her sister borrowed the shoes and later put them under Sarah\u2019s bed.\" Question: When Sarah gets ready, does she assume her shoes are under her dress?\n"], "reference": ["Sarah placed her shoes under her dress before she went shopping. Her sister borrowed the shoes and put them under Sarah\u2019s bed. Sarah doesn\u2019t know that her sister borrowed her shoes. Therefore, the answer is yes, she assumes that the shoes are still under her dress.\n"]} +{"qna_file": "foundational_skills/reasoning/theory_of_mind/qna.yaml", "question_id": "13217000458772167243141483582150185270", "category": "taxonomy", "turns": ["Read the scenario and answer the following question: Scenario: \"Expecting the game to be postponed because of the rain, the Garcia family took the subway home. The score was tied, 3-3. During their commute the rain stopped, and the game soon ended with a score of 5- 3.\" Question: When the Garcia family arrives home, do they believe the score is 5-3?"], "reference": ["When the Garcia family left, the game was tied, 3-3, and they expected the game to be postponed due to the rain. So, they don't know that the game ended during their commute with a score of 5-3. Therefore, the answer is no, they don't believe the score is 5-3.\n"]} +{"qna_file": "foundational_skills/reasoning/theory_of_mind/qna.yaml", "question_id": "204625558555813571908604531813394942740", "category": "taxonomy", "turns": ["Read the scenario and answer the following question: Scenario: \"When Lisa left Jacob, he was deep asleep on the beach. A few minutes later a wave woke him. Seeing Lisa was gone, Jacob decided to go swimming.\" Question: Does Lisa now believe that Jacob is asleep?\n"], "reference": ["When Lisa left Jacob, he was asleep on the beach. So, Lisa doesn't know that Jacob woke up and went swimming. Therefore, the answer is yes, Lisa believes that Jacob is still asleep on the beach.\n"]} +{"qna_file": "foundational_skills/reasoning/theory_of_mind/qna.yaml", "question_id": "318549297918654447514356663453222375932", "category": "taxonomy", "turns": ["Read the scenario and answer the following question: Scenario: \"The weather was so warm today that all the tulips in Pam\u2019s backyard suddenly bloomed. The tulips next to Pam\u2019s office still have not yet flowered, though. Pam has been at work all day.\" Question: When Pam is driving home after work, does she assume her tulips have bloomed?\n"], "reference": ["Since Pam has been at work all day, she doesn't know that the tulips in her backyard have suddenly bloomed. Therefore, the answer is no, when driving home she doesn't assume that her tulips have bloomed.\n"]} +{"qna_file": "foundational_skills/reasoning/theory_of_mind/qna.yaml", "question_id": "319839001852604230671498528924789687512", "category": "taxonomy", "turns": ["Read the scenario and answer the following question: Scenario: \"When Jeff got ready this morning, he put on a light pink shirt instead of a white one. Jeff is colorblind, so he can\u2019t tell the difference between subtle shades of color.\" Question: Does Jeff believe he is wearing a white shirt?"], "reference": ["The scenario mentions that Jeff put on a pink shirt instead of a white one. This means he wanted to put on a white shirt but since he is colorblind and can't tell the difference, he mistakenly put on a pink shirt. He doesn't know we is wearing a pink short. Therefore, the answer is yes, he think he is wearing a white shirt.\n"]} +{"qna_file": "foundational_skills/reasoning/theory_of_mind/qna.yaml", "question_id": "44149513413682805844664797051944968063", "category": "taxonomy", "turns": ["Read the scenario and answer the following question: Scenario: \"To detect intruders, the lab uses an automated system for recording voices. In the empty lab one night, a computer error occurs and a synthetic voice reads the error message.\" Question: Is the number of people in the lab that night zero?\n"], "reference": ["The scenario mentions that the lab was empty that night. Therefore, the answer is yes, the number of people in the lab that night is zero.\n"]} +{"qna_file": "foundational_skills/reasoning/theory_of_mind/qna.yaml", "question_id": "63498394932145751423557426078215489111", "category": "taxonomy", "turns": ["Read the scenario and answer the following question: Scenario: \"Accounts of the country\u2019s economic success were recorded in books from the early 1900s. Soon after, a horrible plague hit the country, and the country was sent into an economic depression.\" Question: Do early 1900s novels portray the country as experiencing economic wealth?"], "reference": ["The scenario mentions that the country\u2019s economic success were recorded in books from the early 1900s. Therefore, the answer is yes, early 1900s novels portray the country as experiencing economic wealth.\n"]} +{"qna_file": "foundational_skills/reasoning/theory_of_mind/qna.yaml", "question_id": "205308421853532842346513294370366642610", "category": "taxonomy", "turns": ["Read the scenario and answer the following question: Scenario: \"Part of the garden is supposed to be reserved for the roses; it\u2019s labeled accordingly. Recently the garden has run wild, and dandelions have taken over the entire flower bed.\" Question: Does the label say these flowers are roses?\n"], "reference": ["The scenario mentions that a part of garden is reserved for roses and it has a label showing that. Therefore, the answer is yes, the label say the flowers are roses.\n"]} +{"qna_file": "foundational_skills/reasoning/unconventional_reasoning/lower_score_wins/qna.yaml", "question_id": "310855955988527299360696225432626887530", "category": "taxonomy", "turns": ["Part of golf is trying to get a higher point total than others. Yes or No?\n"], "reference": ["No that is incorrect, In golf, the objective is to score as few points as possible through various methods such as driving, chipping, and putting. The player with the lowest score at the end of the round wins.\n"]} +{"qna_file": "foundational_skills/reasoning/unconventional_reasoning/lower_score_wins/qna.yaml", "question_id": "78736441356384569630737498464735933862", "category": "taxonomy", "turns": ["In the card game rummy does the player with the highest score or the lowest score win."], "reference": ["In rummy, the player with the lowest score wins. \n"]} +{"qna_file": "foundational_skills/reasoning/unconventional_reasoning/lower_score_wins/qna.yaml", "question_id": "118971624301146257272024515530524869077", "category": "taxonomy", "turns": ["one runner has a time of 4h 30 and another a time of 4h 20, which one wins?\n"], "reference": ["In most running competitions, including marathons, the winner is the runner who completes the racecourse in the shortest amount of time. So, in this case, the runner with a time of 4 hours and 20 minutes would win because they completed the race faster than the runner with a time of 4 hours and 30 minutes.\n"]} +{"qna_file": "foundational_skills/reasoning/common_sense_reasoning/qna.yaml", "question_id": "292983752102232095864714663481939268406", "category": "taxonomy", "turns": ["There are 3 killers in a room. A new person comes into the room and kills one of the 3 people. How many killers are in the room now? Explain the answer step by step.\n"], "reference": ["The answer is 3 killers. A person comes into the room and kills one of the 3 killers, so 2 of the 3 killers remain. However the new person, having killed one of the killers, is now a killer. Hence there are 3 killers in the room. \n"]} +{"qna_file": "foundational_skills/reasoning/common_sense_reasoning/qna.yaml", "question_id": "79697302777874382304266575075184254147", "category": "taxonomy", "turns": ["I am drying some shirts in a wide open space in the sun. If it takes 4 hours to dry 4 shirts, how many hours does it take to dry 8 shirts?"], "reference": ["The answer is 4 hours. Since I am drying the shirts in the sun, it does not matter if I dry 4 or 8 shirts, it will take the same amount of time.\n"]} +{"qna_file": "foundational_skills/reasoning/common_sense_reasoning/qna.yaml", "question_id": "320590730897033119915839694782416720843", "category": "taxonomy", "turns": ["Jack and Jill are in a room with a ball, box, and a basket. Jack put the ball in the basket and then went to school. After Jack left, Jill put the ball in the box and went to school. Where is the ball now? Explain step by step. \n"], "reference": ["The ball is now in the box. Jack put the ball in the basket. After he left, Jill put the ball in the box, so it is now in the box.\n"]} +{"qna_file": "foundational_skills/reasoning/linguistics_reasoning/logical_sequence_of_words/qna.yaml", "question_id": "38178989809535383561614815864559057197", "category": "taxonomy", "turns": ["Arrange the words given below in a meaningful sequence.\n1.Police\u00a0 2. Punishment\u00a0 3. Crime\u00a0 4. Judge\u00a0 5. Judgment\n"], "reference": ["Here is a meaningful sequence:\n1. Crime\n2. Police\n3. Judge\n4. Judgment\n5. Punishment\n"]} +{"qna_file": "foundational_skills/reasoning/linguistics_reasoning/logical_sequence_of_words/qna.yaml", "question_id": "6767681416681346381744048511682034386", "category": "taxonomy", "turns": ["Arrange the following words in a meaningful sequence.\n1. Infection\n2. Consultation\n3. Doctor\n4. Treatment\n5. Recovery"], "reference": ["Here is a meaningful sequence:\n1. Infection\n2. Doctor\n3. Consultation\n4. Treatment\n5. Recovery\n"]} +{"qna_file": "foundational_skills/reasoning/linguistics_reasoning/logical_sequence_of_words/qna.yaml", "question_id": "53622117114584765484861387700826501422", "category": "taxonomy", "turns": ["Arrange the words given below in a meaningful sequence.\na.Word b.Paragraph c.Sentence d.Letters e.Phrase\n"], "reference": ["Here is a meaningful sequence:\n1. Letters\n2. Word\n3. Phrase\n4. Sentence\n5. Paragraph\n"]} +{"qna_file": "foundational_skills/reasoning/linguistics_reasoning/odd_one_out/qna.yaml", "question_id": "324795436124537533142216411539653654022", "category": "taxonomy", "turns": ["Which word is the odd man out? Also give an explanation why\nA. trivial\nB. unimportant\nC. important\nD. insignificant\n"], "reference": ["C. important. Remaining are synonyms of each other.\n"]} +{"qna_file": "foundational_skills/reasoning/linguistics_reasoning/odd_one_out/qna.yaml", "question_id": "286487527560524977650000710931754090809", "category": "taxonomy", "turns": ["Which word does NOT belong with the others? Also give an explanation why\nA. index\nB. glossary\nC. chapter\nD. book"], "reference": ["D. Book. Rest are all parts of a book.\n"]} +{"qna_file": "foundational_skills/reasoning/linguistics_reasoning/odd_one_out/qna.yaml", "question_id": "193212596901443399313689097544179034637", "category": "taxonomy", "turns": ["Which word is the odd man out? Also give an explanation why\nA. hate\nB. fondness\nC. liking\nD. attachment\n"], "reference": ["A. hate. Rest are positive emotions.\n"]} +{"qna_file": "foundational_skills/reasoning/linguistics_reasoning/object_identification/qna.yaml", "question_id": "9376129652505313748988900880362788597", "category": "taxonomy", "turns": ["How many colors are there in this list: four, five, twenty six, blue, alpha, pink.\n"], "reference": ["The list contains two distinct colors - blue and pink.\n"]} +{"qna_file": "foundational_skills/reasoning/linguistics_reasoning/object_identification/qna.yaml", "question_id": "95202116628166667051208889618180112758", "category": "taxonomy", "turns": ["How many human body parts in this list and what are they: bone, table, moon, tongue, mercury, postulate, America, nails."], "reference": ["There are 2 human body parts: Bone and tongue. Depending on if you consider nails to be fingernails, then you could also include them as a third body part.\n"]} +{"qna_file": "foundational_skills/reasoning/linguistics_reasoning/object_identification/qna.yaml", "question_id": "255872237613884807711965944467225129033", "category": "taxonomy", "turns": ["In the list which items are associated with both humans and animals: wisdom, utility, microscope, intestine, cheating, divorce, pulling, magnetism, space, ludo, axiom?\n"], "reference": ["The following items in the list are associated with both humans and animals: intestine, cheating. \n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/causal/qna.yaml", "question_id": "225571771206761594158263437563717696010", "category": "taxonomy", "turns": ["If it is summer, then the days are longer. Are the days longer if it is not summer ?\n"], "reference": ["While days tend to be longer in the summer, just because it is not summer doesn't mean days are necessarily shorter.\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/causal/qna.yaml", "question_id": "31075242281634831797237789507450794676", "category": "taxonomy", "turns": ["If all cats are mammals and some mammals are black, can we conclude that some cats are black?"], "reference": ["No, we cannot conclusively conclude that some cats are black based solely on the given premises. The statement \"some mammals are black\" does not necessarily guarantee that among those mammals are cats.\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/causal/qna.yaml", "question_id": "186605568946423368062781809941052703119", "category": "taxonomy", "turns": ["If all squares are rectangles and a rectangle has four sides, can we conclude that all squares have four sides?\n"], "reference": ["Yes, we can conclude that all squares have four sides based on the given premises.\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "122000398825150483085330538623944234347", "category": "taxonomy", "turns": ["i have 7 kids and i have only one bun and i need to share it without splitting, tell me the split fraction\n"], "reference": ["If you have one bun and need to share it among 7 kids without splitting it, each child would receive 1/7 of the bun. So the split fraction would be 1/7.\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "321250237548041497069373588875103125878", "category": "taxonomy", "turns": ["If a doctor gives you three pills and tells you to take one pill every half hour, how long would the pills last?"], "reference": ["The doctor instructs you to take one pill every half hour. So, you would take the first pill immediately, the second pill after 30 minutes, and the third pill after another 30 minutes, totaling one hour. After one hour, all three pills would have been consumed.\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "199975546227160776896763216962472780767", "category": "taxonomy", "turns": ["If it takes 5 machines 5 minutes to make 5 widgets, how long would it take 100 machines to make 100 widgets?\n"], "reference": ["The answer to this question is still 5 minutes.\nThe rate at which the widgets are produced remains constant regardless of the number of machines. Each machine produces one widget in 5 minutes, so whether there are 5 machines or 100 machines, it would still take 5 minutes to produce 100 widgets.\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "248025386135807391370680093872004527973", "category": "taxonomy", "turns": ["Which of the following is the odd one out?\nA) Earth\nB) Mars\nC) Jupiter\nD) Moon\n"], "reference": ["The odd one out is Moon. All of the others are planets from the solar system, but Moon is a satillite that orbits around the Earth.\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "163998429574116457272751395628485792572", "category": "taxonomy", "turns": ["A, B and C are sisters, D is the brother of E and E is the daughter of B. How is A related to D ?"], "reference": ["A is D's aunt. \nD is the brother of E\nE is the daughter of B, making D the son of B\nB is the sister of A, making A the aunt of both D and E. \n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "283956719709768162058781448709604191788", "category": "taxonomy", "turns": ["Which number should come next in the series, 48, 24, 12, ......?\n"], "reference": ["6\\. The series is dividing the the number in half at each step\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "76341820815379879552305133803024133009", "category": "taxonomy", "turns": ["Jane runs faster than Joe. Joe runs faster than Sam. Does Sam run faster than Jane?\n"], "reference": ["Sam doesn't run faster than Jane as Jane runs faster than Joe and Joe runs faster than Sam.\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "285694428740467711736111444912935277221", "category": "taxonomy", "turns": ["You're an expert in house chores. It takes 4 hours to dry 5 shirts in open air. How long does it take to dry 20 shirts?"], "reference": ["When drying clothes outside, several factors can affect the drying time, such as temperature, humidity, wind, and the number of shirts.\nIf there's sufficient space for drying shirts in parallel, drying 20 shirts could take as long as drying 5 shirts so 4 hours. However, when there's only sufficient space for 5 shirts, you'll need to dry the shirts in batches so considering that you need to dry 20 shirts, you would need 4 batches bringing the total time to 16 hours.\n\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "171343142260026728274898175026241576716", "category": "taxonomy", "turns": ["Marc and John are together in a room with a ball, a basket and a cupboard. Marc puts the ball in the basket and leaves the room. John takes the ball and puts in the cupboard and leaves the room. When they return in the evening, they don't know what happened to the ball after they've left the room. Where do they each think the ball is? Explain your reasoning step by step.\n\n\n"], "reference": ["Marc thinks the ball is in the basket as that's where it was when he left the room. John, on the other hand, thinks the ball is in the cupboard where he put it before he left the room.\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "97084977809626648492150587285270862247", "category": "taxonomy", "turns": ["Mike's mom has 4 kids. 3 of them are Luis, Drake and Matilda. What is the name of the 4th kid?\n"], "reference": ["Based on the information provided, the names of Mike's mom's four kids are Luis, Drake, Matilda, and Mike. Therefore the name of the 4th kid is Mike.\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "223020763880202331327745094787031161517", "category": "taxonomy", "turns": ["4 cars left San Fransisco at 12pm and they reached San Jose, 41.60 mi apart, at 1pm. How much time will be needed for 8 cars? Consider they have similar speed."], "reference": ["Let's analyze the situation step by step:\n\n1\\. We know that 4 cars traveled from San Francisco to San Jose, which is 41.60 miles apart.\n2\\. Each car travels at a similar speed. Having more cars on the road will not reduce the time that any one car takes to drive from San Francisco to San Jose.\n3\\. Therefore, the time it takes for each car to travel the distance is same, 1 hour.\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "35247899971565308536470213322269454259", "category": "taxonomy", "turns": ["Jane has 1 car. When she is at home then her car is in the driveway. Jane is now at her office. Can you find the number of car in her driveway now?\n"], "reference": ["Based on the information provided, we know that Jane has one car and that it is in the driveway when she is at home. However, when Jane is at her office, we know that she used her car for commuting. This implies that the car is not in the driveway. As Jane only has 1 car, then there must be no cars in Jane's driveway now. Therefore the answer is 0.\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "138091241521529880319109143777337935312", "category": "taxonomy", "turns": ["\"You are a teacher and need to give grade to the students. The grades are defined as follows:\\n\\nFor marks less than 40, grade is F.\\nFor marks between 40 and 60, grade is C.\\nFor marks between 60 and 80, grade is B.\\nFor marks above 80, grade is A.\\n\\nYou will be given a student's marks as input and you need to output the grade.\\n\\nMarks : 47\\nGrade:\\u00a0\\n\"\n"], "reference": ["C (The student's marks is between 40 and 60.)\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "213355609015880795968163047748418640088", "category": "taxonomy", "turns": ["You are a fitness trainer designing workout plans for clients. Based on number of hours a client exercised in a week, determine and output the appropriate fitness level:\n\nFor less than 3 hours, fitness level is Beginner.\nFor 3 to 5 hours, fitness level is Intermediate.\nFor 5 to 7 hours, fitness level is Advanced.\nFor more than 7 hours, fitness level is Expert.\n\nWeekly exercise hours: 4\nFitness level: "], "reference": ["Intermediate\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "44339368633466322891100739551808735676", "category": "taxonomy", "turns": ["As a travel guide, you want to assess the experiences of tourists. Based on the number of landmarks visited by a tourist in a city, determine the travel exploration level using the following guidelines: \n\nFor 0 to 2 landmarks, exploration level is Casual. \nFor 3 to 5 landmarks, exploration level is Enthusiast. \nFor 6 to 8 landmarks, exploration level is Adventurer. \nFor more than 8 landmarks, exploration level is Explorer. \n\nNumber of landmarks visited: 4 \nExploration level:\u00a0\n"], "reference": ["Enthusiast\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/tabular/qna.yaml", "question_id": "234233870159341419842865244848953191242", "category": "taxonomy", "turns": ["Given the following table, predict the next value in the sequence:\n\nDay\tTemperature (\u00b0C)\n1\t20\n2\t22\n3\t25\n4\t28\n5\t?\n\n"], "reference": ["To predict the next value in the temperature sequence, we can look at the pattern in the previous values. The differences between consecutive temperatures are:\n\n2 (day 2 - day 1)\n3 (day 3 - day 2)\n3 (day 4 - day 3)\n4 (day 5 - day 4)\n\nThis increasing difference suggests that the temperature will continue to increase. To find the next temperature, we can add the difference from day 4 to the temperature of day 4:\n\nNext temperature = Day 4 temperature + Difference\n= 28 \u00b0C + 3 \u00b0C\n= 31 \u00b0C\n\nTherefore, the next value in the sequence is 31 degrees Celsius.\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/tabular/qna.yaml", "question_id": "212185784796983471810902311649092104842", "category": "taxonomy", "turns": ["You're an analyst examining the performance of a retail company's product lines across different regions. Analyze the sales data provided in the table below and identify the best-performing product line in each region. For example the best performing product in North is Product B. Similarly identify for other regions as well.\n\nRegion\tProduct A Sales (in $)\tProduct B Sales (in $)\tProduct C Sales (in $)\nNorth\t$10,000\t $12,000\t $9,000\nSouth $8,000\t $10,000\t $11,000\nEast\t $11,000\t $9,000\t $10,000\nWest\t$9,000\t $11,000\t $12,000"], "reference": ["North: Product A\nSouth: Product C\nEast: Product A\nWest: Product C\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/tabular/qna.yaml", "question_id": "200656539618809357681888915956904200911", "category": "taxonomy", "turns": ["Consider the following table representing the number of fruits bought by three different people (A, B, C) at a fruit stall over three days (Day 1, Day 2, Day 3):\n\nPerson\tDay 1\tDay 2\tDay 3\nA\t5\t8\t4\nB\t3\t6\t7\nC\t2\t5\t6\nOn which day lowest number of fruits were brought?\n"], "reference": ["To calculate the total number of fruits bought on each day we first sum up the number of fruits bought in each day.\nTotal fruits for A = 5 (Day 1) + 8 (Day 2) + 4 (Day 3) = 17\nTotal fruits for B = 3 (Day 1) + 6 (Day 2) + 7 (Day 3) = 16\nTotal fruits for C = 2 (Day 1) + 5 (Day 2) + 6 (Day 3) = 13\n\nHence on Day 3 the lowest number of fruits were bought.\n"]} +{"qna_file": "compositional_skills/general/tables/empty/qna.yaml", "question_id": "196886616014243165011351365362811958343", "category": "taxonomy", "turns": ["Create an empty table with 3 columns and 30 rows. Please label the rows in the first column from 1 to 30 \n"], "reference": ["```\n| | | |\n| -- | | |\n| 1 | | |\n| 2 | | |\n| 3 | | |\n| 4 | | |\n| 5 | | |\n| 6 | | |\n| 7 | | |\n| 8 | | |\n| 9 | | |\n| 10 | | |\n| 11 | | |\n| 12 | | |\n| 13 | | |\n| 14 | | |\n| 15 | | |\n| 16 | | |\n| 17 | | |\n| 18 | | |\n| 19 | | |\n| 20 | | |\n| 21 | | |\n| 22 | | |\n| 23 | | |\n| 24 | | |\n| 25 | | |\n| 26 | | |\n| 27 | | |\n| 28 | | |\n| 29 | | |\n| 30 | | |\n```\n\n"]} +{"qna_file": "compositional_skills/general/tables/empty/qna.yaml", "question_id": "239691706976310879670810477668222868605", "category": "taxonomy", "turns": ["Create an empty table with 7 rows and 7 columns. Please label the rows in the first column from 1 to 7. "], "reference": ["```\n| | | | | | | |\n| - | | | | | | |\n| 1 | | | | | | |\n| 2 | | | | | | |\n| 3 | | | | | | |\n| 4 | | | | | | |\n| 5 | | | | | | |\n| 6 | | | | | | |\n| 7 | | | | | | |\n```\n\n"]} +{"qna_file": "compositional_skills/general/tables/empty/qna.yaml", "question_id": "5642324896298622635011223790098755871", "category": "taxonomy", "turns": ["Create an empty table with 30 columns and 3 rows. Please label the rows in the first column from 1 to 3 \n"], "reference": ["|\u00a0\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\n| - |\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\n| 1 |\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\n| 2 |\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\n| 3 |\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\n"]} +{"qna_file": "compositional_skills/general/tables/editing/add_remove/qna.yaml", "question_id": "137721570681090875484008252437754673924", "category": "taxonomy", "turns": ["Can you edit the following table to remove everyone whose first initial is a vowel from the following table\n\n```\n| First Initial | Last Name | Company |\n| ------------- | ---------- | ------- |\n| a | smith | xyz |\n| b | jones | xyz |\n| c | george | abc |\n| d | hicks | xyz |\n| e | long | abc |\n| f | parker | abc |\n| g | burn | xyz |\n| h | talbot | abc |\n| i | washington | abc |\n```\n\n"], "reference": ["I have removed all rows that had a vowel in the first initial\n\n```\n| First Initial | Last Name | Company |\n| ------------- | --------- | ------- |\n| b | jones | xyz |\n| c | george | abc |\n| d | hicks | xyz |\n| f | parker | abc |\n| g | burn | xyz |\n| h | talbot | abc |\n```\n\n"]} +{"qna_file": "compositional_skills/general/tables/editing/add_remove/qna.yaml", "question_id": "292571355638061499832053565543520290186", "category": "taxonomy", "turns": ["Can you please add an empty column labeled Email between last name and company?\n\n| First Initial | Last Name | Company |\n| ------------- | ---------- | ------- |\n| a | smith | xyz |\n| b | jones | xyz |\n| c | george | abc |\n| d | hicks | xyz |\n| e | long | abc |\n| f | parker | abc |\n| g | burn | xyz |\n| h | talbot | abc |\n| i | washington | abc |"], "reference": ["```\n| First Initial | Last Name | Email | Company |\n| ------------- | ---------- | ----- | ------- |\n| a | smith | | xyz |\n| b | jones | | xyz |\n| c | george | | abc |\n| d | hicks | | xyz |\n| e | long | | abc |\n| f | parker | | abc |\n| g | burn | | xyz |\n| h | talbot | | abc |\n| i | washington | | abc |\n```\n\n"]} +{"qna_file": "compositional_skills/general/tables/editing/add_remove/qna.yaml", "question_id": "287140923584038829006734185307350402552", "category": "taxonomy", "turns": ["Can you add the following records to the below table?\nR Frank from xyz\nP Parks from abc\nT Higgins from efg\nQ Riley from efg\n\n```\n| First Initial | Last Name | Company |\n| ------------- | ---------- | ------- |\n| a | smith | xyz |\n| b | jones | xyz |\n| c | george | abc |\n| d | hicks | xyz |\n| e | long | abc |\n| f | parker | abc |\n| g | burn | xyz |\n| h | talbot | abc |\n| i | washington | abc |\n```\n\n"], "reference": ["```\n| First Initial | Last Name | Company |\n| ------------- | ---------- | ------- |\n| a | smith | xyz |\n| b | jones | xyz |\n| c | george | abc |\n| d | hicks | xyz |\n| e | long | abc |\n| f | parker | abc |\n| g | burn | xyz |\n| h | talbot | abc |\n| i | washington | abc |\n| r | frank | xyz |\n| p | parks | abc |\n| t | higgins | efg |\n| q | riley | efg |\n```\n\n"]} +{"qna_file": "compositional_skills/general/tables/editing/combining_altering/qna.yaml", "question_id": "208725020637644065902544089637889259495", "category": "taxonomy", "turns": ["Can you add an email colum to the end of the table. Then populate it combining the \"First Initial\" with the \"Last Name\" and then making the email their @\"company\".com\n\n```\n| First Initial | Last Name | Company |\n| ------------- | ---------- | ------- |\n| a | smith | xyz |\n| b | jones | xyz |\n| c | george | abc |\n| d | hicks | xyz |\n| e | long | abc |\n| f | parker | abc |\n| g | burn | xyz |\n| h | talbot | abc |\n| i | washington | abc |\n```\n\n"], "reference": ["```\n| First Initial | Last Name | Company | Email |\n| ------------- | ---------- | ------- | ------------------- |\n| a | smith | xyz | asmith@xyz.com |\n| b | jones | xyz | bjones@xyz.com |\n| c | george | abc | cgeorge@abc.com |\n| d | hicks | xyz | dhicks@xyz.com |\n| e | long | abc | elong@abc.com |\n| f | parker | abc | fparker@abc.com |\n| g | burn | xyz | gburn@xyz.com |\n| h | talbot | abc | htalbot@abc.com |\n| i | washington | abc | iwashington@abc.com |\n```\n\n"]} +{"qna_file": "compositional_skills/general/tables/editing/combining_altering/qna.yaml", "question_id": "113398287520622914570960623082876417480", "category": "taxonomy", "turns": ["Can you please move the email column to the beginning of the following table?\n\n| First Initial | Last Name | Company | Email |\n| ------------- | ---------- | ------- | ------------------- |\n| a | smith | xyz | asmith@xyz.com |\n| b | jones | xyz | bjones@xyz.com |\n| c | george | abc | cgeorge@abc.com |\n| d | hicks | xyz | dhicks@xyz.com |\n| e | long | abc | elong@abc.com |\n| f | parker | abc | fparker@abc.com |\n| g | burn | xyz | gburn@xyz.com |\n| h | talbot | abc | htalbot@abc.com |\n| i | washington | abc | iwashington@abc.com |"], "reference": ["```\n| Email | First Initial | Last Name | Company |\n| ------------------- | ------------- | ---------- | ------- |\n| asmith@xyz.com | a | smith | xyz |\n| bjones@xyz.com | b | jones | xyz |\n| cgeorge@abc.com | c | george | abc |\n| dhicks@xyz.com | d | hicks | xyz |\n| elong@abc.com | e | long | abc |\n| fparker@abc.com | f | parker | abc |\n| gburn@xyz.com | g | burn | xyz |\n| htalbot@abc.com | h | talbot | abc |\n| iwashington@abc.com | i | washington | abc |\n```\n\n"]} +{"qna_file": "compositional_skills/general/tables/editing/combining_altering/qna.yaml", "question_id": "207059250253775485046927178165580494577", "category": "taxonomy", "turns": ["Can you please update the username from \"First Initial\".\"Last Namel\".\"Company\" to be \"Company\"-\"Last Name\"?\n\n```\n| First Initial | Last Name | Company | username |\n| ------------- | ---------- | ------- | ---------------- |\n| a | smith | xyz | a.smith.xyz |\n| b | jones | xyz | b.jones.xyz |\n| c | george | abc | c.george.abc |\n| d | hicks | xyz | d.hicks.xyz |\n| e | long | abc | e.long.abc |\n| f | parker | abc | f.parker.abc |\n| g | burn | xyz | g.burn.xyz |\n| h | talbot | abc | h.talbot.abc |\n| i | washington | abc | i.washington.abc |\n```\n\n"], "reference": ["```\n| First Initial | Last Name | Company | username |\n| ------------- | ---------- | ------- | -------------- |\n| a | smith | xyz | xyz-smith |\n| b | jones | xyz | xyz-jones |\n| c | george | abc | abc-george |\n| d | hicks | xyz | xyz-hicks |\n| e | long | abc | abc-long |\n| f | parker | abc | abc-parker |\n| g | burn | xyz | xyz-burn |\n| h | talbot | abc | abc-talbot |\n| i | washington | abc | abc-washington |\n```\n\n"]} +{"qna_file": "compositional_skills/STEM/science/units_conversion/temperature_conversion/qna.yaml", "question_id": "337304126539778877681267727421536727602", "category": "taxonomy", "turns": ["What is 41 degrees Fahrenheit converted to Celsius?\n"], "reference": ["The formula to convert Fahrenheit to Celsius is ((F)-32)\\*5/9. When applying this formula, you can see that (41-31)\\*5/9=5. So 41 degrees Fahrenheit is 5 degrees Celsius.\n"]} +{"qna_file": "compositional_skills/STEM/science/units_conversion/temperature_conversion/qna.yaml", "question_id": "87915633027137148319779609222423399625", "category": "taxonomy", "turns": ["What is 38 degrees Celsius converted to Fahrenheit?"], "reference": ["The formula to convert Celsius to Fahrenheit is (C\\*9/5)+32. When applying this formula, you can see that (38\\*9/5)+32=100.4. So 38 degrees Celsius is 100.4 degrees Fahrenheit.\n"]} +{"qna_file": "compositional_skills/STEM/science/units_conversion/temperature_conversion/qna.yaml", "question_id": "7286473655077226826746870490519514766", "category": "taxonomy", "turns": ["What is hotter 35 degrees Celsius or 65 degrees Fahrenheit?\n\n"], "reference": ["To compare temperatures in Celsius and Fahrenheit, you can convert one to the other scale. The formula to convert Fahrenheit to Celsius is C=(F-32)\\*5/9\nLets convert 65 degrees Fahrenheit to Celsius\nC=(65-32)\\*5/9\nC=18.33\nTherefore, 35 degrees Celsius is hotter than 65 degrees Fahrenheit, as 35 degrees Celsius is equivalent to 95 degrees Fahrenheit.\n"]} +{"qna_file": "compositional_skills/STEM/science/units_conversion/distance_conversion/qna.yaml", "question_id": "47305076959776596825918658225751656136", "category": "taxonomy", "turns": ["convert 14 km into miles?\n"], "reference": ["Hello! To convert 14 kilometers into miles, you can use the conversion factor 1 km \u2248 0.62 miles . Therefore, 14 kilometers is equivalent to approximately 8.69 miles. Let me know if you have any other questions or need assistance with something else.\n"]} +{"qna_file": "compositional_skills/STEM/science/units_conversion/distance_conversion/qna.yaml", "question_id": "116497979546558092828529943984838741320", "category": "taxonomy", "turns": ["Convert 2 KM into miles"], "reference": ["Hello! To convert 2 kilometers into miles, you can use the conversion factor 1 km \u2248 0.62 miles . Therefore, 2 kilometers is equivalent to approximately 1.24 miles. Let me know if you have any other questions or need assistance with something else.\n"]} +{"qna_file": "compositional_skills/STEM/science/units_conversion/distance_conversion/qna.yaml", "question_id": "128572342126859538078283535872720049007", "category": "taxonomy", "turns": ["Help me converting 6 miles to KM\n"], "reference": ["Hello! To convert 6 miles into kilometers, you can use the conversion factor 1 mile \u2248 1.62 kilometers. Therefore, 6 miles is equivalent to approximately 9.65 kilometers. Let me know if you have any other questions or need assistance with something else.\n"]} +{"qna_file": "compositional_skills/STEM/science/geography/qna.yaml", "question_id": "259155010096837532153920673213319362600", "category": "taxonomy", "turns": ["How many countries start with the letter V?\n"], "reference": ["Here are few countries that starts with letter \"V\"\n- Vanuatu.\n- Vatican City.\n- Venezuela.\n- Vietnam.\n"]} +{"qna_file": "compositional_skills/STEM/science/geography/qna.yaml", "question_id": "145365849075397177510681639653348364373", "category": "taxonomy", "turns": ["How many countries start with the letter J?"], "reference": ["There are 3 countries that start with the letter \"J\":\n1\\. Japan\n2\\. Jordan\n3\\. Jamaica\n"]} +{"qna_file": "compositional_skills/STEM/science/geography/qna.yaml", "question_id": "65845626335159062682373268350400449802", "category": "taxonomy", "turns": ["How many countries start with the letter W?\n"], "reference": ["Wales is the only country that starts with the letter W\n"]} +{"qna_file": "compositional_skills/STEM/math/arithmetic_reasoning/qna.yaml", "question_id": "326984083497849738224610300989191623177", "category": "taxonomy", "turns": ["Joey is 20 years younger than Becky. In two years, Becky will be twice as old as Joey. What is their age now\n"], "reference": ["Hello! I'm here to help you with this age-related problem. Let's break it down step by step:\n\n1\\. We know that Joey is 20 years younger than Becky, so Becky's age = 20 + Joey's age.\n2\\. Two years from now, Becky will be twice as old as Joey. In other words, (Becky's age + 2) = 2(Joey's age + 2).\n3\\. We can combine the first two equations to find the relationship between Becky's age and Joey's age: 20 + Joey's age + 2 = 2(Joey's age + 2)\n4\\. Solve for Joey's age: Joey's age = 18\n5\\. Now that we have the relationship between Becky's age and Joey's age, we can find their current ages by substituting the given information: Becky's age = (Joey's age + 20) = 38\n\nTherefore, Joey is currently 18 years old and Becky is currently 38 years old.\n"]} +{"qna_file": "compositional_skills/STEM/math/arithmetic_reasoning/qna.yaml", "question_id": "62205968969762978497053348605814382856", "category": "taxonomy", "turns": ["Louise is 26 years old. Her daughter Carmen is 4 years old. In how many years will Louise be double her daughter\u2019s age?"], "reference": ["Hello! I'm here to help you with this age-related problem. Let's break it down step by step:\n\n1\\. We know that Louise is 26 years old and her daughter Carmen is 4 years old.\n2\\. We want to find out in how many years Louise will be double her daughter\u2019s age. Mathematically, this can be represented as: Louise's age + Time = 2 \\* ( Carmen's age + Time)\n3 . To find out in how many years Louise will be double her daughter\u2019s age, we need to put the values of Carmen's age: 4 and Louise's age = 26 \n4\\. So we need to solve: 26 + Time = 2 \\* ( 4 + Time)\n4\\. Solving this equations gives Time = 18\n\nTherefore, it will take Louise 18 years to be double her daughter\u2019s age.\n"]} +{"qna_file": "compositional_skills/STEM/math/arithmetic_reasoning/qna.yaml", "question_id": "13265720414370068757864374300718746428", "category": "taxonomy", "turns": ["Carmen is 12 years older than David. Five years ago, the sum of their ages was 28. How old are they now?\n"], "reference": ["Hello! I'm here to help you with this age-related problem. Let's break it down step by step:\n\n1\\. We know that Carmen is 12 years older than David. So, Carmen's age = David's age + 12.\n2\\. Five years ago, the sum of their ages was 28. So, (David's age - 5) + (Carmen's age - 5) = 28.\n3\\. We can simplify the equation by combining them: (David's age - 5) + ( David's age + 12 - 5) = 28.\n4\\. Solve for David's age: David's age = (28 - 2) / 2 = 13\n5\\. Since Carmen's age is 12 years older than David, we add 12 to f David's age in order to find Carmen's age: David's age + 12 = 25\n\nTherefore, David is currently 13 years old and Carmen is currently 25 years old.\n"]} +{"qna_file": "compositional_skills/STEM/math/arithmetic_w_grammar/qna.yaml", "question_id": "130686829092960504543489287239489465667", "category": "taxonomy", "turns": ["A farmer has 17 sheep. All but 9 die. How many are left?\n"], "reference": ["There are still 9 sheep left. \"All but 9\" implies that 9 sheep remain.\n"]} +{"qna_file": "compositional_skills/STEM/math/arithmetic_w_grammar/qna.yaml", "question_id": "119682070363996541787358728705649539548", "category": "taxonomy", "turns": ["If you were running a race and you passed the person in second place, what place would you be in?"], "reference": ["If you passed the person in second place, you would be in second place.\n"]} +{"qna_file": "compositional_skills/STEM/math/arithmetic_w_grammar/qna.yaml", "question_id": "1348709414930481319561143749008337549", "category": "taxonomy", "turns": ["A baker has 24 cupcakes. She gives away all but 6. How many cupcakes does she have left?\n"], "reference": ["'all but 6' means the baker has 6 cupcakes left.\n"]} +{"qna_file": "compositional_skills/STEM/math/area/qna.yaml", "question_id": "252673827194811679027370540176416468576", "category": "taxonomy", "turns": ["what is the area of circle with radius 6 cm?\n"], "reference": ["To calculate the area of a circle given its radius, you can use the formula \u03c0r\u00b2, where r is the radius. In this case, the radius is 6 centimeters. So, the area would be \u03c0 \\* (6 cm)\u00b2 = approximately 113.1 square centimeters. Let me know if you have any other questions or need assistance with something else.\n"]} +{"qna_file": "compositional_skills/STEM/math/area/qna.yaml", "question_id": "218226030165828125442857046998169408056", "category": "taxonomy", "turns": ["what is the area of circle with radius 2 meters?"], "reference": ["To calculate the area of a circle given its radius, you can use the formula \u03c0r\u00b2, where r is the radius. In this case, the radius is 2 meters. So, the area would be \u03c0 \\* (2 m)\u00b2 = approximately 12.57 square meters. Let me know if you have any other questions or need assistance with something else.\n"]} +{"qna_file": "compositional_skills/STEM/math/area/qna.yaml", "question_id": "324906110275988358020788124265466383183", "category": "taxonomy", "turns": ["Given the area of a circle is 12.57 square centimetre find the radius of the circle?\n\n"], "reference": ["To find the radius of a circle given its area, you can use the formula \u03c0r\u00b2, where r is the radius and \u03c0 is approximately equal to 3.14159. In this case, the area is 12.57 square centimeters. So, you can solve for r by taking the square root of the area: r\u00b2 = (12.57 cm\u00b2)/3.14 \u2248 4.00 cm. then r=\u221a(4.00 cm\u00b2)=2 cm . So, radius of the circle is 2 cm. Let me know if you have any other questions or need assistance with something else.\n\n"]} +{"qna_file": "compositional_skills/STEM/math/mensurational/qna.yaml", "question_id": "16578662502275970942313744680967631626", "category": "taxonomy", "turns": ["A circle has a radius of 21 cm. Find its circumference and area. (Use \u03c0 = 22/7)\n"], "reference": ["Solution: We know,\nCircumference of circle = 2\u03c0r = 2 x (22/7) x 21 = 2 x 22 x 3 = 132 cm\nArea of circle = \u03c0r2\u00a0= (22/7) x 212\u00a0= 22/7 x 21 x 21 = 22 x 3 x 21\nArea of circle with radius, 21cm = 1386 cm2\n"]} +{"qna_file": "compositional_skills/STEM/math/mensurational/qna.yaml", "question_id": "217560678045500153962142062248171742891", "category": "taxonomy", "turns": ["Suppose a quadrilateral having a diagonal of length 10 cm, which divides the quadrilateral into two triangles and the heights of triangles with diagonals as the base, are 4 cm and 6 cm. Find the area of the quadrilateral."], "reference": ["Solution: Given,\nDiagonal, d = 10 cm\nHeight of one triangle, h1\u00a0= 4cm\nHeight of another triangle, h2\u00a0= 6cm\nArea of quadrilateral = \u00bd d(h1+h2) = \u00bd x 10 x (4+6) = 5 x 10 = 50 sq.cm.\n"]} +{"qna_file": "compositional_skills/STEM/math/mensurational/qna.yaml", "question_id": "71485276075909563209233021300541560079", "category": "taxonomy", "turns": ["The area of a trapezium shaped field is 480 m2, the distance between two parallel sides is 15 m and one of the parallel sides is 20 m. Find the other parallel side.\n"], "reference": ["Solution: One of the parallel sides of the trapezium is a = 20 m, let another parallel side be b, height h = 15 m.\nThe given area of trapezium = 480 m2\nWe know, by formula;\nArea of a trapezium = \u00bd h (a+b)\n480 = \u00bd (15) (20+b)\n20 + b = (480\u00d72)/15\nb = 64 \u2013 20 = 44 m\n"]} +{"qna_file": "compositional_skills/STEM/math/pattern_recognition/qna.yaml", "question_id": "115306318572066657834336150710640355214", "category": "taxonomy", "turns": ["Consider the following sequence of numbers: 2, 4, 8, 16, 32. What is the next number in the sequence, and explain the pattern.\n"], "reference": ["The correct answer is 64. The pattern is that each number is the previous number multiplied by 2.\n"]} +{"qna_file": "compositional_skills/STEM/math/pattern_recognition/qna.yaml", "question_id": "200239197920343691982445267305225673463", "category": "taxonomy", "turns": ["Follow the pattern: \n\nA, C, E, G, ... What is the next letter?"], "reference": ["The correct answer is I, as the sequence consists of every second letter in the English alphabet, starting with A (A, C, E, G, I...).\n"]} +{"qna_file": "compositional_skills/STEM/math/pattern_recognition/qna.yaml", "question_id": "106055944101765991187927709200874474011", "category": "taxonomy", "turns": ["Consider this sequence: 3, 9, 27, 81, ... What is the next number?\n"], "reference": ["The correct answer is 243. The pattern involves each number being multiplied by 3 to find the next number (3x3=9, 9x3=27, 27x3=81, 81x3=243).\n"]} +{"qna_file": "compositional_skills/STEM/math/reasoning/qna.yaml", "question_id": "194197388041822203175168971123547188319", "category": "taxonomy", "turns": ["How many numbers are in the list 25, 26, ..., 151?\n"], "reference": ["In order to find the answer you can subtract the first number from the last from the last number of the last, and then add one back in (because you start counting at zero, not one) to get the final answer. So 151 - 25 + = 127.\n"]} +{"qna_file": "compositional_skills/STEM/math/reasoning/qna.yaml", "question_id": "212672488513801611223268398644988426458", "category": "taxonomy", "turns": ["If 4 daps = 7 yaps, and 5 yaps = 3 baps,\nhow many daps equal 42 baps?"], "reference": ["If 4 daps = 7 yaps, and 5 yaps = 3 baps then 42 baps \\* 5/3 yaps/baps \\* 4/7 daps/yaps = 40 daps.\n"]} +{"qna_file": "compositional_skills/STEM/math/reasoning/qna.yaml", "question_id": "318088185628758556800253822235084549673", "category": "taxonomy", "turns": ["When you drop a ball from rest it accelerates downward at 9.8 m/s\u00b2. If you instead throw it\ndownward assuming no air resistance its acceleration immediately after leaving your hand is\n(A) 9.8 m/s\u00b2\n(B) more than 9.8 m/s\u00b2\n(C) less than 9.8 m/s\u00b2\n(D) Cannot say unless the speed of throw is given.\n"], "reference": ["(A) 9.8 m/s\u00b2 -- This is because even though you threw the ball the second time, it will leave your hand with a higher initial velocity, but it will still only accelerate at the same constant rate due to gravitational pull until terminal velocity is reached.\n"]} +{"qna_file": "compositional_skills/STEM/math/time_series/qna.yaml", "question_id": "174359785553990955380227697366943479615", "category": "taxonomy", "turns": ["Question:\nYou are analyzing sales data for a retail store over the past year. The sales data is recorded monthly, and you want to forecast sales for the next three months using a simple moving average (SMA) model. The sales figures for the past 12 months are as follows:\nMonth: January, February, March, April, May, June, July, August, September, October, November, December\nSales: 1000, 1100, 1050, 1200, 1250, 1300, 1400, 1500, 1600, 1550, 1650, 1700 (in USD)\nCalculate the three-month moving average for sales and use it to forecast sales for the next three months.\n"], "reference": ["To calculate the three-month moving average (SMA), we need to take the average of sales figures for the past three months and then use it to forecast sales for the next month. Here's how to calculate the SMA:\n- January: Not enough data for SMA calculation\n- February: Not enough data for SMA calculation\n- March: (1000 + 1100 + 1050) / 3 = 1050\n- April: (1100 + 1050 + 1200) / 3 = 1116.67 (rounded to 2 decimal places)\n- May: (1050 + 1200 + 1250) / 3 = 1166.67 (rounded to 2 decimal places)\nTherefore, the three-month moving average sales forecast for the next three months would be:\n- June: $1116.67\n- July: $1166.67\n- August: $1216.67\n\n"]} +{"qna_file": "compositional_skills/STEM/math/time_series/qna.yaml", "question_id": "157332575045645445929074554438558241658", "category": "taxonomy", "turns": ["You are tasked with forecasting sales for the next six months using a weighted moving average (WMA) model. You decide to assign weights of 0.5, 0.3, and 0.2 to the sales figures from the three most recent months, respectively. Using the same sales data calculate the six-month weighted moving average for sales and use it to forecast sales for the next six months.\n\nMonth: January, February, March, April, May, June, July, August, September, October, November, December\n\nSales: 1000, 1100, 1050, 1200, 1250, 1300, 1400, 1500, 1600, 1550, 1650, 1700 (in USD)"], "reference": ["To calculate the six-month weighted moving average (WMA), we need to multiply each sales figure by its corresponding weight, sum up the weighted values, and divide by the sum of the weights. Here's how to calculate the WMA:\n- January: Not enough data for WMA calculation\n- February: Not enough data for WMA calculation\n- March: Not enough data for WMA calculation\n- April: (1200 \\* 0.5 + 1050 \\* 0.3 + 1100 \\* 0.2) / (0.5 + 0.3 + 0.2) = 1165\n- May: (1250 \\* 0.5 + 1200 \\* 0.3 + 1050 \\* 0.2) / (0.5 + 0.3 + 0.2) = 1205\n- June: (1300 \\* 0.5 + 1250 \\* 0.3 + 1200 \\* 0.2) / (0.5 + 0.3 + 0.2) = 1255\nTherefore, the six-month weighted moving average sales forecast for the next six months would be:\n- July: $1215\n- August: $1265\n- September: $1315\n- October: $1365\n- November: $1415\n- December: $1465\n\n"]} +{"qna_file": "compositional_skills/STEM/math/time_series/qna.yaml", "question_id": "93057076691178316614332560603505840551", "category": "taxonomy", "turns": ["\nYou are tasked with forecasting sales for the next three months using an exponential smoothing (ES) model with a smoothing factor (\u03b1) of 0.3. Using the same sales data provided earlier, calculate the exponential smoothing forecast for the next three months.\n\n\nMonth: January, February, March, April, May, June, July, August, September, October, November, December\n\nSales: 1000, 1100, 1050, 1200, 1250, 1300, 1400, 1500, 1600, 1550, 1650, 1700 (in USD)\n"], "reference": ["To calculate the exponential smoothing forecast, we start with the initial forecast for the first month, which is equal to the actual sales figure for the first month. Then, for each subsequent month, we update the forecast using the smoothing factor (\u03b1) and the difference between the actual sales figure and the previous forecast. Here's how to calculate the exponential smoothing forecast:\n- Initial forecast for January = Actual sales for January = $1000\n- Forecast for February:\n- Forecast for February = (\u03b1 \\* Actual sales for February) + ((1 - \u03b1) \\* Forecast for January)\n- = (0.3 \\* $1100) + (0.7 \\* $1000)\n- = $330 + $700\n- = $1030\n- Forecast for March:\n- Forecast for March = (\u03b1 \\* Actual sales for March) + ((1 - \u03b1) \\* Forecast for February)\n- = (0.3 \\* $1050) + (0.7 \\* $1030)\n- = $315 + $721\n- = $1036\nTherefore, the exponential smoothing forecast for sales for the next three months would be:\n- February: $1030\n- March: $1036\n- April: $1040.8 (rounded to 1 decimal place)\n\n"]} +{"qna_file": "compositional_skills/STEM/math/distance_conversion/qna.yaml", "question_id": "149401334586578214913918606614490198142", "category": "taxonomy", "turns": ["Convert 1000 square feet to square meters\n"], "reference": ["To convert square feet to square meters, we can use the conversion factor of 1 square foot \u2248 0.0929 square meters. Given a value of 1000 square feet, we can calculate the equivalent area in square meters as follows:\n\n1000 square feet \u00d7 0.0929 square meters/square foot = 92.9 square meters\n\nTherefore, 1000 square feet is approximately equal to 92.9 square meters.\n"]} +{"qna_file": "compositional_skills/STEM/math/distance_conversion/qna.yaml", "question_id": "332435408304513045464933143755971696994", "category": "taxonomy", "turns": ["Convert 390 square meters to square feet"], "reference": ["To convert square meters to square feet, we can use the conversion factor of 1 square meter \u2248 10.764 square feet. Given a value of 390 square meters, we can calculate the equivalent area in square feet as follows:\n\n390 square meters \u00d7 10.764 square feet/square meter = 4197.93 square feet\n\nTherefore, 390 square meters is approximately equal to 4197.93 square feet.\n\n"]} +{"qna_file": "compositional_skills/STEM/math/distance_conversion/qna.yaml", "question_id": "256422175651861610409489168209529815135", "category": "taxonomy", "turns": ["What is larger, 1000 square feet or 90 square meters?\n"], "reference": ["To compare the sizes of 1000 square feet and 90 square meters, we can convert both values to the same unit (square meters) using the provided conversion factors:\n\n1000 square feet \u00d7 0.0929 square meters/square foot \u2248 92.9 square meters\n90 square meters \u00d7 10.764square meter/square foot \u2248 968.752 square feet\n\nComparing the two results, we find that 1000 square feet is larger than 90 square meters.\n"]} +{"qna_file": "compositional_skills/STEM/math/distance_conversion/qna.yaml", "question_id": "190848592641402308122310768907541229262", "category": "taxonomy", "turns": ["How many feet are in 500 miles?\n"], "reference": ["To convert 500 miles to feet, you can use the conversion factor 1 mile = 5,280 feet. Therefore, to find the equivalent length in feet, multiply the number of miles by the conversion factor: 500 miles \u00d7 5,280 feet/mile = 2,640,000 feet. I hope this information is helpful! Let me know if you have any other questions.\n"]} +{"qna_file": "compositional_skills/STEM/math/distance_conversion/qna.yaml", "question_id": "295434104124528771206372877081304336750", "category": "taxonomy", "turns": ["How many feet are in 2 miles?"], "reference": ["To convert 2 miles to feet, you can use the conversion factor 1 mile = 5,280 feet. Therefore, to find the equivalent length in feet, multiply the number of miles by the conversion factor: 2 miles \u00d7 5,280 feet/mile = 10,560\n"]} +{"qna_file": "compositional_skills/STEM/math/distance_conversion/qna.yaml", "question_id": "234310271218979560355344740039067161206", "category": "taxonomy", "turns": ["How many feet are in 1000 miles?\n\n"], "reference": ["To convert 1000 miles to feet, you can use the conversion factor 1 mile = 5,280 feet. Therefore, to find the equivalent length in feet, multiply the number of miles by the conversion factor: 1000 miles \u00d7 5,280 feet/mile = 5,280,000 feet. I hope this information is helpful! Let me know if you have any other questions.\n"]} +{"qna_file": "compositional_skills/linguistics/classification/agent_classification/qna.yaml", "question_id": "148667943096617929777199110574640195820", "category": "taxonomy", "turns": ["Classify the following statement into one from the list of available agents. \nYou should return the name only of one of the classes below to answer the question posed to you.\n\nAvailable agents: A list of randomly arranged available agents with definitions to describe the purpose of each.\n\nFlowRepairAgent: This is an agent that enables you to modify a flow\nFlowGeneratorAgent: This is an agent that enables you to create a flow.\n\nYour response should only include the answer. Do not provide any further explanation.\n\nHere are some examples, complete the last one:\n\nInput: send an email with the list of finance customers\nOutput: FlowGeneratorAgent\n\nInput: email the list of job candidates. \nOutput: FlowGeneratorAgent\n\nInput: Add a trello card\nOutput: FlowRepairAgent\n\nInput: Remove slack message from the sequence\nOutput: FlowRepairAgent\n\nInput: add a notification via slack\nOutput:\n"], "reference": ["FlowRepairAgent\n"]} +{"qna_file": "compositional_skills/linguistics/classification/agent_classification/qna.yaml", "question_id": "2399448008627548149537700408182910775", "category": "taxonomy", "turns": ["Classify the following statement into one from the list of available agents. \nYou should return the name only of one of the classes below to answer the question posed to you.\n\nAvailable agents: A list of randomly arranged available agents with definitions to describe the purpose of each.\n\nFlowRepairAgent: This is an agent that enables you to modify a flow\nFlowGeneratorAgent: This is an agent that enables you to create a flow.\n\nYour response should only include the answer. Do not provide any further explanation.\n\nHere are some examples, complete the last one:\n\nInput: create a list of leads for a selected company\nOutput: FlowGeneratorAgent\n\nInput: use salesforce instead of salesoft to retrieve leads\nOutput: FlowRepairAgent\n\nInput: Add the phone numbers to each lead identified\nOutput: FlowRepairAgent\n\nInput: Add email address if phone number not available\nOutput: FlowRepairAgent\n\nInput: Create a list of leads and retrieve titles from linkedIn\nOutput:"], "reference": ["FlowGeneratorAgent\n"]} +{"qna_file": "compositional_skills/linguistics/classification/agent_classification/qna.yaml", "question_id": "108703527040885597689279459999750686427", "category": "taxonomy", "turns": ["Classify the following statement into one from the list of available agents. \nYou should return the name only of one of the classes below to answer the question posed to you.\n\nAvailable agents: A list of randomly arranged available agents with definitions to describe the purpose of each.\n\nFlowRepairAgent: This is an agent that enables you to modify a flow\nFlowGeneratorAgent: This is an agent that enables you to create a flow.\n\nYour response should only include the answer. Do not provide any further explanation.\n\nHere are some examples, complete the last one:\n\nInput: Send an email when a new event is created in EventBrite\nOutput: FlowGeneratorAgent\n\nInput: use gmail for the email application\nOutput: FlowRepairAgent\n\nInput: Notify me when new guests are registered for an event\nOutput: FlowGeneratorAgent\n\nInput: Add VIP status to repeat guests\nOutput: FlowRepairAgent\n\nInput: Ensure email or phone number are provided when registering a user\nOutput:\n"], "reference": ["FlowRepairAgent\n"]} +{"qna_file": "compositional_skills/linguistics/word_gen/qna.yaml", "question_id": "186093931508315906762359852203650858695", "category": "taxonomy", "turns": ["Give me 10 words starting with a\n\n"], "reference": ["Here are 10 words that start with the letter \"a\":\n1. Apple\n2. Adventure\n3. Amazing\n4. Antarctica\n5. Adorable\n6. Accelerate\n7. Artist\n8. Attitude\n9. Assertive\n10. Aquarium\n\n"]} +{"qna_file": "compositional_skills/linguistics/word_gen/qna.yaml", "question_id": "232652504704269465313999616244661111855", "category": "taxonomy", "turns": ["Give me 10 words that contain 3 letters\n"], "reference": ["Here are 10 words that meet your criteria:\n1. Cat\n2. Dog\n3. Sun\n4. Car\n5. Cup\n6. Run\n7. Sky\n8. Joy\n9. Box\n10. Ant\n"]} +{"qna_file": "compositional_skills/linguistics/word_gen/qna.yaml", "question_id": "287188625903951072935762765471475284175", "category": "taxonomy", "turns": ["Rearrange these letters to form a word \"EBIK\"\n"], "reference": ["Sure, I can help you with that! The letters \"EBIK\" can be rearranged to form the word \"BIKE.\"\n"]} +{"qna_file": "compositional_skills/linguistics/complete_common_expressions/qna.yaml", "question_id": "161480748540610701858922404623360627223", "category": "taxonomy", "turns": ["I will provide you with two sentences, separated by a comma. You must fill in the blank in the second sentence, which is represented by three underscores (\\_\\_\\_). Confine your answer to only filling in the blank, do not explain or provide any additional information. The word you use to fill in the blank must rhyme: See you later alligator, in a while \\_\\_\\_\n"], "reference": ["crocodile\n"]} +{"qna_file": "compositional_skills/linguistics/complete_common_expressions/qna.yaml", "question_id": "85459626468998238283612722574881223450", "category": "taxonomy", "turns": ["Many expressions in English rhyme so they are easy to remember. Complete the following common expression by providing the missing word where it says \"\". Your answer must be exactly one word, no more: Cheater cheater, pumpkin "], "reference": ["eater\n"]} +{"qna_file": "compositional_skills/linguistics/complete_common_expressions/qna.yaml", "question_id": "191706525351403953102449248945001390595", "category": "taxonomy", "turns": ["I will provide you a common expression with the last word missing. The missing word is represented by three underscores (\\_\\_\\_), and must rhyme with the rest of the expression. Your answer must be a single word: Good night, sleep \\_\\_\\_\n"], "reference": ["tight\n"]} +{"qna_file": "compositional_skills/linguistics/summarization/list_of_sentences/qna.yaml", "question_id": "217629250243434759965340785181586787288", "category": "taxonomy", "turns": ["Summarize the following list of symptoms into one or two English sentences. Do not generate any suggestion, recommendation or new symptoms.\n\nInput:\n System load too high\nMachine is more than 50% of its time at a load higher than 8 \nThe virtual machine is undersized or the hypervisor host is overloaded. Consider allocating more than 4 CPU for this machine.\n\nOutput:\n"], "reference": ["The system is experiencing significant high system load and a virtual machine is undersized or the hypervisor host is overloaded, indicating potential performance issues.\n"]} +{"qna_file": "compositional_skills/linguistics/summarization/list_of_sentences/qna.yaml", "question_id": "78993804116905138978836976886411652996", "category": "taxonomy", "turns": ["Summarize the following list of symptoms into one or two English sentences. Do not generate any suggestion, recommendation or new symptoms.\n\nInput:\nThis can be a sign of a problem on this service or its downstream\nRelative change in value: 1289%\n Confidence: 95.00%\nThis can be a sign of a problem on one side of the connection\nAbsolute change: 85%\n Confidence: 95.00%\nErroneous call rate stays at a high level for an extended period of time \n Erroneous call rate is too high\n Sudden increase in average latency\n\nOutput"], "reference": ["Sudden increase in average latency and erroneous call rate staying at a high level. This is probably due to a problem in service or its downstream or on one side of the connection.\n"]} +{"qna_file": "compositional_skills/linguistics/summarization/list_of_sentences/qna.yaml", "question_id": "21187129699708595881971315723980585273", "category": "taxonomy", "turns": ["Summarize the following list of symptoms into one or two English sentences. Do not generate any suggestion, recommendation or new symptoms.\n\nInput:\nThe value memory.total has changed from 0 to 67509846016.\nMachine is more than 50% of its time at a load higher than 32\u00a0\n\u00a0System load too high\nThe virtual machine is undersized or the hypervisor host is overloaded. Consider allocating more than 16 CPU for this machine.\n"], "reference": ["System load is too high and virtual machine may be undersized or the hypervisor host is overloaded. The value memory.total has also changed.\n"]} +{"qna_file": "compositional_skills/linguistics/summarization/ignore_pii/qna.yaml", "question_id": "222070321832259472224056798882200861588", "category": "taxonomy", "turns": ["You are a helpful summarizer who is an expert in your field of writing IT incident status updates for internal business owners.\n\nThe status update:\n\\- Should not include names\n\\- Should not contain hashtags like #IncidentResponse\n\\- Should be limited to 250 characters\n\\- Should not include non-business discussions\n\\- Should not include humor or jokes\n\nWrite a one paragraph status update that summarized the issue and the technical work described in the chat.\n\nInclude only information relative to these key points.\u00a0\n\n[Document]\n2022-05-10 09:41:20:\nEve: Thank you, Alice. Let me know if you need any additional assistance.\n\n2022-05-10 09:42:55:\nAlice: Will do, Eve. Thanks for joining the team.\n\n2022-05-10 09:45:00:\nBob: Optimized the query, and the service seems to be stabilizing now. Connection issues are becoming less frequent.\n\n2022-05-10 09:47:15:\nCarol: Great job, Bob! Let's keep an eye on it for a while before confirming complete resolution.\n\n2022-05-10 09:49:30:\nBob: Agreed, Carol. I'll continue monitoring the situation.\u00a0\n[End]\n\nSummary:\n"], "reference": ["After optimizing a query, the team successfully resolved connection issues with the service, reducing their frequency. The system has stabilized, and further monitoring will confirm its full recovery.\n"]} +{"qna_file": "compositional_skills/linguistics/summarization/ignore_pii/qna.yaml", "question_id": "71867518780042338901315556259843449355", "category": "taxonomy", "turns": ["You are a helpful summarizer who is an expert in your field of writing IT incident status updates for internal business owners.\n\nThe status update:\n- Should not include names\n- Should not contain hashtags like #IncidentResponse\n- Should be limited to 250 characters\n- Should not include non-business discussions\n- Should not include humor or jokes\n\nWrite a one paragraph status update that summarized the issue and the technical work described in the chat.\n\nInclude only information relative to these key points. \n\n[Document]\n2022-07-25 09:41:25:\n Eve: Aw, that's wonderful, Carol! What's his name?\n\n2022-07-25 09:43:10:\n Carol: His name is Max. He's a fluffy golden retriever and brings so much joy to my life. I even taught him a few tricks!\n\n2022-07-25 09:45:35:\n Bob: That's fantastic, Carol! Dogs are the best. Now, back to our investigation, has anyone checked the application code for any recent changes or issues?\n\n2022-07-25 09:47:20:\n David: Good point, Bob. I'll review the recent commits and see if there's anything that could potentially cause these problems. \n[End]\n\nSummary:"], "reference": ["The team is checking application code changes to identify any recent modifications that could be causing the reported problems. \n"]} +{"qna_file": "compositional_skills/linguistics/summarization/ignore_pii/qna.yaml", "question_id": "186626683352999270084004348076199855768", "category": "taxonomy", "turns": ["You are a helpful summarizer who is an expert in your field of writing IT incident status updates for internal business owners.\n\nThe status update:\n\\- Should not include names\n\\- Should not contain hashtags like #IncidentResponse\n\\- Should be limited to 250 characters\n\\- Should not include non-business discussions\n\\- Should not include humor or jokes\n\nWrite a one paragraph status update that summarized the issue and the technical work described in the chat.\n\nInclude only information relative to these key points.\u00a0\n\n[Document]\n2022-10-12 09:31:30:\nBob: No worries, Charlie. We're all in this together. Let's continue our investigation and work towards resolving this issue for our customers.\n\n2022-10-12 09:34:00:\nAlice: While we wait for Charlie's analysis, anyone up for a quick distraction? My dog just learned a new trick, and I can't wait to share the video with you all.\n\n2022-10-12 09:36:15:\nBob: Sure, Alice! We could all use a little breather. Dogs are the best stress-busters. Looking forward to seeing the video of your talented furry friend.\n\n2022-10-12 09:38:30:\nCharlie: Haha, I could use some cute dog content right now. Thanks for lightening the mood, Alice.\u00a0\n\nSummary:\n"], "reference": ["We are investigating an issue impacting our services and are awaiting further analysis.\n"]} +{"qna_file": "compositional_skills/linguistics/organize_lists/qna.yaml", "question_id": "77661143051378350859380096639114948420", "category": "taxonomy", "turns": ["can you organize this list of animals from biggest to smallest and tell me which one is the middle largest?\n\n1\\. cat\n2\\. dog\n3\\. flea\n4\\. lion\n5\\. bear\n"], "reference": ["Sure, I'd be happy to help! Here is the list of animals from biggest to smallest, along with the middle-sized animal:\n\n1\\. Bear: Brown bears, such as the grizzly and black bears, can weigh between 300-800 pounds (135-363 kg) and stand up to 6 feet (1.83 meters) tall at the shoulder.\n2\\. Lion: Male lions can weigh between 250-450 pounds (113-204 kg) and stand up to 4.5 feet (1.37 meters) tall at the shoulder.\n3\\. Dog: Small breed dogs, such as the Chihuahua and Maltese, can weigh between 2-10 pounds (0.9-4.5 kg) and stand up to 13 inches (33 cm) tall at the shoulder. Others can be larger and average 80 - 110lbs.\n4\\. Cat: Domestic cats, such as the domestic shorthair and Siamese, can weigh between 8-10 pounds (3.6-4.5 kg) and stand up to 3 feet (0.91 meters) tall at the shoulder.\n5\\. Flea: Adult fleas can weigh between 0.02-0.05 grams (0.0007-0.0018 ounces) and are about 0.12 inches (3 mm) long.\n\nThe middle-sized animal in this list is the dog.\n"]} +{"qna_file": "compositional_skills/linguistics/organize_lists/qna.yaml", "question_id": "189888846104744865782174795075410762144", "category": "taxonomy", "turns": ["can you organize this list of animals from biggest to smallest and tell me which one is the middle largest?\n\n1. cat\n2. bird\n3. ladybug\n4. scorpion\n5. giraffe"], "reference": ["Sure, I'd be happy to help! Here is the list of animals from biggest to smallest, along with the middle-sized animal:\n\n1\\. Giraffe: Adult giraffes can weigh between 2,000-4,000 pounds (914-1,814 kg) and stand up to 16 feet (4.87 meters) tall at the shoulder.\n2\\. Bird: The largest bird species, such as the ostrich and emu, can weigh between 140-320 pounds (63-145 kg) and stand up to 5 feet (1.52 meters) tall at the shoulder.\n3\\. Cat: Domestic cats, such as the domestic shorthair and Siamese, can weigh between 8-10 pounds (3.6-4.5 kg) and stand up to 3 feet (0.91 meters) tall at the shoulder.\n4\\. Scorpion: Adult scorpions can weigh between 0.2-2 grams (0.007-0.07 ounces) and are about 1-6 inches (2.5-15 cm) long.\n5\\. Ladybug: Adult ladybugs can weigh between 0.02-0.05 grams (0.0007-0.0018 ounces) and are about 0.16 inches (4 mm) long.\n\nThe middle-sized animal in this list is the cat.\n"]} +{"qna_file": "compositional_skills/linguistics/organize_lists/qna.yaml", "question_id": "121995513509776861884758138059280808118", "category": "taxonomy", "turns": ["can you organize this list of organisms from biggest to smallest and tell me which one is the middle largest?\n\n1\\. human\n2\\. zebra\n3\\. tiger\n4\\. python\n5\\. wombat\n"], "reference": ["Sure, I'd be happy to help! Here is the list of organisms from biggest to smallest, along with the middle-sized organism:\n\n1\\. Python: The longest snake species, such as the green anaconda and reticulated python, can grow up to 30 feet (9.14 meters) long.\n2\\. Zebra: Male zebras can weigh between 450-550 pounds (204-249 kg) and stand up to 5 feet 6 inches (168 cm) tall at the shoulder.\n3\\. Tiger: Tigers can weigh between 200-400 pounds (90-181 kg) and stand up to 4 feet 11 inches (150 cm) tall at the shoulder.\n4\\. Human: The average height of a human male is 5 feet 9 inches (175 cm), and the average height of a human female is 5 feet 3 inches (160 cm).\n5\\. Wombat: Wombats can weigh between 44-77 pounds (20-36 kg) and stand up to 3 feet 6 inches (107 cm) tall at the shoulder.\n\nThe middle-sized organism in this list is the Tiger, if you are going by length/height. It would be the human if going by weight.\n"]} +{"qna_file": "compositional_skills/linguistics/reversing_string/qna.yaml", "question_id": "219992809188512294879123514028614062460", "category": "taxonomy", "turns": ["reverse the letters in the word velocity\n"], "reference": ["The reversed word is \"yticolev\".\n"]} +{"qna_file": "compositional_skills/linguistics/reversing_string/qna.yaml", "question_id": "208770372963369082959489876134980312158", "category": "taxonomy", "turns": ["reverse the digits in the number 6735725342\n"], "reference": ["The reversed number is 2435275376.\n"]} +{"qna_file": "compositional_skills/linguistics/reversing_string/qna.yaml", "question_id": "35267098264465704953047728228342486975", "category": "taxonomy", "turns": ["reverse the digits in the number 6735725rit342\n\n"], "reference": ["The reversed string is 243tir5275376.\n"]} +{"qna_file": "compositional_skills/linguistics/pattern_recognition/qna.yaml", "question_id": "22189132676687610178241262070613641106", "category": "taxonomy", "turns": ["Fill in the missing letters to complete the word pattern: B \\_ N \\_ N \\_ . and create a meaningful English word\n"], "reference": ["BANANA\n"]} +{"qna_file": "compositional_skills/linguistics/pattern_recognition/qna.yaml", "question_id": "313812484855916859299370830713953082228", "category": "taxonomy", "turns": ["Complete the word pattern: _ A T _ R to create an English word"], "reference": ["LATER\n"]} +{"qna_file": "compositional_skills/linguistics/pattern_recognition/qna.yaml", "question_id": "4430096481961294414076987706300511107", "category": "taxonomy", "turns": ["Prompt: Complete the word pattern: \\_ \\_ \\_ \\_ \\_ \\_ \\_E.The missing letters are G, A, N, L, U, A,G.\n"], "reference": ["LANGUAGE\n"]} +{"qna_file": "compositional_skills/linguistics/jumbled_sentences/qna.yaml", "question_id": "101615453773179268841614165828269868329", "category": "taxonomy", "turns": ["Rearrange the following words/phrases to form meaningful sentences\nExercise\n1\\. soccer / He plays / his friends / with\n2\\. for us / cooks / dinner / She\n3\\. shoes / I wear / comfortable\n4\\. often go / We / library / to the\n5\\. sandwiches / for breakfast / eat / They\n"], "reference": ["Answer\n1.He plays soccer with his friends.\n2\\. She cooks dinner for us.\n3\\. I wear comfortable shoes.\n4\\. We often go to the library.\n5\\. They eat sandwiches for breakfast.\n"]} +{"qna_file": "compositional_skills/linguistics/jumbled_sentences/qna.yaml", "question_id": "54440709378339472233852915336694005596", "category": "taxonomy", "turns": ["Rearrange the following words/phrases to form meaningful sentences\n\nExercise\n\n1. the garbage / dispose of / properly / Please\n2. clappled happily / entered / as santa claus / The children\n3. had traveled / a long way / was tired / and / The old man\n4. has given / us / Our cow / milk today\n5. come from / Where / did / all these letters"], "reference": ["Please dispose of the garbage properly.\nThe children clapped happily as Santa Claus entered.\nThe old man had traveled a long way and was tired.\nOur cow has given us milk today.\nWhere did all these letters come from?\n"]} +{"qna_file": "compositional_skills/linguistics/jumbled_sentences/qna.yaml", "question_id": "197163736219188071863113578585340858328", "category": "taxonomy", "turns": ["Rearrange the following words/phrases to form meaningful sentences\n\nExercise\n1\\. cleaning / The toilet / a thorough / needed\n2\\. inspired / to succeed / The teacher / her students\n3\\. is exciting / Learning / every day / new things\n4\\. in the class / Seema / child / is the youngest\n5\\. can buy / almost anything / Rich people / they want\n"], "reference": ["Answers\nThe toilet needed a thorough cleaning.\nThe teacher inspired her students to succeed.\nLearning new things every day is exciting.\nSeema is the youngest child in the class.\nRich people can buy almost anything they want.\n\n"]} +{"qna_file": "compositional_skills/linguistics/rhyming_words/qna.yaml", "question_id": "54312911349778793486855842470338606300", "category": "taxonomy", "turns": ["Give few rhyming words for cool.\n"], "reference": ["Here are a few rhyming words for \"cool\":\n1\\. Pool\n2\\. Fool\n3\\. Tool\n4\\. Rule\n5\\. Mule\n"]} +{"qna_file": "compositional_skills/linguistics/rhyming_words/qna.yaml", "question_id": "287580053780674726253607674886288065838", "category": "taxonomy", "turns": ["give one rhyming word for pan"], "reference": ["Sure, I can help with that! Here's one rhyming word for \"pan\":\n1\\. Man\n\n"]} +{"qna_file": "compositional_skills/linguistics/rhyming_words/qna.yaml", "question_id": "156722688807824739867629660728739163344", "category": "taxonomy", "turns": ["Give three rhyming words for meet\n"], "reference": ["Here are three rhyming words for \"meet\":\n\n1\\. Street\n2\\. Neat\n3\\. Beat\n\n\n"]} +{"qna_file": "compositional_skills/extraction/annual_report/bullet_points/qna.yaml", "question_id": "276549514571388309132827245495658651139", "category": "taxonomy", "turns": ["Given the context below:\n## 8 Management Discussion\n\n## MANAGEMENT DISCUSSION SNAPSHOT\n\n($ and shares in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022 *\u00a0\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent/Margin Change\u00a0\u00a0|\n|----------------------------------------------------------------------|--------------|---------------|------------------------------------|\n| Revenue\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $6 0 , 5 3 0 | $5 7 , 3 5 0\u00a0| 5.5 %**\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Gross profit margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 54.0 %\u00a0\u00a0\u00a0\u00a0| 54.9 %\u00a0\u00a0\u00a0\u00a0| (0.9)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Total expense and other (income)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $3 1 , 5 3 1 | $2 6 , 4 9\u00a0\u00a0| 18.3 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations before income taxes\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 1 5 6\u00a0\u00a0| $4 , 8 3 7\u00a0\u00a0| (76.1)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Provision for/(benefit from) income taxes from continuing operations | $ (626)\u00a0\u00a0\u00a0| $ 124\u00a0\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 7 8 3\u00a0\u00a0| $4 , 7 1 2\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2.9 %\u00a0\u00a0\u00a0\u00a0| 8.2 %\u00a0\u00a0\u00a0\u00a0\u00a0| (5.3)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ (143) \uf020\u00a0\u00a0| $1 , 0 3\u00a0\u00a0\uf020 | NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Net income\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 6 3 9\u00a0\u00a0| $5 , 7 4 3\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Earnings per share from continuing operations-assuming dilution\u00a0\u00a0\u00a0| $ 1.95\u00a0\u00a0\u00a0\u00a0| $ 5.21\u00a0\u00a0\u00a0\u00a0| (62.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Consolidated earnings per share-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 1.80\u00a0\u00a0\u00a0\u00a0| $ 6.35\u00a0\u00a0\u00a0\u00a0| (71.7)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Weighted-average shares outstanding-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 912.3\u00a0\u00a0\u00a0\u00a0| 904.6\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Assets \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $127,243\u00a0\u00a0\u00a0| $132,001\u00a0\u00a0\u00a0| (3.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Liabilities \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $105,222\u00a0\u00a0\u00a0| $113,005\u00a0\u00a0\u00a0| (6.9)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Equity \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $2 2 , 0 2 1 | $1 8 , 9 6\u00a0\u00a0| 15.9 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\n\uf0c8\uf020 At December 31.\n\nNM-Not meaningful\n\nThe following table provides the company's operating (non-GAAP) earnings for 2022 and 2021. See page 29 for additional information.\n\n($ in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent Change\u00a0\u00a0|\n|--------------------------------------------------------|----------|----------|-----------------------------|\n| Net income as reported\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,639 * | $5,743\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax | (143)\u00a0\u00a0| 1,030\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,783 * | $4,712\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating adjustments (net of tax)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Acquisition-related charges\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1,329\u00a0\u00a0| 1,424\u00a0\u00a0| (6.7)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating retirement-related costs/(income)\u00a0\u00a0\u00a0\u00a0| 4,933 *\u00a0| 1,031\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| U.S. tax reform impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| (70)\u00a0\u00a0\u00a0| 89\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Kyndryl-related impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 351\u00a0\u00a0\u00a0| (81)\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Operating (non-GAAP) earnings\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $8,326\u00a0\u00a0| $7,174\u00a0\u00a0| 16.0 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Diluted operating (non-GAAP) earnings per share\u00a0\u00a0\u00a0\u00a0| $9 . 1 3 | $7 . 9 3 | 15.1 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nNM-Not meaningful\n\n## Macroeconomic Environment\n\nOur business profile positions us well in challenging times. Our diversification across geographies, industries, clients and business mix provides some stability in revenue, profit and cash generation.\n\nThroughout 2022, we experienced escalating labor and component costs and a strong U.S. dollar. While those dynamics have put pressure on our margin profile, we are seeing progress in the actions we have taken to mitigate the impacts of these higher costs. Consulting, which makes up well over half of IBM's workforce, is most impacted by the labor cost inflation. We have begun to see improved utilization and priced margin improvements year over year, and our acquisitions have become more accretive, all of which will benefit our margin profile going forward. Our Consulting pre-tax margin of 8.8 percent increased 0.7 points in 2022 versus the prior year and improved 3.2 points in the second half of 2022 compared to the first half reflecting the benefit of these actions. Additionally, across all of our product-based businesses, we have executed price increases above our historical level of increases to be more reflective of the labor and component costs we are incurring due to the inflationary environment. This includes price increases in our maintenance and support agreements for our hardware and software portfolios. Additionally, despite the many global supply chain disruptions throughout 2022, our supply chain has demonstrated resiliency and the ability to proactively respond to potential disruptions in order to meet our clients' needs. The strengthening of the U.S. dollar impacted our reported revenue and gross profit dollars in 2022. We execute hedging programs which defer but do not eliminate the impact of currency. The gains from these hedging programs are reflected primarily in other income and expense. With the rate and magnitude of\n\nAnswer the following question: Give me a bulleted list of all the non-operating adjustments (net of tax) that are displayed in the second table.\t\n"], "reference": ["- Acquisition-related charges\n- Non-operating retirement-related costs/ (income)\n- U.S. tax reform impacts\n- Kyndryl-related impacts\n"]} +{"qna_file": "compositional_skills/extraction/annual_report/bullet_points/qna.yaml", "question_id": "158741300142587848903681381168692058502", "category": "taxonomy", "turns": ["Given the context below:\n## 8 Management Discussion\n\n## MANAGEMENT DISCUSSION SNAPSHOT\n\n($ and shares in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022 *\u00a0\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent/Margin Change\u00a0\u00a0|\n|----------------------------------------------------------------------|--------------|---------------|------------------------------------|\n| Revenue\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $6 0 , 5 3 0 | $5 7 , 3 5 0\u00a0| 5.5 %**\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Gross profit margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 54.0 %\u00a0\u00a0\u00a0\u00a0| 54.9 %\u00a0\u00a0\u00a0\u00a0| (0.9)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Total expense and other (income)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $3 1 , 5 3 1 | $2 6 , 4 9\u00a0\u00a0| 18.3 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations before income taxes\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 1 5 6\u00a0\u00a0| $4 , 8 3 7\u00a0\u00a0| (76.1)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Provision for/(benefit from) income taxes from continuing operations | $ (626)\u00a0\u00a0\u00a0| $ 124\u00a0\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 7 8 3\u00a0\u00a0| $4 , 7 1 2\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2.9 %\u00a0\u00a0\u00a0\u00a0| 8.2 %\u00a0\u00a0\u00a0\u00a0\u00a0| (5.3)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ (143) \uf020\u00a0\u00a0| $1 , 0 3\u00a0\u00a0\uf020 | NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Net income\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 6 3 9\u00a0\u00a0| $5 , 7 4 3\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Earnings per share from continuing operations-assuming dilution\u00a0\u00a0\u00a0| $ 1.95\u00a0\u00a0\u00a0\u00a0| $ 5.21\u00a0\u00a0\u00a0\u00a0| (62.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Consolidated earnings per share-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 1.80\u00a0\u00a0\u00a0\u00a0| $ 6.35\u00a0\u00a0\u00a0\u00a0| (71.7)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Weighted-average shares outstanding-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 912.3\u00a0\u00a0\u00a0\u00a0| 904.6\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Assets \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $127,243\u00a0\u00a0\u00a0| $132,001\u00a0\u00a0\u00a0| (3.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Liabilities \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $105,222\u00a0\u00a0\u00a0| $113,005\u00a0\u00a0\u00a0| (6.9)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Equity \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $2 2 , 0 2 1 | $1 8 , 9 6\u00a0\u00a0| 15.9 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\n\uf0c8\uf020 At December 31.\n\nNM-Not meaningful\n\nThe following table provides the company's operating (non-GAAP) earnings for 2022 and 2021. See page 29 for additional information.\n\n($ in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent Change\u00a0\u00a0|\n|--------------------------------------------------------|----------|----------|-----------------------------|\n| Net income as reported\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,639 * | $5,743\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax | (143)\u00a0\u00a0| 1,030\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,783 * | $4,712\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating adjustments (net of tax)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Acquisition-related charges\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1,329\u00a0\u00a0| 1,424\u00a0\u00a0| (6.7)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating retirement-related costs/(income)\u00a0\u00a0\u00a0\u00a0| 4,933 *\u00a0| 1,031\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| U.S. tax reform impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| (70)\u00a0\u00a0\u00a0| 89\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Kyndryl-related impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 351\u00a0\u00a0\u00a0| (81)\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Operating (non-GAAP) earnings\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $8,326\u00a0\u00a0| $7,174\u00a0\u00a0| 16.0 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Diluted operating (non-GAAP) earnings per share\u00a0\u00a0\u00a0\u00a0| $9 . 1 3 | $7 . 9 3 | 15.1 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nNM-Not meaningful\n\n## Macroeconomic Environment\n\nOur business profile positions us well in challenging times. Our diversification across geographies, industries, clients and business mix provides some stability in revenue, profit and cash generation.\n\nThroughout 2022, we experienced escalating labor and component costs and a strong U.S. dollar. While those dynamics have put pressure on our margin profile, we are seeing progress in the actions we have taken to mitigate the impacts of these higher costs. Consulting, which makes up well over half of IBM's workforce, is most impacted by the labor cost inflation. We have begun to see improved utilization and priced margin improvements year over year, and our acquisitions have become more accretive, all of which will benefit our margin profile going forward. Our Consulting pre-tax margin of 8.8 percent increased 0.7 points in 2022 versus the prior year and improved 3.2 points in the second half of 2022 compared to the first half reflecting the benefit of these actions. Additionally, across all of our product-based businesses, we have executed price increases above our historical level of increases to be more reflective of the labor and component costs we are incurring due to the inflationary environment. This includes price increases in our maintenance and support agreements for our hardware and software portfolios. Additionally, despite the many global supply chain disruptions throughout 2022, our supply chain has demonstrated resiliency and the ability to proactively respond to potential disruptions in order to meet our clients' needs. The strengthening of the U.S. dollar impacted our reported revenue and gross profit dollars in 2022. We execute hedging programs which defer but do not eliminate the impact of currency. The gains from these hedging programs are reflected primarily in other income and expense. With the rate and magnitude of\n\nAnswer the following question: Create a list of bullets of all the different types of income displayed in the table at the top of the page. "], "reference": ["- Income from continuing operations before income tax\n- Income from continue operations\n- Income from continuing operations margin\n- Income/ (loss) from discontinued operations, net of tax\n- Net income \n"]} +{"qna_file": "compositional_skills/extraction/annual_report/bullet_points/qna.yaml", "question_id": "191397906590284199538176995018135933190", "category": "taxonomy", "turns": ["Given the context below:\n## 8 Management Discussion\n\n## MANAGEMENT DISCUSSION SNAPSHOT\n\n($ and shares in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022 *\u00a0\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent/Margin Change\u00a0\u00a0|\n|----------------------------------------------------------------------|--------------|---------------|------------------------------------|\n| Revenue\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $6 0 , 5 3 0 | $5 7 , 3 5 0\u00a0| 5.5 %**\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Gross profit margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 54.0 %\u00a0\u00a0\u00a0\u00a0| 54.9 %\u00a0\u00a0\u00a0\u00a0| (0.9)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Total expense and other (income)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $3 1 , 5 3 1 | $2 6 , 4 9\u00a0\u00a0| 18.3 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations before income taxes\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 1 5 6\u00a0\u00a0| $4 , 8 3 7\u00a0\u00a0| (76.1)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Provision for/(benefit from) income taxes from continuing operations | $ (626)\u00a0\u00a0\u00a0| $ 124\u00a0\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 7 8 3\u00a0\u00a0| $4 , 7 1 2\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2.9 %\u00a0\u00a0\u00a0\u00a0| 8.2 %\u00a0\u00a0\u00a0\u00a0\u00a0| (5.3)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ (143) \uf020\u00a0\u00a0| $1 , 0 3\u00a0\u00a0\uf020 | NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Net income\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 6 3 9\u00a0\u00a0| $5 , 7 4 3\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Earnings per share from continuing operations-assuming dilution\u00a0\u00a0\u00a0| $ 1.95\u00a0\u00a0\u00a0\u00a0| $ 5.21\u00a0\u00a0\u00a0\u00a0| (62.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Consolidated earnings per share-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 1.80\u00a0\u00a0\u00a0\u00a0| $ 6.35\u00a0\u00a0\u00a0\u00a0| (71.7)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Weighted-average shares outstanding-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 912.3\u00a0\u00a0\u00a0\u00a0| 904.6\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Assets \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $127,243\u00a0\u00a0\u00a0| $132,001\u00a0\u00a0\u00a0| (3.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Liabilities \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $105,222\u00a0\u00a0\u00a0| $113,005\u00a0\u00a0\u00a0| (6.9)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Equity \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $2 2 , 0 2 1 | $1 8 , 9 6\u00a0\u00a0| 15.9 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\n\uf0c8\uf020 At December 31.\n\nNM-Not meaningful\n\nThe following table provides the company's operating (non-GAAP) earnings for 2022 and 2021. See page 29 for additional information.\n\n($ in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent Change\u00a0\u00a0|\n|--------------------------------------------------------|----------|----------|-----------------------------|\n| Net income as reported\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,639 * | $5,743\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax | (143)\u00a0\u00a0| 1,030\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,783 * | $4,712\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating adjustments (net of tax)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Acquisition-related charges\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1,329\u00a0\u00a0| 1,424\u00a0\u00a0| (6.7)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating retirement-related costs/(income)\u00a0\u00a0\u00a0\u00a0| 4,933 *\u00a0| 1,031\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| U.S. tax reform impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| (70)\u00a0\u00a0\u00a0| 89\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Kyndryl-related impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 351\u00a0\u00a0\u00a0| (81)\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Operating (non-GAAP) earnings\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $8,326\u00a0\u00a0| $7,174\u00a0\u00a0| 16.0 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Diluted operating (non-GAAP) earnings per share\u00a0\u00a0\u00a0\u00a0| $9 . 1 3 | $7 . 9 3 | 15.1 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nNM-Not meaningful\n\n## Macroeconomic Environment\n\nOur business profile positions us well in challenging times. Our diversification across geographies, industries, clients and business mix provides some stability in revenue, profit and cash generation.\n\nThroughout 2022, we experienced escalating labor and component costs and a strong U.S. dollar. While those dynamics have put pressure on our margin profile, we are seeing progress in the actions we have taken to mitigate the impacts of these higher costs. Consulting, which makes up well over half of IBM's workforce, is most impacted by the labor cost inflation. We have begun to see improved utilization and priced margin improvements year over year, and our acquisitions have become more accretive, all of which will benefit our margin profile going forward. Our Consulting pre-tax margin of 8.8 percent increased 0.7 points in 2022 versus the prior year and improved 3.2 points in the second half of 2022 compared to the first half reflecting the benefit of these actions. Additionally, across all of our product-based businesses, we have executed price increases above our historical level of increases to be more reflective of the labor and component costs we are incurring due to the inflationary environment. This includes price increases in our maintenance and support agreements for our hardware and software portfolios. Additionally, despite the many global supply chain disruptions throughout 2022, our supply chain has demonstrated resiliency and the ability to proactively respond to potential disruptions in order to meet our clients' needs. The strengthening of the U.S. dollar impacted our reported revenue and gross profit dollars in 2022. We execute hedging programs which defer but do not eliminate the impact of currency. The gains from these hedging programs are reflected primarily in other income and expense. With the rate and magnitude of\n\nAnswer the following question: Which years do these charts cover? Give me your response in a list in bullet points. \n"], "reference": ["- 2021\n- 2022\n"]} +{"qna_file": "compositional_skills/extraction/annual_report/plain_text/qna.yaml", "question_id": "166558449554428886722058627655821217468", "category": "taxonomy", "turns": ["Given the context below:\n## 8 Management Discussion\n\n## MANAGEMENT DISCUSSION SNAPSHOT\n\n($ and shares in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022 *\u00a0\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent/Margin Change\u00a0\u00a0|\n|----------------------------------------------------------------------|--------------|---------------|------------------------------------|\n| Revenue\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $6 0 , 5 3 0 | $5 7 , 3 5 0\u00a0| 5.5 %**\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Gross profit margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 54.0 %\u00a0\u00a0\u00a0\u00a0| 54.9 %\u00a0\u00a0\u00a0\u00a0| (0.9)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Total expense and other (income)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $3 1 , 5 3 1 | $2 6 , 4 9\u00a0\u00a0| 18.3 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations before income taxes\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 1 5 6\u00a0\u00a0| $4 , 8 3 7\u00a0\u00a0| (76.1)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Provision for/(benefit from) income taxes from continuing operations | $ (626)\u00a0\u00a0\u00a0| $ 124\u00a0\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 7 8 3\u00a0\u00a0| $4 , 7 1 2\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2.9 %\u00a0\u00a0\u00a0\u00a0| 8.2 %\u00a0\u00a0\u00a0\u00a0\u00a0| (5.3)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ (143) \uf020\u00a0\u00a0| $1 , 0 3\u00a0\u00a0\uf020 | NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Net income\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 6 3 9\u00a0\u00a0| $5 , 7 4 3\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Earnings per share from continuing operations-assuming dilution\u00a0\u00a0\u00a0| $ 1.95\u00a0\u00a0\u00a0\u00a0| $ 5.21\u00a0\u00a0\u00a0\u00a0| (62.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Consolidated earnings per share-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 1.80\u00a0\u00a0\u00a0\u00a0| $ 6.35\u00a0\u00a0\u00a0\u00a0| (71.7)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Weighted-average shares outstanding-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 912.3\u00a0\u00a0\u00a0\u00a0| 904.6\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Assets \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $127,243\u00a0\u00a0\u00a0| $132,001\u00a0\u00a0\u00a0| (3.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Liabilities \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $105,222\u00a0\u00a0\u00a0| $113,005\u00a0\u00a0\u00a0| (6.9)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Equity \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $2 2 , 0 2 1 | $1 8 , 9 6\u00a0\u00a0| 15.9 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\n\uf0c8\uf020 At December 31.\n\nNM-Not meaningful\n\nThe following table provides the company's operating (non-GAAP) earnings for 2022 and 2021. See page 29 for additional information.\n\n($ in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent Change\u00a0\u00a0|\n|--------------------------------------------------------|----------|----------|-----------------------------|\n| Net income as reported\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,639 * | $5,743\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax | (143)\u00a0\u00a0| 1,030\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,783 * | $4,712\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating adjustments (net of tax)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Acquisition-related charges\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1,329\u00a0\u00a0| 1,424\u00a0\u00a0| (6.7)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating retirement-related costs/(income)\u00a0\u00a0\u00a0\u00a0| 4,933 *\u00a0| 1,031\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| U.S. tax reform impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| (70)\u00a0\u00a0\u00a0| 89\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Kyndryl-related impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 351\u00a0\u00a0\u00a0| (81)\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Operating (non-GAAP) earnings\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $8,326\u00a0\u00a0| $7,174\u00a0\u00a0| 16.0 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Diluted operating (non-GAAP) earnings per share\u00a0\u00a0\u00a0\u00a0| $9 . 1 3 | $7 . 9 3 | 15.1 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nNM-Not meaningful\n\n## Macroeconomic Environment\n\nOur business profile positions us well in challenging times. Our diversification across geographies, industries, clients and business mix provides some stability in revenue, profit and cash generation.\n\nThroughout 2022, we experienced escalating labor and component costs and a strong U.S. dollar. While those dynamics have put pressure on our margin profile, we are seeing progress in the actions we have taken to mitigate the impacts of these higher costs. Consulting, which makes up well over half of IBM's workforce, is most impacted by the labor cost inflation. We have begun to see improved utilization and priced margin improvements year over year, and our acquisitions have become more accretive, all of which will benefit our margin profile going forward. Our Consulting pre-tax margin of 8.8 percent increased 0.7 points in 2022 versus the prior year and improved 3.2 points in the second half of 2022 compared to the first half reflecting the benefit of these actions. Additionally, across all of our product-based businesses, we have executed price increases above our historical level of increases to be more reflective of the labor and component costs we are incurring due to the inflationary environment. This includes price increases in our maintenance and support agreements for our hardware and software portfolios. Additionally, despite the many global supply chain disruptions throughout 2022, our supply chain has demonstrated resiliency and the ability to proactively respond to potential disruptions in order to meet our clients' needs. The strengthening of the U.S. dollar impacted our reported revenue and gross profit dollars in 2022. We execute hedging programs which defer but do not eliminate the impact of currency. The gains from these hedging programs are reflected primarily in other income and expense. With the rate and magnitude of\n\nAnswer the following question: What is more stable as a result of IBM's diversification across geographies, industries, clients, and business mix?\n"], "reference": ["Revenue, Profit, Cash Generation \n"]} +{"qna_file": "compositional_skills/extraction/annual_report/plain_text/qna.yaml", "question_id": "236683433202528146576591774277469991688", "category": "taxonomy", "turns": ["Given the context below:\n## 8 Management Discussion\n\n## MANAGEMENT DISCUSSION SNAPSHOT\n\n($ and shares in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022 *\u00a0\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent/Margin Change\u00a0\u00a0|\n|----------------------------------------------------------------------|--------------|---------------|------------------------------------|\n| Revenue\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $6 0 , 5 3 0 | $5 7 , 3 5 0\u00a0| 5.5 %**\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Gross profit margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 54.0 %\u00a0\u00a0\u00a0\u00a0| 54.9 %\u00a0\u00a0\u00a0\u00a0| (0.9)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Total expense and other (income)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $3 1 , 5 3 1 | $2 6 , 4 9\u00a0\u00a0| 18.3 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations before income taxes\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 1 5 6\u00a0\u00a0| $4 , 8 3 7\u00a0\u00a0| (76.1)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Provision for/(benefit from) income taxes from continuing operations | $ (626)\u00a0\u00a0\u00a0| $ 124\u00a0\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 7 8 3\u00a0\u00a0| $4 , 7 1 2\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2.9 %\u00a0\u00a0\u00a0\u00a0| 8.2 %\u00a0\u00a0\u00a0\u00a0\u00a0| (5.3)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ (143) \uf020\u00a0\u00a0| $1 , 0 3\u00a0\u00a0\uf020 | NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Net income\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 6 3 9\u00a0\u00a0| $5 , 7 4 3\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Earnings per share from continuing operations-assuming dilution\u00a0\u00a0\u00a0| $ 1.95\u00a0\u00a0\u00a0\u00a0| $ 5.21\u00a0\u00a0\u00a0\u00a0| (62.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Consolidated earnings per share-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 1.80\u00a0\u00a0\u00a0\u00a0| $ 6.35\u00a0\u00a0\u00a0\u00a0| (71.7)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Weighted-average shares outstanding-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 912.3\u00a0\u00a0\u00a0\u00a0| 904.6\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Assets \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $127,243\u00a0\u00a0\u00a0| $132,001\u00a0\u00a0\u00a0| (3.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Liabilities \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $105,222\u00a0\u00a0\u00a0| $113,005\u00a0\u00a0\u00a0| (6.9)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Equity \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $2 2 , 0 2 1 | $1 8 , 9 6\u00a0\u00a0| 15.9 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\n\uf0c8\uf020 At December 31.\n\nNM-Not meaningful\n\nThe following table provides the company's operating (non-GAAP) earnings for 2022 and 2021. See page 29 for additional information.\n\n($ in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent Change\u00a0\u00a0|\n|--------------------------------------------------------|----------|----------|-----------------------------|\n| Net income as reported\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,639 * | $5,743\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax | (143)\u00a0\u00a0| 1,030\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,783 * | $4,712\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating adjustments (net of tax)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Acquisition-related charges\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1,329\u00a0\u00a0| 1,424\u00a0\u00a0| (6.7)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating retirement-related costs/(income)\u00a0\u00a0\u00a0\u00a0| 4,933 *\u00a0| 1,031\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| U.S. tax reform impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| (70)\u00a0\u00a0\u00a0| 89\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Kyndryl-related impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 351\u00a0\u00a0\u00a0| (81)\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Operating (non-GAAP) earnings\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $8,326\u00a0\u00a0| $7,174\u00a0\u00a0| 16.0 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Diluted operating (non-GAAP) earnings per share\u00a0\u00a0\u00a0\u00a0| $9 . 1 3 | $7 . 9 3 | 15.1 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nNM-Not meaningful\n\n## Macroeconomic Environment\n\nOur business profile positions us well in challenging times. Our diversification across geographies, industries, clients and business mix provides some stability in revenue, profit and cash generation.\n\nThroughout 2022, we experienced escalating labor and component costs and a strong U.S. dollar. While those dynamics have put pressure on our margin profile, we are seeing progress in the actions we have taken to mitigate the impacts of these higher costs. Consulting, which makes up well over half of IBM's workforce, is most impacted by the labor cost inflation. We have begun to see improved utilization and priced margin improvements year over year, and our acquisitions have become more accretive, all of which will benefit our margin profile going forward. Our Consulting pre-tax margin of 8.8 percent increased 0.7 points in 2022 versus the prior year and improved 3.2 points in the second half of 2022 compared to the first half reflecting the benefit of these actions. Additionally, across all of our product-based businesses, we have executed price increases above our historical level of increases to be more reflective of the labor and component costs we are incurring due to the inflationary environment. This includes price increases in our maintenance and support agreements for our hardware and software portfolios. Additionally, despite the many global supply chain disruptions throughout 2022, our supply chain has demonstrated resiliency and the ability to proactively respond to potential disruptions in order to meet our clients' needs. The strengthening of the U.S. dollar impacted our reported revenue and gross profit dollars in 2022. We execute hedging programs which defer but do not eliminate the impact of currency. The gains from these hedging programs are reflected primarily in other income and expense. With the rate and magnitude of\n\nAnswer the following question: List the diluted operating (non-GAAP) earnings per share from 2021 and 2022, respectively. "], "reference": ["$7,174,000,000, $8,326,000,000\n"]} +{"qna_file": "compositional_skills/extraction/annual_report/plain_text/qna.yaml", "question_id": "103545471824963677995355919730420965985", "category": "taxonomy", "turns": ["Given the context below:\n## 8 Management Discussion\n\n## MANAGEMENT DISCUSSION SNAPSHOT\n\n($ and shares in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022 *\u00a0\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent/Margin Change\u00a0\u00a0|\n|----------------------------------------------------------------------|--------------|---------------|------------------------------------|\n| Revenue\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $6 0 , 5 3 0 | $5 7 , 3 5 0\u00a0| 5.5 %**\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Gross profit margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 54.0 %\u00a0\u00a0\u00a0\u00a0| 54.9 %\u00a0\u00a0\u00a0\u00a0| (0.9)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Total expense and other (income)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $3 1 , 5 3 1 | $2 6 , 4 9\u00a0\u00a0| 18.3 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations before income taxes\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 1 5 6\u00a0\u00a0| $4 , 8 3 7\u00a0\u00a0| (76.1)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Provision for/(benefit from) income taxes from continuing operations | $ (626)\u00a0\u00a0\u00a0| $ 124\u00a0\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 7 8 3\u00a0\u00a0| $4 , 7 1 2\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2.9 %\u00a0\u00a0\u00a0\u00a0| 8.2 %\u00a0\u00a0\u00a0\u00a0\u00a0| (5.3)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ (143) \uf020\u00a0\u00a0| $1 , 0 3\u00a0\u00a0\uf020 | NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Net income\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 6 3 9\u00a0\u00a0| $5 , 7 4 3\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Earnings per share from continuing operations-assuming dilution\u00a0\u00a0\u00a0| $ 1.95\u00a0\u00a0\u00a0\u00a0| $ 5.21\u00a0\u00a0\u00a0\u00a0| (62.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Consolidated earnings per share-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 1.80\u00a0\u00a0\u00a0\u00a0| $ 6.35\u00a0\u00a0\u00a0\u00a0| (71.7)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Weighted-average shares outstanding-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 912.3\u00a0\u00a0\u00a0\u00a0| 904.6\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Assets \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $127,243\u00a0\u00a0\u00a0| $132,001\u00a0\u00a0\u00a0| (3.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Liabilities \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $105,222\u00a0\u00a0\u00a0| $113,005\u00a0\u00a0\u00a0| (6.9)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Equity \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $2 2 , 0 2 1 | $1 8 , 9 6\u00a0\u00a0| 15.9 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\n\uf0c8\uf020 At December 31.\n\nNM-Not meaningful\n\nThe following table provides the company's operating (non-GAAP) earnings for 2022 and 2021. See page 29 for additional information.\n\n($ in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent Change\u00a0\u00a0|\n|--------------------------------------------------------|----------|----------|-----------------------------|\n| Net income as reported\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,639 * | $5,743\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax | (143)\u00a0\u00a0| 1,030\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,783 * | $4,712\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating adjustments (net of tax)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Acquisition-related charges\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1,329\u00a0\u00a0| 1,424\u00a0\u00a0| (6.7)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating retirement-related costs/(income)\u00a0\u00a0\u00a0\u00a0| 4,933 *\u00a0| 1,031\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| U.S. tax reform impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| (70)\u00a0\u00a0\u00a0| 89\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Kyndryl-related impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 351\u00a0\u00a0\u00a0| (81)\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Operating (non-GAAP) earnings\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $8,326\u00a0\u00a0| $7,174\u00a0\u00a0| 16.0 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Diluted operating (non-GAAP) earnings per share\u00a0\u00a0\u00a0\u00a0| $9 . 1 3 | $7 . 9 3 | 15.1 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nNM-Not meaningful\n\n## Macroeconomic Environment\n\nOur business profile positions us well in challenging times. Our diversification across geographies, industries, clients and business mix provides some stability in revenue, profit and cash generation.\n\nThroughout 2022, we experienced escalating labor and component costs and a strong U.S. dollar. While those dynamics have put pressure on our margin profile, we are seeing progress in the actions we have taken to mitigate the impacts of these higher costs. Consulting, which makes up well over half of IBM's workforce, is most impacted by the labor cost inflation. We have begun to see improved utilization and priced margin improvements year over year, and our acquisitions have become more accretive, all of which will benefit our margin profile going forward. Our Consulting pre-tax margin of 8.8 percent increased 0.7 points in 2022 versus the prior year and improved 3.2 points in the second half of 2022 compared to the first half reflecting the benefit of these actions. Additionally, across all of our product-based businesses, we have executed price increases above our historical level of increases to be more reflective of the labor and component costs we are incurring due to the inflationary environment. This includes price increases in our maintenance and support agreements for our hardware and software portfolios. Additionally, despite the many global supply chain disruptions throughout 2022, our supply chain has demonstrated resiliency and the ability to proactively respond to potential disruptions in order to meet our clients' needs. The strengthening of the U.S. dollar impacted our reported revenue and gross profit dollars in 2022. We execute hedging programs which defer but do not eliminate the impact of currency. The gains from these hedging programs are reflected primarily in other income and expense. With the rate and magnitude of\n\nAnswer the following question: Give me a list of all the types of non-operating adjustments (net of tax) shown in the 2nd table. \t\n"], "reference": ["Acquisition-related charges, non-operating retirement-related costs/ (income), U.S. tax reform impacts, Kyndryl-related impacts. \n"]} +{"qna_file": "compositional_skills/extraction/annual_report/markdown/qna.yaml", "question_id": "3371116730474509402118326993539166607", "category": "taxonomy", "turns": ["Given the context below:\n## 8 Management Discussion\n\n## MANAGEMENT DISCUSSION SNAPSHOT\n\n($ and shares in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022 *\u00a0\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent/Margin Change\u00a0\u00a0|\n|----------------------------------------------------------------------|--------------|---------------|------------------------------------|\n| Revenue\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $6 0 , 5 3 0 | $5 7 , 3 5 0\u00a0| 5.5 %**\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Gross profit margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 54.0 %\u00a0\u00a0\u00a0\u00a0| 54.9 %\u00a0\u00a0\u00a0\u00a0| (0.9)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Total expense and other (income)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $3 1 , 5 3 1 | $2 6 , 4 9\u00a0\u00a0| 18.3 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations before income taxes\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 1 5 6\u00a0\u00a0| $4 , 8 3 7\u00a0\u00a0| (76.1)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Provision for/(benefit from) income taxes from continuing operations | $ (626)\u00a0\u00a0\u00a0| $ 124\u00a0\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 7 8 3\u00a0\u00a0| $4 , 7 1 2\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2.9 %\u00a0\u00a0\u00a0\u00a0| 8.2 %\u00a0\u00a0\u00a0\u00a0\u00a0| (5.3)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ (143) \uf020\u00a0\u00a0| $1 , 0 3\u00a0\u00a0\uf020 | NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Net income\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 6 3 9\u00a0\u00a0| $5 , 7 4 3\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Earnings per share from continuing operations-assuming dilution\u00a0\u00a0\u00a0| $ 1.95\u00a0\u00a0\u00a0\u00a0| $ 5.21\u00a0\u00a0\u00a0\u00a0| (62.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Consolidated earnings per share-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 1.80\u00a0\u00a0\u00a0\u00a0| $ 6.35\u00a0\u00a0\u00a0\u00a0| (71.7)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Weighted-average shares outstanding-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 912.3\u00a0\u00a0\u00a0\u00a0| 904.6\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Assets \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $127,243\u00a0\u00a0\u00a0| $132,001\u00a0\u00a0\u00a0| (3.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Liabilities \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $105,222\u00a0\u00a0\u00a0| $113,005\u00a0\u00a0\u00a0| (6.9)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Equity \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $2 2 , 0 2 1 | $1 8 , 9 6\u00a0\u00a0| 15.9 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\n\uf0c8\uf020 At December 31.\n\nNM-Not meaningful\n\nThe following table provides the company's operating (non-GAAP) earnings for 2022 and 2021. See page 29 for additional information.\n\n($ in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent Change\u00a0\u00a0|\n|--------------------------------------------------------|----------|----------|-----------------------------|\n| Net income as reported\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,639 * | $5,743\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax | (143)\u00a0\u00a0| 1,030\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,783 * | $4,712\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating adjustments (net of tax)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Acquisition-related charges\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1,329\u00a0\u00a0| 1,424\u00a0\u00a0| (6.7)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating retirement-related costs/(income)\u00a0\u00a0\u00a0\u00a0| 4,933 *\u00a0| 1,031\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| U.S. tax reform impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| (70)\u00a0\u00a0\u00a0| 89\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Kyndryl-related impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 351\u00a0\u00a0\u00a0| (81)\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Operating (non-GAAP) earnings\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $8,326\u00a0\u00a0| $7,174\u00a0\u00a0| 16.0 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Diluted operating (non-GAAP) earnings per share\u00a0\u00a0\u00a0\u00a0| $9 . 1 3 | $7 . 9 3 | 15.1 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nNM-Not meaningful\n\n## Macroeconomic Environment\n\nOur business profile positions us well in challenging times. Our diversification across geographies, industries, clients and business mix provides some stability in revenue, profit and cash generation.\n\nThroughout 2022, we experienced escalating labor and component costs and a strong U.S. dollar. While those dynamics have put pressure on our margin profile, we are seeing progress in the actions we have taken to mitigate the impacts of these higher costs. Consulting, which makes up well over half of IBM's workforce, is most impacted by the labor cost inflation. We have begun to see improved utilization and priced margin improvements year over year, and our acquisitions have become more accretive, all of which will benefit our margin profile going forward. Our Consulting pre-tax margin of 8.8 percent increased 0.7 points in 2022 versus the prior year and improved 3.2 points in the second half of 2022 compared to the first half reflecting the benefit of these actions. Additionally, across all of our product-based businesses, we have executed price increases above our historical level of increases to be more reflective of the labor and component costs we are incurring due to the inflationary environment. This includes price increases in our maintenance and support agreements for our hardware and software portfolios. Additionally, despite the many global supply chain disruptions throughout 2022, our supply chain has demonstrated resiliency and the ability to proactively respond to potential disruptions in order to meet our clients' needs. The strengthening of the U.S. dollar impacted our reported revenue and gross profit dollars in 2022. We execute hedging programs which defer but do not eliminate the impact of currency. The gains from these hedging programs are reflected primarily in other income and expense. With the rate and magnitude of\n\nAnswer the following question: Please provide a markdown of all non-operating adjustments net of tax from the table that provides the company's operation (non-GAAP) earnings from 2022 and 2021. \n"], "reference": ["```\n| Non-operating adjustments (net of tax) | | | |\n| --------------------------------------------------- | ------- | ----- | ----- |\n| \u00a0\u00a0\u00a0\u00a0Acquisisition-related charges | 1,329 | 1,424 | (6.7) |\n| \u00a0\u00a0\u00a0\u00a0Non-operating retirement-related costs/(income) | 4,933\\* | 1,031 | NM |\n| \u00a0\u00a0\u00a0\u00a0U.S. tax reform impacts | (70) | 89 | NM |\n| \u00a0\u00a0\u00a0\u00a0Kyndryl-related impacts | 351 | (81) | NM |\n```\n\n"]} +{"qna_file": "compositional_skills/extraction/annual_report/markdown/qna.yaml", "question_id": "29373606347768801198458658534540605025", "category": "taxonomy", "turns": ["Given the context below:\n## 8 Management Discussion\n\n## MANAGEMENT DISCUSSION SNAPSHOT\n\n($ and shares in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022 *\u00a0\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent/Margin Change\u00a0\u00a0|\n|----------------------------------------------------------------------|--------------|---------------|------------------------------------|\n| Revenue\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $6 0 , 5 3 0 | $5 7 , 3 5 0\u00a0| 5.5 %**\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Gross profit margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 54.0 %\u00a0\u00a0\u00a0\u00a0| 54.9 %\u00a0\u00a0\u00a0\u00a0| (0.9)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Total expense and other (income)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $3 1 , 5 3 1 | $2 6 , 4 9\u00a0\u00a0| 18.3 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations before income taxes\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 1 5 6\u00a0\u00a0| $4 , 8 3 7\u00a0\u00a0| (76.1)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Provision for/(benefit from) income taxes from continuing operations | $ (626)\u00a0\u00a0\u00a0| $ 124\u00a0\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 7 8 3\u00a0\u00a0| $4 , 7 1 2\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2.9 %\u00a0\u00a0\u00a0\u00a0| 8.2 %\u00a0\u00a0\u00a0\u00a0\u00a0| (5.3)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ (143) \uf020\u00a0\u00a0| $1 , 0 3\u00a0\u00a0\uf020 | NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Net income\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 6 3 9\u00a0\u00a0| $5 , 7 4 3\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Earnings per share from continuing operations-assuming dilution\u00a0\u00a0\u00a0| $ 1.95\u00a0\u00a0\u00a0\u00a0| $ 5.21\u00a0\u00a0\u00a0\u00a0| (62.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Consolidated earnings per share-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 1.80\u00a0\u00a0\u00a0\u00a0| $ 6.35\u00a0\u00a0\u00a0\u00a0| (71.7)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Weighted-average shares outstanding-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 912.3\u00a0\u00a0\u00a0\u00a0| 904.6\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Assets \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $127,243\u00a0\u00a0\u00a0| $132,001\u00a0\u00a0\u00a0| (3.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Liabilities \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $105,222\u00a0\u00a0\u00a0| $113,005\u00a0\u00a0\u00a0| (6.9)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Equity \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $2 2 , 0 2 1 | $1 8 , 9 6\u00a0\u00a0| 15.9 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\n\uf0c8\uf020 At December 31.\n\nNM-Not meaningful\n\nThe following table provides the company's operating (non-GAAP) earnings for 2022 and 2021. See page 29 for additional information.\n\n($ in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent Change\u00a0\u00a0|\n|--------------------------------------------------------|----------|----------|-----------------------------|\n| Net income as reported\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,639 * | $5,743\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax | (143)\u00a0\u00a0| 1,030\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,783 * | $4,712\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating adjustments (net of tax)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Acquisition-related charges\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1,329\u00a0\u00a0| 1,424\u00a0\u00a0| (6.7)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating retirement-related costs/(income)\u00a0\u00a0\u00a0\u00a0| 4,933 *\u00a0| 1,031\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| U.S. tax reform impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| (70)\u00a0\u00a0\u00a0| 89\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Kyndryl-related impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 351\u00a0\u00a0\u00a0| (81)\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Operating (non-GAAP) earnings\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $8,326\u00a0\u00a0| $7,174\u00a0\u00a0| 16.0 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Diluted operating (non-GAAP) earnings per share\u00a0\u00a0\u00a0\u00a0| $9 . 1 3 | $7 . 9 3 | 15.1 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nNM-Not meaningful\n\n## Macroeconomic Environment\n\nOur business profile positions us well in challenging times. Our diversification across geographies, industries, clients and business mix provides some stability in revenue, profit and cash generation.\n\nThroughout 2022, we experienced escalating labor and component costs and a strong U.S. dollar. While those dynamics have put pressure on our margin profile, we are seeing progress in the actions we have taken to mitigate the impacts of these higher costs. Consulting, which makes up well over half of IBM's workforce, is most impacted by the labor cost inflation. We have begun to see improved utilization and priced margin improvements year over year, and our acquisitions have become more accretive, all of which will benefit our margin profile going forward. Our Consulting pre-tax margin of 8.8 percent increased 0.7 points in 2022 versus the prior year and improved 3.2 points in the second half of 2022 compared to the first half reflecting the benefit of these actions. Additionally, across all of our product-based businesses, we have executed price increases above our historical level of increases to be more reflective of the labor and component costs we are incurring due to the inflationary environment. This includes price increases in our maintenance and support agreements for our hardware and software portfolios. Additionally, despite the many global supply chain disruptions throughout 2022, our supply chain has demonstrated resiliency and the ability to proactively respond to potential disruptions in order to meet our clients' needs. The strengthening of the U.S. dollar impacted our reported revenue and gross profit dollars in 2022. We execute hedging programs which defer but do not eliminate the impact of currency. The gains from these hedging programs are reflected primarily in other income and expense. With the rate and magnitude of\n\nAnswer the following question: Give me a markdown table showing all of the positive Year-to-Year Percent/Margin Change values from the first table. Make the table have 4 columns with \"For year ended December 31\", \"2022\", \"2021\", and \"Yr.-to-.Yr. Percent/Margin Change\" be the column headers. "], "reference": ["```\n| For year ended December 31 | 2022 | 2021 | Yr.-to-Yr. Percent/Margin Change |\n| ----------------------------------------------------- | ------------ | ------------ | -------------------------------- |\n| Revenue | $60,530 | $57,350 | 5.5%\\*\\* |\n| Total expense and other (income) | $31,531 | $26,649 | 18.3% | \n| Weighted-average shares outstanding-assuming dilution | 912.3 | 904.6 | 0.8% |\n| Equity | $ 22,021 | $18,996 | 15.9% \n```\n\n"]} +{"qna_file": "compositional_skills/extraction/annual_report/markdown/qna.yaml", "question_id": "32595901518022412813368895078677464330", "category": "taxonomy", "turns": ["Given the context below:\n## 8 Management Discussion\n\n## MANAGEMENT DISCUSSION SNAPSHOT\n\n($ and shares in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022 *\u00a0\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent/Margin Change\u00a0\u00a0|\n|----------------------------------------------------------------------|--------------|---------------|------------------------------------|\n| Revenue\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $6 0 , 5 3 0 | $5 7 , 3 5 0\u00a0| 5.5 %**\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Gross profit margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 54.0 %\u00a0\u00a0\u00a0\u00a0| 54.9 %\u00a0\u00a0\u00a0\u00a0| (0.9)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Total expense and other (income)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $3 1 , 5 3 1 | $2 6 , 4 9\u00a0\u00a0| 18.3 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations before income taxes\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 1 5 6\u00a0\u00a0| $4 , 8 3 7\u00a0\u00a0| (76.1)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Provision for/(benefit from) income taxes from continuing operations | $ (626)\u00a0\u00a0\u00a0| $ 124\u00a0\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 7 8 3\u00a0\u00a0| $4 , 7 1 2\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2.9 %\u00a0\u00a0\u00a0\u00a0| 8.2 %\u00a0\u00a0\u00a0\u00a0\u00a0| (5.3)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ (143) \uf020\u00a0\u00a0| $1 , 0 3\u00a0\u00a0\uf020 | NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Net income\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 6 3 9\u00a0\u00a0| $5 , 7 4 3\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Earnings per share from continuing operations-assuming dilution\u00a0\u00a0\u00a0| $ 1.95\u00a0\u00a0\u00a0\u00a0| $ 5.21\u00a0\u00a0\u00a0\u00a0| (62.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Consolidated earnings per share-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 1.80\u00a0\u00a0\u00a0\u00a0| $ 6.35\u00a0\u00a0\u00a0\u00a0| (71.7)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Weighted-average shares outstanding-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 912.3\u00a0\u00a0\u00a0\u00a0| 904.6\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Assets \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $127,243\u00a0\u00a0\u00a0| $132,001\u00a0\u00a0\u00a0| (3.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Liabilities \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $105,222\u00a0\u00a0\u00a0| $113,005\u00a0\u00a0\u00a0| (6.9)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Equity \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $2 2 , 0 2 1 | $1 8 , 9 6\u00a0\u00a0| 15.9 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\n\uf0c8\uf020 At December 31.\n\nNM-Not meaningful\n\nThe following table provides the company's operating (non-GAAP) earnings for 2022 and 2021. See page 29 for additional information.\n\n($ in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent Change\u00a0\u00a0|\n|--------------------------------------------------------|----------|----------|-----------------------------|\n| Net income as reported\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,639 * | $5,743\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax | (143)\u00a0\u00a0| 1,030\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,783 * | $4,712\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating adjustments (net of tax)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Acquisition-related charges\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1,329\u00a0\u00a0| 1,424\u00a0\u00a0| (6.7)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating retirement-related costs/(income)\u00a0\u00a0\u00a0\u00a0| 4,933 *\u00a0| 1,031\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| U.S. tax reform impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| (70)\u00a0\u00a0\u00a0| 89\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Kyndryl-related impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 351\u00a0\u00a0\u00a0| (81)\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Operating (non-GAAP) earnings\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $8,326\u00a0\u00a0| $7,174\u00a0\u00a0| 16.0 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Diluted operating (non-GAAP) earnings per share\u00a0\u00a0\u00a0\u00a0| $9 . 1 3 | $7 . 9 3 | 15.1 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nNM-Not meaningful\n\n## Macroeconomic Environment\n\nOur business profile positions us well in challenging times. Our diversification across geographies, industries, clients and business mix provides some stability in revenue, profit and cash generation.\n\nThroughout 2022, we experienced escalating labor and component costs and a strong U.S. dollar. While those dynamics have put pressure on our margin profile, we are seeing progress in the actions we have taken to mitigate the impacts of these higher costs. Consulting, which makes up well over half of IBM's workforce, is most impacted by the labor cost inflation. We have begun to see improved utilization and priced margin improvements year over year, and our acquisitions have become more accretive, all of which will benefit our margin profile going forward. Our Consulting pre-tax margin of 8.8 percent increased 0.7 points in 2022 versus the prior year and improved 3.2 points in the second half of 2022 compared to the first half reflecting the benefit of these actions. Additionally, across all of our product-based businesses, we have executed price increases above our historical level of increases to be more reflective of the labor and component costs we are incurring due to the inflationary environment. This includes price increases in our maintenance and support agreements for our hardware and software portfolios. Additionally, despite the many global supply chain disruptions throughout 2022, our supply chain has demonstrated resiliency and the ability to proactively respond to potential disruptions in order to meet our clients' needs. The strengthening of the U.S. dollar impacted our reported revenue and gross profit dollars in 2022. We execute hedging programs which defer but do not eliminate the impact of currency. The gains from these hedging programs are reflected primarily in other income and expense. With the rate and magnitude of\n\nAnswer the following question: Extract the final value of assets, liabilities, and equity at December 31 of 2022 and 2021. Create a markdown table returning those values. \n"], "reference": ["```\n| | 2022 | 2021 |\n| ----------- | -------------- | ------------- |\n| Assets | \u00a0$\u00a0\u00a0\u00a0127,243 | \u00a0$\u00a0\u00a0\u00a0132,001 |\n| Liabilities | \u00a0$\u00a0\u00a0\u00a0105,222 | \u00a0$\u00a0\u00a0\u00a0113,005 |\n| Equity | \u00a0$\u00a0\u00a0\u00a0\u00a022,021 | \u00a0$\u00a0\u00a0\u00a0\u00a018,996 |\n```\n\n"]} +{"qna_file": "compositional_skills/extraction/annual_report/reasoning/qna.yaml", "question_id": "202761913534005021305273408192319569173", "category": "taxonomy", "turns": ["Given the context below:\n## 8 Management Discussion\n\n## MANAGEMENT DISCUSSION SNAPSHOT\n\n($ and shares in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022 *\u00a0\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent/Margin Change\u00a0\u00a0|\n|----------------------------------------------------------------------|--------------|---------------|------------------------------------|\n| Revenue\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $6 0 , 5 3 0 | $5 7 , 3 5 0\u00a0| 5.5 %**\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Gross profit margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 54.0 %\u00a0\u00a0\u00a0\u00a0| 54.9 %\u00a0\u00a0\u00a0\u00a0| (0.9)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Total expense and other (income)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $3 1 , 5 3 1 | $2 6 , 4 9\u00a0\u00a0| 18.3 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations before income taxes\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 1 5 6\u00a0\u00a0| $4 , 8 3 7\u00a0\u00a0| (76.1)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Provision for/(benefit from) income taxes from continuing operations | $ (626)\u00a0\u00a0\u00a0| $ 124\u00a0\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 7 8 3\u00a0\u00a0| $4 , 7 1 2\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2.9 %\u00a0\u00a0\u00a0\u00a0| 8.2 %\u00a0\u00a0\u00a0\u00a0\u00a0| (5.3)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ (143) \uf020\u00a0\u00a0| $1 , 0 3\u00a0\u00a0\uf020 | NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Net income\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 6 3 9\u00a0\u00a0| $5 , 7 4 3\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Earnings per share from continuing operations-assuming dilution\u00a0\u00a0\u00a0| $ 1.95\u00a0\u00a0\u00a0\u00a0| $ 5.21\u00a0\u00a0\u00a0\u00a0| (62.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Consolidated earnings per share-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 1.80\u00a0\u00a0\u00a0\u00a0| $ 6.35\u00a0\u00a0\u00a0\u00a0| (71.7)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Weighted-average shares outstanding-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 912.3\u00a0\u00a0\u00a0\u00a0| 904.6\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Assets \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $127,243\u00a0\u00a0\u00a0| $132,001\u00a0\u00a0\u00a0| (3.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Liabilities \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $105,222\u00a0\u00a0\u00a0| $113,005\u00a0\u00a0\u00a0| (6.9)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Equity \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $2 2 , 0 2 1 | $1 8 , 9 6\u00a0\u00a0| 15.9 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\n\uf0c8\uf020 At December 31.\n\nNM-Not meaningful\n\nThe following table provides the company's operating (non-GAAP) earnings for 2022 and 2021. See page 29 for additional information.\n\n($ in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent Change\u00a0\u00a0|\n|--------------------------------------------------------|----------|----------|-----------------------------|\n| Net income as reported\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,639 * | $5,743\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax | (143)\u00a0\u00a0| 1,030\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,783 * | $4,712\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating adjustments (net of tax)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Acquisition-related charges\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1,329\u00a0\u00a0| 1,424\u00a0\u00a0| (6.7)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating retirement-related costs/(income)\u00a0\u00a0\u00a0\u00a0| 4,933 *\u00a0| 1,031\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| U.S. tax reform impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| (70)\u00a0\u00a0\u00a0| 89\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Kyndryl-related impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 351\u00a0\u00a0\u00a0| (81)\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Operating (non-GAAP) earnings\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $8,326\u00a0\u00a0| $7,174\u00a0\u00a0| 16.0 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Diluted operating (non-GAAP) earnings per share\u00a0\u00a0\u00a0\u00a0| $9 . 1 3 | $7 . 9 3 | 15.1 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nNM-Not meaningful\n\n## Macroeconomic Environment\n\nOur business profile positions us well in challenging times. Our diversification across geographies, industries, clients and business mix provides some stability in revenue, profit and cash generation.\n\nThroughout 2022, we experienced escalating labor and component costs and a strong U.S. dollar. While those dynamics have put pressure on our margin profile, we are seeing progress in the actions we have taken to mitigate the impacts of these higher costs. Consulting, which makes up well over half of IBM's workforce, is most impacted by the labor cost inflation. We have begun to see improved utilization and priced margin improvements year over year, and our acquisitions have become more accretive, all of which will benefit our margin profile going forward. Our Consulting pre-tax margin of 8.8 percent increased 0.7 points in 2022 versus the prior year and improved 3.2 points in the second half of 2022 compared to the first half reflecting the benefit of these actions. Additionally, across all of our product-based businesses, we have executed price increases above our historical level of increases to be more reflective of the labor and component costs we are incurring due to the inflationary environment. This includes price increases in our maintenance and support agreements for our hardware and software portfolios. Additionally, despite the many global supply chain disruptions throughout 2022, our supply chain has demonstrated resiliency and the ability to proactively respond to potential disruptions in order to meet our clients' needs. The strengthening of the U.S. dollar impacted our reported revenue and gross profit dollars in 2022. We execute hedging programs which defer but do not eliminate the impact of currency. The gains from these hedging programs are reflected primarily in other income and expense. With the rate and magnitude of\n\nAnswer the following question: What is the relationship between Assets, Liabilities, and Equity?\n"], "reference": ["Generally, assets should be equal to liabilities + equity. This is true in the table in 2021 as Liabilities ($105,222) + Equity ($22,021) is equal to Assets ($127,243) and in 2022 where the Liabilities ($113,005) + Equity ($18,996) is equal to Assets ($132,001). \n"]} +{"qna_file": "compositional_skills/extraction/annual_report/reasoning/qna.yaml", "question_id": "322703584480120814589247769797312278466", "category": "taxonomy", "turns": ["Given the context below:\n## 8 Management Discussion\n\n## MANAGEMENT DISCUSSION SNAPSHOT\n\n($ and shares in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022 *\u00a0\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent/Margin Change\u00a0\u00a0|\n|----------------------------------------------------------------------|--------------|---------------|------------------------------------|\n| Revenue\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $6 0 , 5 3 0 | $5 7 , 3 5 0\u00a0| 5.5 %**\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Gross profit margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 54.0 %\u00a0\u00a0\u00a0\u00a0| 54.9 %\u00a0\u00a0\u00a0\u00a0| (0.9)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Total expense and other (income)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $3 1 , 5 3 1 | $2 6 , 4 9\u00a0\u00a0| 18.3 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations before income taxes\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 1 5 6\u00a0\u00a0| $4 , 8 3 7\u00a0\u00a0| (76.1)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Provision for/(benefit from) income taxes from continuing operations | $ (626)\u00a0\u00a0\u00a0| $ 124\u00a0\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 7 8 3\u00a0\u00a0| $4 , 7 1 2\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2.9 %\u00a0\u00a0\u00a0\u00a0| 8.2 %\u00a0\u00a0\u00a0\u00a0\u00a0| (5.3)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ (143) \uf020\u00a0\u00a0| $1 , 0 3\u00a0\u00a0\uf020 | NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Net income\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 6 3 9\u00a0\u00a0| $5 , 7 4 3\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Earnings per share from continuing operations-assuming dilution\u00a0\u00a0\u00a0| $ 1.95\u00a0\u00a0\u00a0\u00a0| $ 5.21\u00a0\u00a0\u00a0\u00a0| (62.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Consolidated earnings per share-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 1.80\u00a0\u00a0\u00a0\u00a0| $ 6.35\u00a0\u00a0\u00a0\u00a0| (71.7)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Weighted-average shares outstanding-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 912.3\u00a0\u00a0\u00a0\u00a0| 904.6\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Assets \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $127,243\u00a0\u00a0\u00a0| $132,001\u00a0\u00a0\u00a0| (3.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Liabilities \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $105,222\u00a0\u00a0\u00a0| $113,005\u00a0\u00a0\u00a0| (6.9)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Equity \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $2 2 , 0 2 1 | $1 8 , 9 6\u00a0\u00a0| 15.9 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\n\uf0c8\uf020 At December 31.\n\nNM-Not meaningful\n\nThe following table provides the company's operating (non-GAAP) earnings for 2022 and 2021. See page 29 for additional information.\n\n($ in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent Change\u00a0\u00a0|\n|--------------------------------------------------------|----------|----------|-----------------------------|\n| Net income as reported\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,639 * | $5,743\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax | (143)\u00a0\u00a0| 1,030\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,783 * | $4,712\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating adjustments (net of tax)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Acquisition-related charges\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1,329\u00a0\u00a0| 1,424\u00a0\u00a0| (6.7)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating retirement-related costs/(income)\u00a0\u00a0\u00a0\u00a0| 4,933 *\u00a0| 1,031\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| U.S. tax reform impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| (70)\u00a0\u00a0\u00a0| 89\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Kyndryl-related impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 351\u00a0\u00a0\u00a0| (81)\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Operating (non-GAAP) earnings\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $8,326\u00a0\u00a0| $7,174\u00a0\u00a0| 16.0 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Diluted operating (non-GAAP) earnings per share\u00a0\u00a0\u00a0\u00a0| $9 . 1 3 | $7 . 9 3 | 15.1 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nNM-Not meaningful\n\n## Macroeconomic Environment\n\nOur business profile positions us well in challenging times. Our diversification across geographies, industries, clients and business mix provides some stability in revenue, profit and cash generation.\n\nThroughout 2022, we experienced escalating labor and component costs and a strong U.S. dollar. While those dynamics have put pressure on our margin profile, we are seeing progress in the actions we have taken to mitigate the impacts of these higher costs. Consulting, which makes up well over half of IBM's workforce, is most impacted by the labor cost inflation. We have begun to see improved utilization and priced margin improvements year over year, and our acquisitions have become more accretive, all of which will benefit our margin profile going forward. Our Consulting pre-tax margin of 8.8 percent increased 0.7 points in 2022 versus the prior year and improved 3.2 points in the second half of 2022 compared to the first half reflecting the benefit of these actions. Additionally, across all of our product-based businesses, we have executed price increases above our historical level of increases to be more reflective of the labor and component costs we are incurring due to the inflationary environment. This includes price increases in our maintenance and support agreements for our hardware and software portfolios. Additionally, despite the many global supply chain disruptions throughout 2022, our supply chain has demonstrated resiliency and the ability to proactively respond to potential disruptions in order to meet our clients' needs. The strengthening of the U.S. dollar impacted our reported revenue and gross profit dollars in 2022. We execute hedging programs which defer but do not eliminate the impact of currency. The gains from these hedging programs are reflected primarily in other income and expense. With the rate and magnitude of\n\nAnswer the following question: Which listed type of income had the greatest percentage margin change between years?"], "reference": ["\"Income from continuing operations before income taxes\" had the greatest difference between 2021 and 2022 with a year-to-year percent/margin change of -76.1%. \n"]} +{"qna_file": "compositional_skills/extraction/annual_report/reasoning/qna.yaml", "question_id": "304975444396586509041052817878438361784", "category": "taxonomy", "turns": ["Given the context below:\n## 8 Management Discussion\n\n## MANAGEMENT DISCUSSION SNAPSHOT\n\n($ and shares in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022 *\u00a0\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent/Margin Change\u00a0\u00a0|\n|----------------------------------------------------------------------|--------------|---------------|------------------------------------|\n| Revenue\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $6 0 , 5 3 0 | $5 7 , 3 5 0\u00a0| 5.5 %**\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Gross profit margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 54.0 %\u00a0\u00a0\u00a0\u00a0| 54.9 %\u00a0\u00a0\u00a0\u00a0| (0.9)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Total expense and other (income)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $3 1 , 5 3 1 | $2 6 , 4 9\u00a0\u00a0| 18.3 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations before income taxes\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 1 5 6\u00a0\u00a0| $4 , 8 3 7\u00a0\u00a0| (76.1)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Provision for/(benefit from) income taxes from continuing operations | $ (626)\u00a0\u00a0\u00a0| $ 124\u00a0\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 7 8 3\u00a0\u00a0| $4 , 7 1 2\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2.9 %\u00a0\u00a0\u00a0\u00a0| 8.2 %\u00a0\u00a0\u00a0\u00a0\u00a0| (5.3)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ (143) \uf020\u00a0\u00a0| $1 , 0 3\u00a0\u00a0\uf020 | NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Net income\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 6 3 9\u00a0\u00a0| $5 , 7 4 3\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Earnings per share from continuing operations-assuming dilution\u00a0\u00a0\u00a0| $ 1.95\u00a0\u00a0\u00a0\u00a0| $ 5.21\u00a0\u00a0\u00a0\u00a0| (62.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Consolidated earnings per share-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 1.80\u00a0\u00a0\u00a0\u00a0| $ 6.35\u00a0\u00a0\u00a0\u00a0| (71.7)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Weighted-average shares outstanding-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 912.3\u00a0\u00a0\u00a0\u00a0| 904.6\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Assets \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $127,243\u00a0\u00a0\u00a0| $132,001\u00a0\u00a0\u00a0| (3.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Liabilities \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $105,222\u00a0\u00a0\u00a0| $113,005\u00a0\u00a0\u00a0| (6.9)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Equity \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $2 2 , 0 2 1 | $1 8 , 9 6\u00a0\u00a0| 15.9 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\n\uf0c8\uf020 At December 31.\n\nNM-Not meaningful\n\nThe following table provides the company's operating (non-GAAP) earnings for 2022 and 2021. See page 29 for additional information.\n\n($ in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent Change\u00a0\u00a0|\n|--------------------------------------------------------|----------|----------|-----------------------------|\n| Net income as reported\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,639 * | $5,743\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax | (143)\u00a0\u00a0| 1,030\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,783 * | $4,712\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating adjustments (net of tax)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Acquisition-related charges\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1,329\u00a0\u00a0| 1,424\u00a0\u00a0| (6.7)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating retirement-related costs/(income)\u00a0\u00a0\u00a0\u00a0| 4,933 *\u00a0| 1,031\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| U.S. tax reform impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| (70)\u00a0\u00a0\u00a0| 89\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Kyndryl-related impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 351\u00a0\u00a0\u00a0| (81)\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Operating (non-GAAP) earnings\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $8,326\u00a0\u00a0| $7,174\u00a0\u00a0| 16.0 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Diluted operating (non-GAAP) earnings per share\u00a0\u00a0\u00a0\u00a0| $9 . 1 3 | $7 . 9 3 | 15.1 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nNM-Not meaningful\n\n## Macroeconomic Environment\n\nOur business profile positions us well in challenging times. Our diversification across geographies, industries, clients and business mix provides some stability in revenue, profit and cash generation.\n\nThroughout 2022, we experienced escalating labor and component costs and a strong U.S. dollar. While those dynamics have put pressure on our margin profile, we are seeing progress in the actions we have taken to mitigate the impacts of these higher costs. Consulting, which makes up well over half of IBM's workforce, is most impacted by the labor cost inflation. We have begun to see improved utilization and priced margin improvements year over year, and our acquisitions have become more accretive, all of which will benefit our margin profile going forward. Our Consulting pre-tax margin of 8.8 percent increased 0.7 points in 2022 versus the prior year and improved 3.2 points in the second half of 2022 compared to the first half reflecting the benefit of these actions. Additionally, across all of our product-based businesses, we have executed price increases above our historical level of increases to be more reflective of the labor and component costs we are incurring due to the inflationary environment. This includes price increases in our maintenance and support agreements for our hardware and software portfolios. Additionally, despite the many global supply chain disruptions throughout 2022, our supply chain has demonstrated resiliency and the ability to proactively respond to potential disruptions in order to meet our clients' needs. The strengthening of the U.S. dollar impacted our reported revenue and gross profit dollars in 2022. We execute hedging programs which defer but do not eliminate the impact of currency. The gains from these hedging programs are reflected primarily in other income and expense. With the rate and magnitude of\n\nAnswer the following question: How is IBM mitigating the impacts of higher labor and component costs?\n"], "reference": ["IBM is taking numerous actions to mitigate high labor and component costs by improving utilization and pricing margins in their Consulting division, making more accretive acquisitions, executing price increases across all of their product-based businesses, and deploying hedging programs to defer the impact of currency. \n"]} +{"qna_file": "compositional_skills/extraction/annual_report/csv/qna.yaml", "question_id": "127296046401623856685375886933152377487", "category": "taxonomy", "turns": ["Given the context below:\n## 8 Management Discussion\n\n## MANAGEMENT DISCUSSION SNAPSHOT\n\n($ and shares in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022 *\u00a0\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent/Margin Change\u00a0\u00a0|\n|----------------------------------------------------------------------|--------------|---------------|------------------------------------|\n| Revenue\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $6 0 , 5 3 0 | $5 7 , 3 5 0\u00a0| 5.5 %**\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Gross profit margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 54.0 %\u00a0\u00a0\u00a0\u00a0| 54.9 %\u00a0\u00a0\u00a0\u00a0| (0.9)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Total expense and other (income)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $3 1 , 5 3 1 | $2 6 , 4 9\u00a0\u00a0| 18.3 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations before income taxes\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 1 5 6\u00a0\u00a0| $4 , 8 3 7\u00a0\u00a0| (76.1)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Provision for/(benefit from) income taxes from continuing operations | $ (626)\u00a0\u00a0\u00a0| $ 124\u00a0\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 7 8 3\u00a0\u00a0| $4 , 7 1 2\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2.9 %\u00a0\u00a0\u00a0\u00a0| 8.2 %\u00a0\u00a0\u00a0\u00a0\u00a0| (5.3)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ (143) \uf020\u00a0\u00a0| $1 , 0 3\u00a0\u00a0\uf020 | NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Net income\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 6 3 9\u00a0\u00a0| $5 , 7 4 3\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Earnings per share from continuing operations-assuming dilution\u00a0\u00a0\u00a0| $ 1.95\u00a0\u00a0\u00a0\u00a0| $ 5.21\u00a0\u00a0\u00a0\u00a0| (62.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Consolidated earnings per share-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 1.80\u00a0\u00a0\u00a0\u00a0| $ 6.35\u00a0\u00a0\u00a0\u00a0| (71.7)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Weighted-average shares outstanding-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 912.3\u00a0\u00a0\u00a0\u00a0| 904.6\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Assets \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $127,243\u00a0\u00a0\u00a0| $132,001\u00a0\u00a0\u00a0| (3.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Liabilities \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $105,222\u00a0\u00a0\u00a0| $113,005\u00a0\u00a0\u00a0| (6.9)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Equity \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $2 2 , 0 2 1 | $1 8 , 9 6\u00a0\u00a0| 15.9 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\n\uf0c8\uf020 At December 31.\n\nNM-Not meaningful\n\nThe following table provides the company's operating (non-GAAP) earnings for 2022 and 2021. See page 29 for additional information.\n\n($ in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent Change\u00a0\u00a0|\n|--------------------------------------------------------|----------|----------|-----------------------------|\n| Net income as reported\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,639 * | $5,743\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax | (143)\u00a0\u00a0| 1,030\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,783 * | $4,712\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating adjustments (net of tax)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Acquisition-related charges\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1,329\u00a0\u00a0| 1,424\u00a0\u00a0| (6.7)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating retirement-related costs/(income)\u00a0\u00a0\u00a0\u00a0| 4,933 *\u00a0| 1,031\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| U.S. tax reform impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| (70)\u00a0\u00a0\u00a0| 89\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Kyndryl-related impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 351\u00a0\u00a0\u00a0| (81)\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Operating (non-GAAP) earnings\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $8,326\u00a0\u00a0| $7,174\u00a0\u00a0| 16.0 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Diluted operating (non-GAAP) earnings per share\u00a0\u00a0\u00a0\u00a0| $9 . 1 3 | $7 . 9 3 | 15.1 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nNM-Not meaningful\n\n## Macroeconomic Environment\n\nOur business profile positions us well in challenging times. Our diversification across geographies, industries, clients and business mix provides some stability in revenue, profit and cash generation.\n\nThroughout 2022, we experienced escalating labor and component costs and a strong U.S. dollar. While those dynamics have put pressure on our margin profile, we are seeing progress in the actions we have taken to mitigate the impacts of these higher costs. Consulting, which makes up well over half of IBM's workforce, is most impacted by the labor cost inflation. We have begun to see improved utilization and priced margin improvements year over year, and our acquisitions have become more accretive, all of which will benefit our margin profile going forward. Our Consulting pre-tax margin of 8.8 percent increased 0.7 points in 2022 versus the prior year and improved 3.2 points in the second half of 2022 compared to the first half reflecting the benefit of these actions. Additionally, across all of our product-based businesses, we have executed price increases above our historical level of increases to be more reflective of the labor and component costs we are incurring due to the inflationary environment. This includes price increases in our maintenance and support agreements for our hardware and software portfolios. Additionally, despite the many global supply chain disruptions throughout 2022, our supply chain has demonstrated resiliency and the ability to proactively respond to potential disruptions in order to meet our clients' needs. The strengthening of the U.S. dollar impacted our reported revenue and gross profit dollars in 2022. We execute hedging programs which defer but do not eliminate the impact of currency. The gains from these hedging programs are reflected primarily in other income and expense. With the rate and magnitude of\n\nAnswer the following question: Give me the table providing the company's operating (non-GAAP) earnings just for 2022 in csv format. \n"], "reference": ["For year ended December 31:,2022\nNet income reported,\"$1,638\\*\"\n\"Income/(loss) from discontinued operations, net of tax\",(143)\nIncoming from continuing operations,\"$1,783\\*\"\nNon-operating adjustments (net of tax),\n\u00a0\u00a0Acquisition-related charges,\"1,329\"\n\u00a0\u00a0\u00a0Non-operating retirement-related costs/(income),\"4,933\\*\"\n\u00a0\u00a0\u00a0U.S. tax reform impacts,(70)\n\u00a0\u00a0\u00a0Kyndryl-related impacts,351\nOperating (non-GAAP) earnings,\"$8,326 \"\nDiluted operating (non-GAAP) earning per share,$9.13\u00a0\n"]} +{"qna_file": "compositional_skills/extraction/annual_report/csv/qna.yaml", "question_id": "77832818533239894740595742162556107668", "category": "taxonomy", "turns": ["Given the context below:\n## 8 Management Discussion\n\n## MANAGEMENT DISCUSSION SNAPSHOT\n\n($ and shares in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022 *\u00a0\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent/Margin Change\u00a0\u00a0|\n|----------------------------------------------------------------------|--------------|---------------|------------------------------------|\n| Revenue\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $6 0 , 5 3 0 | $5 7 , 3 5 0\u00a0| 5.5 %**\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Gross profit margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 54.0 %\u00a0\u00a0\u00a0\u00a0| 54.9 %\u00a0\u00a0\u00a0\u00a0| (0.9)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Total expense and other (income)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $3 1 , 5 3 1 | $2 6 , 4 9\u00a0\u00a0| 18.3 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations before income taxes\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 1 5 6\u00a0\u00a0| $4 , 8 3 7\u00a0\u00a0| (76.1)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Provision for/(benefit from) income taxes from continuing operations | $ (626)\u00a0\u00a0\u00a0| $ 124\u00a0\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 7 8 3\u00a0\u00a0| $4 , 7 1 2\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2.9 %\u00a0\u00a0\u00a0\u00a0| 8.2 %\u00a0\u00a0\u00a0\u00a0\u00a0| (5.3)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ (143) \uf020\u00a0\u00a0| $1 , 0 3\u00a0\u00a0\uf020 | NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Net income\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 6 3 9\u00a0\u00a0| $5 , 7 4 3\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Earnings per share from continuing operations-assuming dilution\u00a0\u00a0\u00a0| $ 1.95\u00a0\u00a0\u00a0\u00a0| $ 5.21\u00a0\u00a0\u00a0\u00a0| (62.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Consolidated earnings per share-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 1.80\u00a0\u00a0\u00a0\u00a0| $ 6.35\u00a0\u00a0\u00a0\u00a0| (71.7)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Weighted-average shares outstanding-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 912.3\u00a0\u00a0\u00a0\u00a0| 904.6\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Assets \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $127,243\u00a0\u00a0\u00a0| $132,001\u00a0\u00a0\u00a0| (3.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Liabilities \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $105,222\u00a0\u00a0\u00a0| $113,005\u00a0\u00a0\u00a0| (6.9)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Equity \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $2 2 , 0 2 1 | $1 8 , 9 6\u00a0\u00a0| 15.9 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\n\uf0c8\uf020 At December 31.\n\nNM-Not meaningful\n\nThe following table provides the company's operating (non-GAAP) earnings for 2022 and 2021. See page 29 for additional information.\n\n($ in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent Change\u00a0\u00a0|\n|--------------------------------------------------------|----------|----------|-----------------------------|\n| Net income as reported\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,639 * | $5,743\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax | (143)\u00a0\u00a0| 1,030\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,783 * | $4,712\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating adjustments (net of tax)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Acquisition-related charges\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1,329\u00a0\u00a0| 1,424\u00a0\u00a0| (6.7)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating retirement-related costs/(income)\u00a0\u00a0\u00a0\u00a0| 4,933 *\u00a0| 1,031\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| U.S. tax reform impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| (70)\u00a0\u00a0\u00a0| 89\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Kyndryl-related impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 351\u00a0\u00a0\u00a0| (81)\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Operating (non-GAAP) earnings\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $8,326\u00a0\u00a0| $7,174\u00a0\u00a0| 16.0 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Diluted operating (non-GAAP) earnings per share\u00a0\u00a0\u00a0\u00a0| $9 . 1 3 | $7 . 9 3 | 15.1 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nNM-Not meaningful\n\n## Macroeconomic Environment\n\nOur business profile positions us well in challenging times. Our diversification across geographies, industries, clients and business mix provides some stability in revenue, profit and cash generation.\n\nThroughout 2022, we experienced escalating labor and component costs and a strong U.S. dollar. While those dynamics have put pressure on our margin profile, we are seeing progress in the actions we have taken to mitigate the impacts of these higher costs. Consulting, which makes up well over half of IBM's workforce, is most impacted by the labor cost inflation. We have begun to see improved utilization and priced margin improvements year over year, and our acquisitions have become more accretive, all of which will benefit our margin profile going forward. Our Consulting pre-tax margin of 8.8 percent increased 0.7 points in 2022 versus the prior year and improved 3.2 points in the second half of 2022 compared to the first half reflecting the benefit of these actions. Additionally, across all of our product-based businesses, we have executed price increases above our historical level of increases to be more reflective of the labor and component costs we are incurring due to the inflationary environment. This includes price increases in our maintenance and support agreements for our hardware and software portfolios. Additionally, despite the many global supply chain disruptions throughout 2022, our supply chain has demonstrated resiliency and the ability to proactively respond to potential disruptions in order to meet our clients' needs. The strengthening of the U.S. dollar impacted our reported revenue and gross profit dollars in 2022. We execute hedging programs which defer but do not eliminate the impact of currency. The gains from these hedging programs are reflected primarily in other income and expense. With the rate and magnitude of\n\nAnswer the following question: Recreate the first table up until \"Net income\" in .csv format. "], "reference": ["For year ended December 31,2022\\*,2021,Yr.-to-Yr. Percent/Margin Change\nRevenue,\"$60,530 \",\"$57,350 \",5.5%\\*\\*\nGross profit margin,54.0%,54.9%,(.9)pts.\nTotal expense and other (income),\"$31,531 \",\" $26,649 \",18.3%\nIncome from continuing operations before income taxes,\" $1,156.00 \",\" $4,837 \",(76.1)%\nProvision for/(benefit from) income taxes from continuing operations , $(626), $124 ,NM\nIncoming from continuing operations,\" $1,783 \",\" $4,712 \",(62.2)%\nIncoming from continuing operations margin,2.9%,8.2%,(5.3)pts.\n\"Income/(loss) from discontinued operations, net of tax\", $(143),\" $1,030 \",NM\nNet income,\" $1,639 \",\" $5,743 \",(71.5)%\n"]} +{"qna_file": "compositional_skills/extraction/annual_report/csv/qna.yaml", "question_id": "259135565206321977954913499959754115269", "category": "taxonomy", "turns": ["Given the context below:\n## 8 Management Discussion\n\n## MANAGEMENT DISCUSSION SNAPSHOT\n\n($ and shares in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022 *\u00a0\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent/Margin Change\u00a0\u00a0|\n|----------------------------------------------------------------------|--------------|---------------|------------------------------------|\n| Revenue\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $6 0 , 5 3 0 | $5 7 , 3 5 0\u00a0| 5.5 %**\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Gross profit margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 54.0 %\u00a0\u00a0\u00a0\u00a0| 54.9 %\u00a0\u00a0\u00a0\u00a0| (0.9)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Total expense and other (income)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $3 1 , 5 3 1 | $2 6 , 4 9\u00a0\u00a0| 18.3 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations before income taxes\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 1 5 6\u00a0\u00a0| $4 , 8 3 7\u00a0\u00a0| (76.1)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Provision for/(benefit from) income taxes from continuing operations | $ (626)\u00a0\u00a0\u00a0| $ 124\u00a0\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 7 8 3\u00a0\u00a0| $4 , 7 1 2\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations margin\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2.9 %\u00a0\u00a0\u00a0\u00a0| 8.2 %\u00a0\u00a0\u00a0\u00a0\u00a0| (5.3)pts.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ (143) \uf020\u00a0\u00a0| $1 , 0 3\u00a0\u00a0\uf020 | NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Net income\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1 , 6 3 9\u00a0\u00a0| $5 , 7 4 3\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Earnings per share from continuing operations-assuming dilution\u00a0\u00a0\u00a0| $ 1.95\u00a0\u00a0\u00a0\u00a0| $ 5.21\u00a0\u00a0\u00a0\u00a0| (62.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Consolidated earnings per share-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 1.80\u00a0\u00a0\u00a0\u00a0| $ 6.35\u00a0\u00a0\u00a0\u00a0| (71.7)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Weighted-average shares outstanding-assuming dilution\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 912.3\u00a0\u00a0\u00a0\u00a0| 904.6\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Assets \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $127,243\u00a0\u00a0\u00a0| $132,001\u00a0\u00a0\u00a0| (3.6)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Liabilities \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $105,222\u00a0\u00a0\u00a0| $113,005\u00a0\u00a0\u00a0| (6.9)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Equity \uf0c9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $2 2 , 0 2 1 | $1 8 , 9 6\u00a0\u00a0| 15.9 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\n\uf0c8\uf020 At December 31.\n\nNM-Not meaningful\n\nThe following table provides the company's operating (non-GAAP) earnings for 2022 and 2021. See page 29 for additional information.\n\n($ in millions except per share amounts)\n\n| For year ended December 31:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 2022\u00a0\u00a0\u00a0| 2021\u00a0\u00a0\u00a0| Yr.-to-Yr. Percent Change\u00a0\u00a0|\n|--------------------------------------------------------|----------|----------|-----------------------------|\n| Net income as reported\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,639 * | $5,743\u00a0\u00a0| (71.5)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income/(loss) from discontinued operations, net of tax | (143)\u00a0\u00a0| 1,030\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Income from continuing operations\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $1,783 * | $4,712\u00a0\u00a0| (62.2)%\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating adjustments (net of tax)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Acquisition-related charges\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1,329\u00a0\u00a0| 1,424\u00a0\u00a0| (6.7)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Non-operating retirement-related costs/(income)\u00a0\u00a0\u00a0\u00a0| 4,933 *\u00a0| 1,031\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| U.S. tax reform impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| (70)\u00a0\u00a0\u00a0| 89\u00a0\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Kyndryl-related impacts\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 351\u00a0\u00a0\u00a0| (81)\u00a0\u00a0\u00a0| NM\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Operating (non-GAAP) earnings\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $8,326\u00a0\u00a0| $7,174\u00a0\u00a0| 16.0 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Diluted operating (non-GAAP) earnings per share\u00a0\u00a0\u00a0\u00a0| $9 . 1 3 | $7 . 9 3 | 15.1 %\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nNM-Not meaningful\n\n## Macroeconomic Environment\n\nOur business profile positions us well in challenging times. Our diversification across geographies, industries, clients and business mix provides some stability in revenue, profit and cash generation.\n\nThroughout 2022, we experienced escalating labor and component costs and a strong U.S. dollar. While those dynamics have put pressure on our margin profile, we are seeing progress in the actions we have taken to mitigate the impacts of these higher costs. Consulting, which makes up well over half of IBM's workforce, is most impacted by the labor cost inflation. We have begun to see improved utilization and priced margin improvements year over year, and our acquisitions have become more accretive, all of which will benefit our margin profile going forward. Our Consulting pre-tax margin of 8.8 percent increased 0.7 points in 2022 versus the prior year and improved 3.2 points in the second half of 2022 compared to the first half reflecting the benefit of these actions. Additionally, across all of our product-based businesses, we have executed price increases above our historical level of increases to be more reflective of the labor and component costs we are incurring due to the inflationary environment. This includes price increases in our maintenance and support agreements for our hardware and software portfolios. Additionally, despite the many global supply chain disruptions throughout 2022, our supply chain has demonstrated resiliency and the ability to proactively respond to potential disruptions in order to meet our clients' needs. The strengthening of the U.S. dollar impacted our reported revenue and gross profit dollars in 2022. We execute hedging programs which defer but do not eliminate the impact of currency. The gains from these hedging programs are reflected primarily in other income and expense. With the rate and magnitude of\n\nAnswer the following question: Show me the Year-to-Year Percent/Margin Change between 2021 and 2022 from the table at the top of the page. Format the results in csv. \n"], "reference": ["For year ended December 31,Yr.-to-Yr. Percent/Margin Change\nRevenue,5.5%\\*\\*\nGross profit margin,(.9)pts.\nTotal expense and other (income),18.3%\nIncome from continuing operations before income taxes,(76.1)%\nProvision for/(benefit from) income taxes from continuing operations ,NM\nIncoming from continuing operations,(62.2)%\nIncoming from continuing operations margin,(5.3)pts.\n\"Income/(loss) from discontinued operations, net of tax\",NM\nNet income,(71.5)%\nEarnings per share from continuing operations-assuming dilution,(62.6)%\nConsolidated earnings per share-assuming dilution,(71.7)%\nWeighted-average shares outstanding-assuming dilution,0.8%\nAssets,(3.6)%\nLiabilities,(6.9)%\nEquity,15.9%\n"]} +{"qna_file": "compositional_skills/extraction/commercial_lease_agreement/bullet_points/qna.yaml", "question_id": "172647536321725590928875580972120400649", "category": "taxonomy", "turns": ["Given the context below:\n## COMMERCIAL LEASE AGREEMENT\n\nTHIS LEASE AGREEMENT is made and entered into on December 1, 2013, by and between Temple CB, LLC, whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Landlord '), and Okra Energy, Inc., whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Tenant ').\n\n## ARTICLE I-GRANT OF LEASE\n\nLandlord, in consideration of the rents to be paid and the covenants and agreements to be performed and observed by the Tenant, does hereby lease to the Tenant and the Tenant does hereby lease and take from the Landlord the property described in Exhibit 'A ' attached hereto and by reference made a part hereof (the 'Leased Premises '), together with, as part of the parcel, all improvements located thereon.\n\n## ARTICLE II-LEASE TERM\n\nSection l. Term of Lease. The term of this Lease shall begin on the Commencement Date, as defined in Section 2 of this Article II, and shall terminate on May 31, 2020 ('the Termination Date '); provided, however, that at the option of Tenant, Tenant may renew this Lease for five additional successive one-year terms at a Monthly Rent of $100,000 per month, provided that notice of such renewal is given in writing no less than 120 days prior to the Termination Date or the expiration of any one-year renewal term. Tenant may at any time cancel this Lease and terminate all of its obligations hereunder by the payment of $300,000, plus all other amounts then due under this Lease.\n\nSection 2. Commencement Date. The 'Commencement Date ' shall mean December 1, 2013.\n\n## ARTICLE III-EXTENSIONS\n\nThe parties hereto may elect to extend this Agreement upon such terms and conditions as may be agreed upon in writing and signed by the parties at the time of any such extension.\n\n## ARTICLE IV-DETERMINATION OF RENT\n\nSection 1. Monthly Rent: The Tenant agrees to pay the Landlord and the Landlord agrees to accept, during the term hereof, at such place as the Landlord shall from time to time direct by notice to the Tenant, monthly rent set forth in the following table:\n\n| Initial Period of December 1, 2013 to May 31, 2014:\u00a0\u00a0| $ 0\u00a0\u00a0\u00a0|\n|-------------------------------------------------------|----------|\n| June 1, 2014 to May 31, 2015:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 30,000 |\n| June 1, 2015 to May 31, 2016:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 40,000 |\n| June 1, 2016 to May 31, 2017:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 50,000 |\n| June 1, 2017 to May 31, 2018:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 60,000 |\n| June 1, 2019 to May 31, 2020:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 70,000 |\n\nAnswer the following question: Please list the parties involved in this lease agreement using bullet lists. Use \"Landlord\" and \"Tenant\" as top-level bullet points and list the entity name and address as sub-bullets.\u00a0\n"], "reference": ["Here is the request bullet list with \"Landlord\" and \"Tenant\" as top-level bullet points:\n- Landlord:\n - Name: Temple CB, LLC\n - Address: 4350 Temple City Boulevard, El Monte, California 91731\n- Tenant:\n - Name: Okra Energy, Inc.\n - Address: 4350 Temple City Boulevard, El Monte, California 91731\n\n"]} +{"qna_file": "compositional_skills/extraction/commercial_lease_agreement/bullet_points/qna.yaml", "question_id": "272058048198044852801506329786654008467", "category": "taxonomy", "turns": ["Given the context below:\n## COMMERCIAL LEASE AGREEMENT\n\nTHIS LEASE AGREEMENT is made and entered into on December 1, 2013, by and between Temple CB, LLC, whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Landlord '), and Okra Energy, Inc., whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Tenant ').\n\n## ARTICLE I-GRANT OF LEASE\n\nLandlord, in consideration of the rents to be paid and the covenants and agreements to be performed and observed by the Tenant, does hereby lease to the Tenant and the Tenant does hereby lease and take from the Landlord the property described in Exhibit 'A ' attached hereto and by reference made a part hereof (the 'Leased Premises '), together with, as part of the parcel, all improvements located thereon.\n\n## ARTICLE II-LEASE TERM\n\nSection l. Term of Lease. The term of this Lease shall begin on the Commencement Date, as defined in Section 2 of this Article II, and shall terminate on May 31, 2020 ('the Termination Date '); provided, however, that at the option of Tenant, Tenant may renew this Lease for five additional successive one-year terms at a Monthly Rent of $100,000 per month, provided that notice of such renewal is given in writing no less than 120 days prior to the Termination Date or the expiration of any one-year renewal term. Tenant may at any time cancel this Lease and terminate all of its obligations hereunder by the payment of $300,000, plus all other amounts then due under this Lease.\n\nSection 2. Commencement Date. The 'Commencement Date ' shall mean December 1, 2013.\n\n## ARTICLE III-EXTENSIONS\n\nThe parties hereto may elect to extend this Agreement upon such terms and conditions as may be agreed upon in writing and signed by the parties at the time of any such extension.\n\n## ARTICLE IV-DETERMINATION OF RENT\n\nSection 1. Monthly Rent: The Tenant agrees to pay the Landlord and the Landlord agrees to accept, during the term hereof, at such place as the Landlord shall from time to time direct by notice to the Tenant, monthly rent set forth in the following table:\n\n| Initial Period of December 1, 2013 to May 31, 2014:\u00a0\u00a0| $ 0\u00a0\u00a0\u00a0|\n|-------------------------------------------------------|----------|\n| June 1, 2014 to May 31, 2015:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 30,000 |\n| June 1, 2015 to May 31, 2016:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 40,000 |\n| June 1, 2016 to May 31, 2017:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 50,000 |\n| June 1, 2017 to May 31, 2018:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 60,000 |\n| June 1, 2019 to May 31, 2020:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 70,000 |\n\nAnswer the following question: List the monthly rent after Jun 2016 into a bulleted list. "], "reference": ["The monthly rent after Jun 2026 is listed below: \n- June 1, 2016 - May 31, 2017: $50,000\n- June 1, 2017 - May 31, 2018: $60,000\n- June 1, 2019 - May 31, 2020: $70,000\n"]} +{"qna_file": "compositional_skills/extraction/commercial_lease_agreement/bullet_points/qna.yaml", "question_id": "64188986438460081856952229763997864829", "category": "taxonomy", "turns": ["Given the context below:\n## COMMERCIAL LEASE AGREEMENT\n\nTHIS LEASE AGREEMENT is made and entered into on December 1, 2013, by and between Temple CB, LLC, whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Landlord '), and Okra Energy, Inc., whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Tenant ').\n\n## ARTICLE I-GRANT OF LEASE\n\nLandlord, in consideration of the rents to be paid and the covenants and agreements to be performed and observed by the Tenant, does hereby lease to the Tenant and the Tenant does hereby lease and take from the Landlord the property described in Exhibit 'A ' attached hereto and by reference made a part hereof (the 'Leased Premises '), together with, as part of the parcel, all improvements located thereon.\n\n## ARTICLE II-LEASE TERM\n\nSection l. Term of Lease. The term of this Lease shall begin on the Commencement Date, as defined in Section 2 of this Article II, and shall terminate on May 31, 2020 ('the Termination Date '); provided, however, that at the option of Tenant, Tenant may renew this Lease for five additional successive one-year terms at a Monthly Rent of $100,000 per month, provided that notice of such renewal is given in writing no less than 120 days prior to the Termination Date or the expiration of any one-year renewal term. Tenant may at any time cancel this Lease and terminate all of its obligations hereunder by the payment of $300,000, plus all other amounts then due under this Lease.\n\nSection 2. Commencement Date. The 'Commencement Date ' shall mean December 1, 2013.\n\n## ARTICLE III-EXTENSIONS\n\nThe parties hereto may elect to extend this Agreement upon such terms and conditions as may be agreed upon in writing and signed by the parties at the time of any such extension.\n\n## ARTICLE IV-DETERMINATION OF RENT\n\nSection 1. Monthly Rent: The Tenant agrees to pay the Landlord and the Landlord agrees to accept, during the term hereof, at such place as the Landlord shall from time to time direct by notice to the Tenant, monthly rent set forth in the following table:\n\n| Initial Period of December 1, 2013 to May 31, 2014:\u00a0\u00a0| $ 0\u00a0\u00a0\u00a0|\n|-------------------------------------------------------|----------|\n| June 1, 2014 to May 31, 2015:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 30,000 |\n| June 1, 2015 to May 31, 2016:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 40,000 |\n| June 1, 2016 to May 31, 2017:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 50,000 |\n| June 1, 2017 to May 31, 2018:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 60,000 |\n| June 1, 2019 to May 31, 2020:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 70,000 |\n\nAnswer the following question: Use a bulleted list to capture the monthly rent in this agreement. \n"], "reference": ["Here is the summary of the monthly rent mentioned in the agreement. \n- The monthly rent for December 1, 2013 - May 31, 2014 is $0.\n- The monthly rent for June 1, 2014 - May 31, 2015 is $30,000.\n- The monthly rent for June 1, 2015 - May 31, 2016 is $40,000.\n- The monthly rent for June 1, 2016 - May 31, 2017 is $50,000.\n- The monthly rent for June 1, 2017 - May 31, 2018 is $60,000.\n- The monthly rent for June 1, 2019 - May 31, 2020 is $70,000.\n"]} +{"qna_file": "compositional_skills/extraction/commercial_lease_agreement/plain_text/qna.yaml", "question_id": "229678340518563780964734295686353661684", "category": "taxonomy", "turns": ["Given the context below:\n## COMMERCIAL LEASE AGREEMENT\n\nTHIS LEASE AGREEMENT is made and entered into on December 1, 2013, by and between Temple CB, LLC, whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Landlord '), and Okra Energy, Inc., whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Tenant ').\n\n## ARTICLE I-GRANT OF LEASE\n\nLandlord, in consideration of the rents to be paid and the covenants and agreements to be performed and observed by the Tenant, does hereby lease to the Tenant and the Tenant does hereby lease and take from the Landlord the property described in Exhibit 'A ' attached hereto and by reference made a part hereof (the 'Leased Premises '), together with, as part of the parcel, all improvements located thereon.\n\n## ARTICLE II-LEASE TERM\n\nSection l. Term of Lease. The term of this Lease shall begin on the Commencement Date, as defined in Section 2 of this Article II, and shall terminate on May 31, 2020 ('the Termination Date '); provided, however, that at the option of Tenant, Tenant may renew this Lease for five additional successive one-year terms at a Monthly Rent of $100,000 per month, provided that notice of such renewal is given in writing no less than 120 days prior to the Termination Date or the expiration of any one-year renewal term. Tenant may at any time cancel this Lease and terminate all of its obligations hereunder by the payment of $300,000, plus all other amounts then due under this Lease.\n\nSection 2. Commencement Date. The 'Commencement Date ' shall mean December 1, 2013.\n\n## ARTICLE III-EXTENSIONS\n\nThe parties hereto may elect to extend this Agreement upon such terms and conditions as may be agreed upon in writing and signed by the parties at the time of any such extension.\n\n## ARTICLE IV-DETERMINATION OF RENT\n\nSection 1. Monthly Rent: The Tenant agrees to pay the Landlord and the Landlord agrees to accept, during the term hereof, at such place as the Landlord shall from time to time direct by notice to the Tenant, monthly rent set forth in the following table:\n\n| Initial Period of December 1, 2013 to May 31, 2014:\u00a0\u00a0| $ 0\u00a0\u00a0\u00a0|\n|-------------------------------------------------------|----------|\n| June 1, 2014 to May 31, 2015:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 30,000 |\n| June 1, 2015 to May 31, 2016:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 40,000 |\n| June 1, 2016 to May 31, 2017:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 50,000 |\n| June 1, 2017 to May 31, 2018:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 60,000 |\n| June 1, 2019 to May 31, 2020:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 70,000 |\n\nAnswer the following question: During which time periods is the monthly rent $50,000? \n"], "reference": ["The time period during which the rent is $50,000 is June 1, 2016 to May 31, 2017. \n"]} +{"qna_file": "compositional_skills/extraction/commercial_lease_agreement/plain_text/qna.yaml", "question_id": "244397845723638727910219602827447539570", "category": "taxonomy", "turns": ["Given the context below:\n## COMMERCIAL LEASE AGREEMENT\n\nTHIS LEASE AGREEMENT is made and entered into on December 1, 2013, by and between Temple CB, LLC, whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Landlord '), and Okra Energy, Inc., whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Tenant ').\n\n## ARTICLE I-GRANT OF LEASE\n\nLandlord, in consideration of the rents to be paid and the covenants and agreements to be performed and observed by the Tenant, does hereby lease to the Tenant and the Tenant does hereby lease and take from the Landlord the property described in Exhibit 'A ' attached hereto and by reference made a part hereof (the 'Leased Premises '), together with, as part of the parcel, all improvements located thereon.\n\n## ARTICLE II-LEASE TERM\n\nSection l. Term of Lease. The term of this Lease shall begin on the Commencement Date, as defined in Section 2 of this Article II, and shall terminate on May 31, 2020 ('the Termination Date '); provided, however, that at the option of Tenant, Tenant may renew this Lease for five additional successive one-year terms at a Monthly Rent of $100,000 per month, provided that notice of such renewal is given in writing no less than 120 days prior to the Termination Date or the expiration of any one-year renewal term. Tenant may at any time cancel this Lease and terminate all of its obligations hereunder by the payment of $300,000, plus all other amounts then due under this Lease.\n\nSection 2. Commencement Date. The 'Commencement Date ' shall mean December 1, 2013.\n\n## ARTICLE III-EXTENSIONS\n\nThe parties hereto may elect to extend this Agreement upon such terms and conditions as may be agreed upon in writing and signed by the parties at the time of any such extension.\n\n## ARTICLE IV-DETERMINATION OF RENT\n\nSection 1. Monthly Rent: The Tenant agrees to pay the Landlord and the Landlord agrees to accept, during the term hereof, at such place as the Landlord shall from time to time direct by notice to the Tenant, monthly rent set forth in the following table:\n\n| Initial Period of December 1, 2013 to May 31, 2014:\u00a0\u00a0| $ 0\u00a0\u00a0\u00a0|\n|-------------------------------------------------------|----------|\n| June 1, 2014 to May 31, 2015:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 30,000 |\n| June 1, 2015 to May 31, 2016:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 40,000 |\n| June 1, 2016 to May 31, 2017:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 50,000 |\n| June 1, 2017 to May 31, 2018:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 60,000 |\n| June 1, 2019 to May 31, 2020:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 70,000 |\n\nAnswer the following question: What organizations are mentioned in this agreement? "], "reference": ["The organizations mentioned in this agreement includes: Temple CB, LLC, and Okra Energy, Inc.\n"]} +{"qna_file": "compositional_skills/extraction/commercial_lease_agreement/plain_text/qna.yaml", "question_id": "314333047281388150938264085360174483524", "category": "taxonomy", "turns": ["Given the context below:\n## COMMERCIAL LEASE AGREEMENT\n\nTHIS LEASE AGREEMENT is made and entered into on December 1, 2013, by and between Temple CB, LLC, whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Landlord '), and Okra Energy, Inc., whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Tenant ').\n\n## ARTICLE I-GRANT OF LEASE\n\nLandlord, in consideration of the rents to be paid and the covenants and agreements to be performed and observed by the Tenant, does hereby lease to the Tenant and the Tenant does hereby lease and take from the Landlord the property described in Exhibit 'A ' attached hereto and by reference made a part hereof (the 'Leased Premises '), together with, as part of the parcel, all improvements located thereon.\n\n## ARTICLE II-LEASE TERM\n\nSection l. Term of Lease. The term of this Lease shall begin on the Commencement Date, as defined in Section 2 of this Article II, and shall terminate on May 31, 2020 ('the Termination Date '); provided, however, that at the option of Tenant, Tenant may renew this Lease for five additional successive one-year terms at a Monthly Rent of $100,000 per month, provided that notice of such renewal is given in writing no less than 120 days prior to the Termination Date or the expiration of any one-year renewal term. Tenant may at any time cancel this Lease and terminate all of its obligations hereunder by the payment of $300,000, plus all other amounts then due under this Lease.\n\nSection 2. Commencement Date. The 'Commencement Date ' shall mean December 1, 2013.\n\n## ARTICLE III-EXTENSIONS\n\nThe parties hereto may elect to extend this Agreement upon such terms and conditions as may be agreed upon in writing and signed by the parties at the time of any such extension.\n\n## ARTICLE IV-DETERMINATION OF RENT\n\nSection 1. Monthly Rent: The Tenant agrees to pay the Landlord and the Landlord agrees to accept, during the term hereof, at such place as the Landlord shall from time to time direct by notice to the Tenant, monthly rent set forth in the following table:\n\n| Initial Period of December 1, 2013 to May 31, 2014:\u00a0\u00a0| $ 0\u00a0\u00a0\u00a0|\n|-------------------------------------------------------|----------|\n| June 1, 2014 to May 31, 2015:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 30,000 |\n| June 1, 2015 to May 31, 2016:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 40,000 |\n| June 1, 2016 to May 31, 2017:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 50,000 |\n| June 1, 2017 to May 31, 2018:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 60,000 |\n| June 1, 2019 to May 31, 2020:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 70,000 |\n\nAnswer the following question: What is the lease beginning and end date? \n"], "reference": ["The lease beginning date is December 1, 2013, and the end date is May 31, 2020. \n"]} +{"qna_file": "compositional_skills/extraction/commercial_lease_agreement/plain_text/qna.yaml", "question_id": "256127947257959728870571268188521900634", "category": "taxonomy", "turns": ["Given the context below:\n## ARTICLE XVI-DEFAULT\n\n## Section 1. Landlord's Remedies. In the event that:\n\na. Tenant shall on three or more occasions be in default in the payment of rent or other charges herein required to be paid by Tenant (default herein being defined as payment received by Landlord ten or more days subsequent to the due date), regardless of whether or not such default has occurred on consecutive or non-consecutive months; or\n\nb. Tenant has caused a lien to be filed against the Landlord's property and said lien is not removed within thirty (30) days of recordation thereof; or\n\nc. Tenant shall default in the observance or performance of any of the covenants and agreements required to be performed and observed by Tenant hereunder for a period of thirty (30) days after notice to Tenant in writing of such default (or if such default shall reasonably take more than thirty (30) days to cure, Tenant shall not have commenced the same within the thirty (30) days and diligently prosecuted the same to completion); or\n\nd. Sixty (60) days have elapsed after the commencement of any proceeding by or against Tenant, whether by the filing of a petition or otherwise, seeking any reorganization, arrangement, composition, readjustment, liquidation, dissolution or similar relief under the present or future Federal Bankruptcy Act or any other present or future applicable federal, state or other statute or law, whereby such proceeding shall not have been dismissed (provided, however, that the nondismissal of any such proceeding shall not be a default hereunder so long as all of Tenant's covenants and obligations hereunder are being performed by or on behalf of Tenant); then Landlord shall be entitled to its election (unless Tenant shall cure such default prior to such election), to exercise concurrently or successively, any one or more of the following rights:\n\nI. Terminate this Lease by giving Tenant notice of termination, in which event this Lease shall expire and terminate on the date specified in such notice of termination, with the same force and effect as though the date so specified were the date herein originally fixed as the termination date of the term of this Lease, and all rights of Tenant under this Lease and in and to the Premises shall expire and terminate, and Tenant shall remain liable for all obligations under this Lease arising up to the date of such termination, and Tenant shall surrender the Premises to Landlord on the date specified in such notice; or\n\nii. Terminate this Lease as provided herein and recover from Tenant all damages Landlord may incur by reason of Tenant's default, including, without limitation, a sum which, at the date of such termination, represents the then value of the excess, if any, of (a) the Minimum Rent, Percentage Rent, Taxes and all other sums which would have been payable hereunder by Tenant for the period commencing with the day following the date of such termination and ending with the date herein before set for the expiration of the full term hereby granted, over (b) the aggregate reasonable rental value of the Premises for the same period, all of which excess sum shall be deemed immediately due and payable; or\n\nAnswer the following question: What is the definitions of late payment from the tenant, before it triggers possible consequences for the tenant? \n"], "reference": ["The late payment is defined as he tenant must be late on rent three times or more and each late payment must be\u00a0ten or more days past the due date.\n\n"]} +{"qna_file": "compositional_skills/extraction/commercial_lease_agreement/plain_text/qna.yaml", "question_id": "168239486965086110769804819249503162907", "category": "taxonomy", "turns": ["Given the context below:\n## ARTICLE XVI-DEFAULT\n\n## Section 1. Landlord's Remedies. In the event that:\n\na. Tenant shall on three or more occasions be in default in the payment of rent or other charges herein required to be paid by Tenant (default herein being defined as payment received by Landlord ten or more days subsequent to the due date), regardless of whether or not such default has occurred on consecutive or non-consecutive months; or\n\nb. Tenant has caused a lien to be filed against the Landlord's property and said lien is not removed within thirty (30) days of recordation thereof; or\n\nc. Tenant shall default in the observance or performance of any of the covenants and agreements required to be performed and observed by Tenant hereunder for a period of thirty (30) days after notice to Tenant in writing of such default (or if such default shall reasonably take more than thirty (30) days to cure, Tenant shall not have commenced the same within the thirty (30) days and diligently prosecuted the same to completion); or\n\nd. Sixty (60) days have elapsed after the commencement of any proceeding by or against Tenant, whether by the filing of a petition or otherwise, seeking any reorganization, arrangement, composition, readjustment, liquidation, dissolution or similar relief under the present or future Federal Bankruptcy Act or any other present or future applicable federal, state or other statute or law, whereby such proceeding shall not have been dismissed (provided, however, that the nondismissal of any such proceeding shall not be a default hereunder so long as all of Tenant's covenants and obligations hereunder are being performed by or on behalf of Tenant); then Landlord shall be entitled to its election (unless Tenant shall cure such default prior to such election), to exercise concurrently or successively, any one or more of the following rights:\n\nI. Terminate this Lease by giving Tenant notice of termination, in which event this Lease shall expire and terminate on the date specified in such notice of termination, with the same force and effect as though the date so specified were the date herein originally fixed as the termination date of the term of this Lease, and all rights of Tenant under this Lease and in and to the Premises shall expire and terminate, and Tenant shall remain liable for all obligations under this Lease arising up to the date of such termination, and Tenant shall surrender the Premises to Landlord on the date specified in such notice; or\n\nii. Terminate this Lease as provided herein and recover from Tenant all damages Landlord may incur by reason of Tenant's default, including, without limitation, a sum which, at the date of such termination, represents the then value of the excess, if any, of (a) the Minimum Rent, Percentage Rent, Taxes and all other sums which would have been payable hereunder by Tenant for the period commencing with the day following the date of such termination and ending with the date herein before set for the expiration of the full term hereby granted, over (b) the aggregate reasonable rental value of the Premises for the same period, all of which excess sum shall be deemed immediately due and payable; or\n\nAnswer the following question: If the tenant fails to perform according to the terms and rules of the leasing agreement, how long do they have to address the issue upon receiving a notice from the landlord? "], "reference": ["30 days. \n"]} +{"qna_file": "compositional_skills/extraction/commercial_lease_agreement/plain_text/qna.yaml", "question_id": "242887182447561321257905522655660584227", "category": "taxonomy", "turns": ["Given the context below:\n## ARTICLE XVI-DEFAULT\n\n## Section 1. Landlord's Remedies. In the event that:\n\na. Tenant shall on three or more occasions be in default in the payment of rent or other charges herein required to be paid by Tenant (default herein being defined as payment received by Landlord ten or more days subsequent to the due date), regardless of whether or not such default has occurred on consecutive or non-consecutive months; or\n\nb. Tenant has caused a lien to be filed against the Landlord's property and said lien is not removed within thirty (30) days of recordation thereof; or\n\nc. Tenant shall default in the observance or performance of any of the covenants and agreements required to be performed and observed by Tenant hereunder for a period of thirty (30) days after notice to Tenant in writing of such default (or if such default shall reasonably take more than thirty (30) days to cure, Tenant shall not have commenced the same within the thirty (30) days and diligently prosecuted the same to completion); or\n\nd. Sixty (60) days have elapsed after the commencement of any proceeding by or against Tenant, whether by the filing of a petition or otherwise, seeking any reorganization, arrangement, composition, readjustment, liquidation, dissolution or similar relief under the present or future Federal Bankruptcy Act or any other present or future applicable federal, state or other statute or law, whereby such proceeding shall not have been dismissed (provided, however, that the nondismissal of any such proceeding shall not be a default hereunder so long as all of Tenant's covenants and obligations hereunder are being performed by or on behalf of Tenant); then Landlord shall be entitled to its election (unless Tenant shall cure such default prior to such election), to exercise concurrently or successively, any one or more of the following rights:\n\nI. Terminate this Lease by giving Tenant notice of termination, in which event this Lease shall expire and terminate on the date specified in such notice of termination, with the same force and effect as though the date so specified were the date herein originally fixed as the termination date of the term of this Lease, and all rights of Tenant under this Lease and in and to the Premises shall expire and terminate, and Tenant shall remain liable for all obligations under this Lease arising up to the date of such termination, and Tenant shall surrender the Premises to Landlord on the date specified in such notice; or\n\nii. Terminate this Lease as provided herein and recover from Tenant all damages Landlord may incur by reason of Tenant's default, including, without limitation, a sum which, at the date of such termination, represents the then value of the excess, if any, of (a) the Minimum Rent, Percentage Rent, Taxes and all other sums which would have been payable hereunder by Tenant for the period commencing with the day following the date of such termination and ending with the date herein before set for the expiration of the full term hereby granted, over (b) the aggregate reasonable rental value of the Premises for the same period, all of which excess sum shall be deemed immediately due and payable; or\n\nAnswer the following question: Who is responsible for paying the rent if the lease is terminated due to the tenant's bankruptcy?\n"], "reference": ["The tenant remains liable for all rent and other obligations under the lease up to the date of termination.\n"]} +{"qna_file": "compositional_skills/extraction/commercial_lease_agreement/markdown/qna.yaml", "question_id": "168988362330738562715271172938631130397", "category": "taxonomy", "turns": ["Given the context below:\n## COMMERCIAL LEASE AGREEMENT\n\nTHIS LEASE AGREEMENT is made and entered into on December 1, 2013, by and between Temple CB, LLC, whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Landlord '), and Okra Energy, Inc., whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Tenant ').\n\n## ARTICLE I-GRANT OF LEASE\n\nLandlord, in consideration of the rents to be paid and the covenants and agreements to be performed and observed by the Tenant, does hereby lease to the Tenant and the Tenant does hereby lease and take from the Landlord the property described in Exhibit 'A ' attached hereto and by reference made a part hereof (the 'Leased Premises '), together with, as part of the parcel, all improvements located thereon.\n\n## ARTICLE II-LEASE TERM\n\nSection l. Term of Lease. The term of this Lease shall begin on the Commencement Date, as defined in Section 2 of this Article II, and shall terminate on May 31, 2020 ('the Termination Date '); provided, however, that at the option of Tenant, Tenant may renew this Lease for five additional successive one-year terms at a Monthly Rent of $100,000 per month, provided that notice of such renewal is given in writing no less than 120 days prior to the Termination Date or the expiration of any one-year renewal term. Tenant may at any time cancel this Lease and terminate all of its obligations hereunder by the payment of $300,000, plus all other amounts then due under this Lease.\n\nSection 2. Commencement Date. The 'Commencement Date ' shall mean December 1, 2013.\n\n## ARTICLE III-EXTENSIONS\n\nThe parties hereto may elect to extend this Agreement upon such terms and conditions as may be agreed upon in writing and signed by the parties at the time of any such extension.\n\n## ARTICLE IV-DETERMINATION OF RENT\n\nSection 1. Monthly Rent: The Tenant agrees to pay the Landlord and the Landlord agrees to accept, during the term hereof, at such place as the Landlord shall from time to time direct by notice to the Tenant, monthly rent set forth in the following table:\n\n| Initial Period of December 1, 2013 to May 31, 2014:\u00a0\u00a0| $ 0\u00a0\u00a0\u00a0|\n|-------------------------------------------------------|----------|\n| June 1, 2014 to May 31, 2015:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 30,000 |\n| June 1, 2015 to May 31, 2016:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 40,000 |\n| June 1, 2016 to May 31, 2017:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 50,000 |\n| June 1, 2017 to May 31, 2018:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 60,000 |\n| June 1, 2019 to May 31, 2020:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 70,000 |\n\nAnswer the following question: Create a markdown table with the following columns: Party, Name, Address. \nPlease add a row for the landlord entity and a row for the tenant. \n"], "reference": ["\\## Parties | Party | Name | Address |\n|---|---|---|\n| Landlord | Temple CB, LLC | 4350 Temple City Boulevard, El Monte, California 91731 |\n| Tenant | Okra Energy, Inc. | 4350 Temple City Boulevard, El Monte, California 91731 |\n"]} +{"qna_file": "compositional_skills/extraction/commercial_lease_agreement/markdown/qna.yaml", "question_id": "73007727424924267800371699049549308841", "category": "taxonomy", "turns": ["Given the context below:\n## COMMERCIAL LEASE AGREEMENT\n\nTHIS LEASE AGREEMENT is made and entered into on December 1, 2013, by and between Temple CB, LLC, whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Landlord '), and Okra Energy, Inc., whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Tenant ').\n\n## ARTICLE I-GRANT OF LEASE\n\nLandlord, in consideration of the rents to be paid and the covenants and agreements to be performed and observed by the Tenant, does hereby lease to the Tenant and the Tenant does hereby lease and take from the Landlord the property described in Exhibit 'A ' attached hereto and by reference made a part hereof (the 'Leased Premises '), together with, as part of the parcel, all improvements located thereon.\n\n## ARTICLE II-LEASE TERM\n\nSection l. Term of Lease. The term of this Lease shall begin on the Commencement Date, as defined in Section 2 of this Article II, and shall terminate on May 31, 2020 ('the Termination Date '); provided, however, that at the option of Tenant, Tenant may renew this Lease for five additional successive one-year terms at a Monthly Rent of $100,000 per month, provided that notice of such renewal is given in writing no less than 120 days prior to the Termination Date or the expiration of any one-year renewal term. Tenant may at any time cancel this Lease and terminate all of its obligations hereunder by the payment of $300,000, plus all other amounts then due under this Lease.\n\nSection 2. Commencement Date. The 'Commencement Date ' shall mean December 1, 2013.\n\n## ARTICLE III-EXTENSIONS\n\nThe parties hereto may elect to extend this Agreement upon such terms and conditions as may be agreed upon in writing and signed by the parties at the time of any such extension.\n\n## ARTICLE IV-DETERMINATION OF RENT\n\nSection 1. Monthly Rent: The Tenant agrees to pay the Landlord and the Landlord agrees to accept, during the term hereof, at such place as the Landlord shall from time to time direct by notice to the Tenant, monthly rent set forth in the following table:\n\n| Initial Period of December 1, 2013 to May 31, 2014:\u00a0\u00a0| $ 0\u00a0\u00a0\u00a0|\n|-------------------------------------------------------|----------|\n| June 1, 2014 to May 31, 2015:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 30,000 |\n| June 1, 2015 to May 31, 2016:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 40,000 |\n| June 1, 2016 to May 31, 2017:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 50,000 |\n| June 1, 2017 to May 31, 2018:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 60,000 |\n| June 1, 2019 to May 31, 2020:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 70,000 |\n\nAnswer the following question: Please display the monthly rent table in markdown format. "], "reference": ["| Time Period | Rent |\n|---|---|\n| December 1, 2013 - May 31, 2014 | $0 |\n| June 1, 2014 - May 31, 2015 | $30,000 |\n| June 1, 2015 - May 31, 2016 | $40,000 |\n| June 1, 2016 - May 31, 2017 | $50,000 |\n| June 1, 2017 - May 31, 2018 | $60,000 |\n| June 1, 2019 - May 31, 2020 | $70,000 |\n\n"]} +{"qna_file": "compositional_skills/extraction/commercial_lease_agreement/markdown/qna.yaml", "question_id": "155488732950655439293375985537221767430", "category": "taxonomy", "turns": ["Given the context below:\n## COMMERCIAL LEASE AGREEMENT\n\nTHIS LEASE AGREEMENT is made and entered into on December 1, 2013, by and between Temple CB, LLC, whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Landlord '), and Okra Energy, Inc., whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Tenant ').\n\n## ARTICLE I-GRANT OF LEASE\n\nLandlord, in consideration of the rents to be paid and the covenants and agreements to be performed and observed by the Tenant, does hereby lease to the Tenant and the Tenant does hereby lease and take from the Landlord the property described in Exhibit 'A ' attached hereto and by reference made a part hereof (the 'Leased Premises '), together with, as part of the parcel, all improvements located thereon.\n\n## ARTICLE II-LEASE TERM\n\nSection l. Term of Lease. The term of this Lease shall begin on the Commencement Date, as defined in Section 2 of this Article II, and shall terminate on May 31, 2020 ('the Termination Date '); provided, however, that at the option of Tenant, Tenant may renew this Lease for five additional successive one-year terms at a Monthly Rent of $100,000 per month, provided that notice of such renewal is given in writing no less than 120 days prior to the Termination Date or the expiration of any one-year renewal term. Tenant may at any time cancel this Lease and terminate all of its obligations hereunder by the payment of $300,000, plus all other amounts then due under this Lease.\n\nSection 2. Commencement Date. The 'Commencement Date ' shall mean December 1, 2013.\n\n## ARTICLE III-EXTENSIONS\n\nThe parties hereto may elect to extend this Agreement upon such terms and conditions as may be agreed upon in writing and signed by the parties at the time of any such extension.\n\n## ARTICLE IV-DETERMINATION OF RENT\n\nSection 1. Monthly Rent: The Tenant agrees to pay the Landlord and the Landlord agrees to accept, during the term hereof, at such place as the Landlord shall from time to time direct by notice to the Tenant, monthly rent set forth in the following table:\n\n| Initial Period of December 1, 2013 to May 31, 2014:\u00a0\u00a0| $ 0\u00a0\u00a0\u00a0|\n|-------------------------------------------------------|----------|\n| June 1, 2014 to May 31, 2015:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 30,000 |\n| June 1, 2015 to May 31, 2016:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 40,000 |\n| June 1, 2016 to May 31, 2017:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 50,000 |\n| June 1, 2017 to May 31, 2018:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 60,000 |\n| June 1, 2019 to May 31, 2020:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 70,000 |\n\nAnswer the following question: Please list the monthly rent after June 1, 2016 in a markdown table, with one column on \"Period\" and another column on \"Monthly rent quantity\". \n"], "reference": ["| Period | Monthly rent quantity |\n|---|---|\n| June 1, 2016 - May 31, 2017 | $50,000 |\n| June 1, 2017 - May 31, 2018 | $60,000 |\n| June 1, 2019 - May 31, 2020 | $70,000 |\n"]} +{"qna_file": "compositional_skills/extraction/commercial_lease_agreement/reasoning/qna.yaml", "question_id": "175745130039202703674828641529878645579", "category": "taxonomy", "turns": ["Given the context below:\n## COMMERCIAL LEASE AGREEMENT\n\nTHIS LEASE AGREEMENT is made and entered into on December 1, 2013, by and between Temple CB, LLC, whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Landlord '), and Okra Energy, Inc., whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Tenant ').\n\n## ARTICLE I-GRANT OF LEASE\n\nLandlord, in consideration of the rents to be paid and the covenants and agreements to be performed and observed by the Tenant, does hereby lease to the Tenant and the Tenant does hereby lease and take from the Landlord the property described in Exhibit 'A ' attached hereto and by reference made a part hereof (the 'Leased Premises '), together with, as part of the parcel, all improvements located thereon.\n\n## ARTICLE II-LEASE TERM\n\nSection l. Term of Lease. The term of this Lease shall begin on the Commencement Date, as defined in Section 2 of this Article II, and shall terminate on May 31, 2020 ('the Termination Date '); provided, however, that at the option of Tenant, Tenant may renew this Lease for five additional successive one-year terms at a Monthly Rent of $100,000 per month, provided that notice of such renewal is given in writing no less than 120 days prior to the Termination Date or the expiration of any one-year renewal term. Tenant may at any time cancel this Lease and terminate all of its obligations hereunder by the payment of $300,000, plus all other amounts then due under this Lease.\n\nSection 2. Commencement Date. The 'Commencement Date ' shall mean December 1, 2013.\n\n## ARTICLE III-EXTENSIONS\n\nThe parties hereto may elect to extend this Agreement upon such terms and conditions as may be agreed upon in writing and signed by the parties at the time of any such extension.\n\n## ARTICLE IV-DETERMINATION OF RENT\n\nSection 1. Monthly Rent: The Tenant agrees to pay the Landlord and the Landlord agrees to accept, during the term hereof, at such place as the Landlord shall from time to time direct by notice to the Tenant, monthly rent set forth in the following table:\n\n| Initial Period of December 1, 2013 to May 31, 2014:\u00a0\u00a0| $ 0\u00a0\u00a0\u00a0|\n|-------------------------------------------------------|----------|\n| June 1, 2014 to May 31, 2015:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 30,000 |\n| June 1, 2015 to May 31, 2016:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 40,000 |\n| June 1, 2016 to May 31, 2017:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 50,000 |\n| June 1, 2017 to May 31, 2018:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 60,000 |\n| June 1, 2019 to May 31, 2020:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 70,000 |\n\nAnswer the following question: If the tenant has a budget of one million dollars after the lease terminates, how many months can the tenant continue the lease? \n"], "reference": ["Given that the monthly rent is $100,000 after the termination date, with the budget of one 1 million, the lease can last another 10 months. \n"]} +{"qna_file": "compositional_skills/extraction/commercial_lease_agreement/reasoning/qna.yaml", "question_id": "82616909378625224529025121839885550602", "category": "taxonomy", "turns": ["Given the context below:\n## COMMERCIAL LEASE AGREEMENT\n\nTHIS LEASE AGREEMENT is made and entered into on December 1, 2013, by and between Temple CB, LLC, whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Landlord '), and Okra Energy, Inc., whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Tenant ').\n\n## ARTICLE I-GRANT OF LEASE\n\nLandlord, in consideration of the rents to be paid and the covenants and agreements to be performed and observed by the Tenant, does hereby lease to the Tenant and the Tenant does hereby lease and take from the Landlord the property described in Exhibit 'A ' attached hereto and by reference made a part hereof (the 'Leased Premises '), together with, as part of the parcel, all improvements located thereon.\n\n## ARTICLE II-LEASE TERM\n\nSection l. Term of Lease. The term of this Lease shall begin on the Commencement Date, as defined in Section 2 of this Article II, and shall terminate on May 31, 2020 ('the Termination Date '); provided, however, that at the option of Tenant, Tenant may renew this Lease for five additional successive one-year terms at a Monthly Rent of $100,000 per month, provided that notice of such renewal is given in writing no less than 120 days prior to the Termination Date or the expiration of any one-year renewal term. Tenant may at any time cancel this Lease and terminate all of its obligations hereunder by the payment of $300,000, plus all other amounts then due under this Lease.\n\nSection 2. Commencement Date. The 'Commencement Date ' shall mean December 1, 2013.\n\n## ARTICLE III-EXTENSIONS\n\nThe parties hereto may elect to extend this Agreement upon such terms and conditions as may be agreed upon in writing and signed by the parties at the time of any such extension.\n\n## ARTICLE IV-DETERMINATION OF RENT\n\nSection 1. Monthly Rent: The Tenant agrees to pay the Landlord and the Landlord agrees to accept, during the term hereof, at such place as the Landlord shall from time to time direct by notice to the Tenant, monthly rent set forth in the following table:\n\n| Initial Period of December 1, 2013 to May 31, 2014:\u00a0\u00a0| $ 0\u00a0\u00a0\u00a0|\n|-------------------------------------------------------|----------|\n| June 1, 2014 to May 31, 2015:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 30,000 |\n| June 1, 2015 to May 31, 2016:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 40,000 |\n| June 1, 2016 to May 31, 2017:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 50,000 |\n| June 1, 2017 to May 31, 2018:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 60,000 |\n| June 1, 2019 to May 31, 2020:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 70,000 |\n\nAnswer the following question: In total, how many months have a month rate of less than $50,000? "], "reference": ["Based on the table provided in the document, there are three time periods where the rate is less than $50,000:\n- December 1, 2013 - May 31, 2014: $0 (6 months)\n- June 1, 2014 - May 31, 2015: $30,000 (12 months)\n- June 1, 2015 - May 31, 2016: $40,000 (12 months)\nSo in total, there are 30 months where the rent is less than $50,000. \n\n"]} +{"qna_file": "compositional_skills/extraction/commercial_lease_agreement/reasoning/qna.yaml", "question_id": "313828062606420990234680697555275955885", "category": "taxonomy", "turns": ["Given the context below:\n## COMMERCIAL LEASE AGREEMENT\n\nTHIS LEASE AGREEMENT is made and entered into on December 1, 2013, by and between Temple CB, LLC, whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Landlord '), and Okra Energy, Inc., whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Tenant ').\n\n## ARTICLE I-GRANT OF LEASE\n\nLandlord, in consideration of the rents to be paid and the covenants and agreements to be performed and observed by the Tenant, does hereby lease to the Tenant and the Tenant does hereby lease and take from the Landlord the property described in Exhibit 'A ' attached hereto and by reference made a part hereof (the 'Leased Premises '), together with, as part of the parcel, all improvements located thereon.\n\n## ARTICLE II-LEASE TERM\n\nSection l. Term of Lease. The term of this Lease shall begin on the Commencement Date, as defined in Section 2 of this Article II, and shall terminate on May 31, 2020 ('the Termination Date '); provided, however, that at the option of Tenant, Tenant may renew this Lease for five additional successive one-year terms at a Monthly Rent of $100,000 per month, provided that notice of such renewal is given in writing no less than 120 days prior to the Termination Date or the expiration of any one-year renewal term. Tenant may at any time cancel this Lease and terminate all of its obligations hereunder by the payment of $300,000, plus all other amounts then due under this Lease.\n\nSection 2. Commencement Date. The 'Commencement Date ' shall mean December 1, 2013.\n\n## ARTICLE III-EXTENSIONS\n\nThe parties hereto may elect to extend this Agreement upon such terms and conditions as may be agreed upon in writing and signed by the parties at the time of any such extension.\n\n## ARTICLE IV-DETERMINATION OF RENT\n\nSection 1. Monthly Rent: The Tenant agrees to pay the Landlord and the Landlord agrees to accept, during the term hereof, at such place as the Landlord shall from time to time direct by notice to the Tenant, monthly rent set forth in the following table:\n\n| Initial Period of December 1, 2013 to May 31, 2014:\u00a0\u00a0| $ 0\u00a0\u00a0\u00a0|\n|-------------------------------------------------------|----------|\n| June 1, 2014 to May 31, 2015:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 30,000 |\n| June 1, 2015 to May 31, 2016:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 40,000 |\n| June 1, 2016 to May 31, 2017:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 50,000 |\n| June 1, 2017 to May 31, 2018:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 60,000 |\n| June 1, 2019 to May 31, 2020:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 70,000 |\n\nAnswer the following question: When is the steepest price increase in monthly rent? \n"], "reference": ["Between May 31, 2014 and June 1, 2014, there is a price increase of $30,000, which is the steepest price change of all time. \n"]} +{"qna_file": "compositional_skills/extraction/commercial_lease_agreement/csv/qna.yaml", "question_id": "179146985562153323128331754126690542800", "category": "taxonomy", "turns": ["Given the context below:\n## COMMERCIAL LEASE AGREEMENT\n\nTHIS LEASE AGREEMENT is made and entered into on December 1, 2013, by and between Temple CB, LLC, whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Landlord '), and Okra Energy, Inc., whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Tenant ').\n\n## ARTICLE I-GRANT OF LEASE\n\nLandlord, in consideration of the rents to be paid and the covenants and agreements to be performed and observed by the Tenant, does hereby lease to the Tenant and the Tenant does hereby lease and take from the Landlord the property described in Exhibit 'A ' attached hereto and by reference made a part hereof (the 'Leased Premises '), together with, as part of the parcel, all improvements located thereon.\n\n## ARTICLE II-LEASE TERM\n\nSection l. Term of Lease. The term of this Lease shall begin on the Commencement Date, as defined in Section 2 of this Article II, and shall terminate on May 31, 2020 ('the Termination Date '); provided, however, that at the option of Tenant, Tenant may renew this Lease for five additional successive one-year terms at a Monthly Rent of $100,000 per month, provided that notice of such renewal is given in writing no less than 120 days prior to the Termination Date or the expiration of any one-year renewal term. Tenant may at any time cancel this Lease and terminate all of its obligations hereunder by the payment of $300,000, plus all other amounts then due under this Lease.\n\nSection 2. Commencement Date. The 'Commencement Date ' shall mean December 1, 2013.\n\n## ARTICLE III-EXTENSIONS\n\nThe parties hereto may elect to extend this Agreement upon such terms and conditions as may be agreed upon in writing and signed by the parties at the time of any such extension.\n\n## ARTICLE IV-DETERMINATION OF RENT\n\nSection 1. Monthly Rent: The Tenant agrees to pay the Landlord and the Landlord agrees to accept, during the term hereof, at such place as the Landlord shall from time to time direct by notice to the Tenant, monthly rent set forth in the following table:\n\n| Initial Period of December 1, 2013 to May 31, 2014:\u00a0\u00a0| $ 0\u00a0\u00a0\u00a0|\n|-------------------------------------------------------|----------|\n| June 1, 2014 to May 31, 2015:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 30,000 |\n| June 1, 2015 to May 31, 2016:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 40,000 |\n| June 1, 2016 to May 31, 2017:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 50,000 |\n| June 1, 2017 to May 31, 2018:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 60,000 |\n| June 1, 2019 to May 31, 2020:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 70,000 |\n\nAnswer the following question: List the monthly rent after June 1, 2016 in CSV format. \n"], "reference": ["Time period, Rent\n\"June 1, 2016 to May 31, 2017:\" $50000\n\"June 1, 2017 to May 31, 2018:\" $60000\n\"June 1, 2019 to May 31, 2020:\" $70000\n\n"]} +{"qna_file": "compositional_skills/extraction/commercial_lease_agreement/csv/qna.yaml", "question_id": "7883453001091666598159072997627818833", "category": "taxonomy", "turns": ["Given the context below:\n## COMMERCIAL LEASE AGREEMENT\n\nTHIS LEASE AGREEMENT is made and entered into on December 1, 2013, by and between Temple CB, LLC, whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Landlord '), and Okra Energy, Inc., whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Tenant ').\n\n## ARTICLE I-GRANT OF LEASE\n\nLandlord, in consideration of the rents to be paid and the covenants and agreements to be performed and observed by the Tenant, does hereby lease to the Tenant and the Tenant does hereby lease and take from the Landlord the property described in Exhibit 'A ' attached hereto and by reference made a part hereof (the 'Leased Premises '), together with, as part of the parcel, all improvements located thereon.\n\n## ARTICLE II-LEASE TERM\n\nSection l. Term of Lease. The term of this Lease shall begin on the Commencement Date, as defined in Section 2 of this Article II, and shall terminate on May 31, 2020 ('the Termination Date '); provided, however, that at the option of Tenant, Tenant may renew this Lease for five additional successive one-year terms at a Monthly Rent of $100,000 per month, provided that notice of such renewal is given in writing no less than 120 days prior to the Termination Date or the expiration of any one-year renewal term. Tenant may at any time cancel this Lease and terminate all of its obligations hereunder by the payment of $300,000, plus all other amounts then due under this Lease.\n\nSection 2. Commencement Date. The 'Commencement Date ' shall mean December 1, 2013.\n\n## ARTICLE III-EXTENSIONS\n\nThe parties hereto may elect to extend this Agreement upon such terms and conditions as may be agreed upon in writing and signed by the parties at the time of any such extension.\n\n## ARTICLE IV-DETERMINATION OF RENT\n\nSection 1. Monthly Rent: The Tenant agrees to pay the Landlord and the Landlord agrees to accept, during the term hereof, at such place as the Landlord shall from time to time direct by notice to the Tenant, monthly rent set forth in the following table:\n\n| Initial Period of December 1, 2013 to May 31, 2014:\u00a0\u00a0| $ 0\u00a0\u00a0\u00a0|\n|-------------------------------------------------------|----------|\n| June 1, 2014 to May 31, 2015:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 30,000 |\n| June 1, 2015 to May 31, 2016:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 40,000 |\n| June 1, 2016 to May 31, 2017:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 50,000 |\n| June 1, 2017 to May 31, 2018:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 60,000 |\n| June 1, 2019 to May 31, 2020:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 70,000 |\n\nAnswer the following question: Create a table, in csv format, that captures who is the Landlord in this agreement, who is the Tenant in this agreement, and what are the lease start and termination date? "], "reference": ["Landlord, Temple CB LLC\nTenant, Okra Energy Inc.\nStart date, December 1, 2013\nEnd data, May 31, 2020\n"]} +{"qna_file": "compositional_skills/extraction/commercial_lease_agreement/csv/qna.yaml", "question_id": "88406853982717047504862763416357608522", "category": "taxonomy", "turns": ["Given the context below:\n## COMMERCIAL LEASE AGREEMENT\n\nTHIS LEASE AGREEMENT is made and entered into on December 1, 2013, by and between Temple CB, LLC, whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Landlord '), and Okra Energy, Inc., whose address is 4350 Temple City Boulevard, El Monte, California 91731 (hereinafter referred to as 'Tenant ').\n\n## ARTICLE I-GRANT OF LEASE\n\nLandlord, in consideration of the rents to be paid and the covenants and agreements to be performed and observed by the Tenant, does hereby lease to the Tenant and the Tenant does hereby lease and take from the Landlord the property described in Exhibit 'A ' attached hereto and by reference made a part hereof (the 'Leased Premises '), together with, as part of the parcel, all improvements located thereon.\n\n## ARTICLE II-LEASE TERM\n\nSection l. Term of Lease. The term of this Lease shall begin on the Commencement Date, as defined in Section 2 of this Article II, and shall terminate on May 31, 2020 ('the Termination Date '); provided, however, that at the option of Tenant, Tenant may renew this Lease for five additional successive one-year terms at a Monthly Rent of $100,000 per month, provided that notice of such renewal is given in writing no less than 120 days prior to the Termination Date or the expiration of any one-year renewal term. Tenant may at any time cancel this Lease and terminate all of its obligations hereunder by the payment of $300,000, plus all other amounts then due under this Lease.\n\nSection 2. Commencement Date. The 'Commencement Date ' shall mean December 1, 2013.\n\n## ARTICLE III-EXTENSIONS\n\nThe parties hereto may elect to extend this Agreement upon such terms and conditions as may be agreed upon in writing and signed by the parties at the time of any such extension.\n\n## ARTICLE IV-DETERMINATION OF RENT\n\nSection 1. Monthly Rent: The Tenant agrees to pay the Landlord and the Landlord agrees to accept, during the term hereof, at such place as the Landlord shall from time to time direct by notice to the Tenant, monthly rent set forth in the following table:\n\n| Initial Period of December 1, 2013 to May 31, 2014:\u00a0\u00a0| $ 0\u00a0\u00a0\u00a0|\n|-------------------------------------------------------|----------|\n| June 1, 2014 to May 31, 2015:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 30,000 |\n| June 1, 2015 to May 31, 2016:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 40,000 |\n| June 1, 2016 to May 31, 2017:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 50,000 |\n| June 1, 2017 to May 31, 2018:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 60,000 |\n| June 1, 2019 to May 31, 2020:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $ 70,000 |\n\nAnswer the following question: Take the table on monthly rent and convert it into a .csv table. \n"], "reference": ["Time period, Rent\n\"December 1, 2013 to May 31, 2014:\", $0\n\"June 1, 2014 to May 31, 2015:\", $30000\n\"June 1, 2015 to May 31, 2016:\" $40000\n\"June 1, 2016 to May 31, 2017:\" $50000\n\"June 1, 2017 to May 31, 2018:\" $60000\n\"June 1, 2019 to May 31, 2020:\" $70000\n"]} +{"qna_file": "compositional_skills/extraction/information/named_entities/dates_and_events/qna.yaml", "question_id": "46570474197018698211014135771568173246", "category": "taxonomy", "turns": ["Given the context below:\nOlav Aaraas( born 10 July 1950) is a Norwegian historian and museum director.\\n\\n He was born in Fredrikstad.\\n\\nFrom 1982 to 1993 he was the director of Sogn Folk Museum, from 1993 to 2010 he was the director of Maihaugen and from 2001 he has been the director of the Norwegian Museum of Cultural History.\\n\\nIn 2010 he was decorated with the Royal Norwegian Order of St. Olav.\\nWar Dogs is a 1942 American film directed by S. Roy Luby.\\nIan Barry is an Australian director of film and TV.\\nPeter Levin is an American director of film, television and theatre.\\nWar Dogs is 1943 American one-reel World War II animated cartoon directed by William Hanna and Joseph Barbera.\\n\\nReleased with the film \"Best Foot Forward\" by Metro-Goldwyn-Mayer, its main theme is war dogs training that is shown with one yellow dim-witted dog.\\nV. Uvarov was a Soviet set decorator.\\n\\nHe was nominated for an Academy Award for Best Art Direction for his work in the epic film \"War and Peace\" (1967).\\nJesse Edward Hobson( May 2, 1911 \u2013 November 5, 1970) was the director of SRI International from 1947 to 1955.\\n\\nPrior to SRI, he was the director of the Armour Research Foundation.\\nLee Sandales is an art director.\\n\\nOn January 24, 2012, he was nominated for an Academy Award for his work on the 2011 film \"War Horse\".\\nJoseph Roland Barbera (March 24, 1911 \u2013 December 18, 2006) was an American animator, director, producer, storyboard artist, and cartoon artist, whose film and television cartoon characters entertained millions of fans worldwide for much of the 20th century.\\n\\nHe was born to Italian immigrants in New York City, where he lived, attended college and began his career through his young adult years.\\n\\nAfter working odd jobs and as a banker, Barbera joined Van Beuren Studios in 1927 and subsequently Terrytoons in 1929.\\n\\nIn 1930, he moved to California and while working at Metro-Goldwyn-Mayer (MGM), Barbera met William Hanna.\\n\\nThe two men began a collaboration that was at first best known for producing \"Tom and Jerry\".\\n\\nIn 1950, after MGM dissolved their animation department, they co-founded Hanna-Barbera, which became the most successful television animation studio in the business, producing programs such as \"The FlintstonesYogi BearScooby-DooTop CatThe SmurfsHuckleberry Hound\" and \"The Jetsons\".\\n\\nIn 1967, Hanna-Barbera was sold to Taft Broadcasting for $12 million, but Hanna and Barbera remained heads of the company.\\n\\nIn 1991, the studio was sold to Turner Broadcasting System, which in turn was merged with Time Warner, owners of Warner Bros., in 1996; Hanna and Barbera stayed on as advisors.\\n\\nHanna and Barbera won seven Academy Awards and eight Emmy Awards.\\n\\nTheir cartoon shows have become cultural icons, and their cartoon characters have appeared in other media such as films, books, and toys.\\n\\nHanna-Barbera's shows had a worldwide audience of over 300 million people in the 1960s and have been translated into more than 28 languages.\\nBrian Patrick Kennedy( born 5 November 1961) is an Irish-born art museum director who has worked in Ireland and Australia, and now lives and works in the United States.\\n\\nHe is currently the director of the Peabody Essex Museum.\\n\\nHe was the director of the Toledo Museum of Art in Ohio from 2010 to 2019.\\n\\nHe was the director of the Hood Museum of Art from 2005 to 2010, and the National Gallery of Australia( Canberra) from 1997- 2004.\n\nAnswer the following question: Extract the dates and events mentioned in the given text."], "reference": ["Here is a list of dates and events from the provided text:\n* 1950: Olav Aaraas was born in Fredrikstad, Norway. * 1982-1993: Olav Aaraas served as the director of Sogn Folk Museum in Norway. * 1993-2010: Olav Aaraas served as the director of Maihaugen in Norway. * 2001-present: Olav Aaraas has been the director of the Norwegian Museum of Cultural History. * 2010: Olav Aaraas was decorated with the Royal Norwegian Order of St. Olav. * 1942: The American film \"War Dogs\" was directed by S. Roy Luby. * 1943: The American one-reel World War II animated cartoon \"War Dogs\" was directed by William Hanna and Joseph Barbera. * 1967: The epic film \"War and Peace\" was released, for which V. Uvarov was nominated for an Academy Award for Best Art Direction. * 1947-1955: Jesse Edward Hobson served as the director of SRI International. * January 24, 2012: Lee Sandales was nominated for an Academy Award for his work on the film \"War Horse\". * March 24, 1911 - December 18, 2006: Joseph Roland Barbera, co-founder of Hanna-Barbera, was born and died. * 1927: Joseph Roland Barbera joined Van Beuren Studios. * 1929: Joseph Roland Barbera joined Terrytoons. * 1930: Joseph Roland Barbera moved to California and began working at Metro-Goldwyn-Mayer (MGM). * 1950: Joseph Roland Barbera co-founded Hanna-Barbera with William Hanna. * 1950: After MGM dissolved their animation department, Hanna-Barbera became the most successful television animation studio in the business. * 1967: Hanna-Barbera was sold to Taft Broadcasting for $12 million. * 1991: The studio was sold to Turner Broadcasting System. * 1996: Turner Broadcasting System was merged with Time Warner, owners of Warner Bros. * Hanna-Barbera won seven Academy Awards and eight Emmy Awards. * Brian Patrick Kennedy was born on November 5, 1961. * 2010-2019: Brian Patrick Kennedy served as the director of the Toledo Museum of Art in Ohio. * 2005-2010: Brian Patrick Kennedy served as the director of the Hood Museum of Art. * 1997-2004: Brian Patrick Kennedy served as the director of the National Gallery of Australia in Canberra. * Present: Brian Patrick Kennedy is the director of the Peabody Essex Museum.\n"]} +{"qna_file": "compositional_skills/extraction/information/named_entities/places/qna.yaml", "question_id": "339895209861053340017988974097778497225", "category": "taxonomy", "turns": ["Given the context below:\n'Brian Patrick Kennedy( born 5 November 1961) is an Irish- born art museum director who has worked in Ireland and Australia, and now lives and works in the United States.\\n\\nHe is currently the director of the Peabody Essex Museum.\\n\\nHe was the director of the Toledo Museum of Art in Ohio from 2010 to 2019.\\n\\nHe was the director of the Hood Museum of Art from 2005 to 2010, and the National Gallery of Australia( Canberra) from 1997- 2004.\\nIan Barry is an Australian director of film and TV.\\nSaltwater is a 2000 Irish drama film written and directed by Conor McPherson.\\n\\nThe film stars Peter McDonald, Brian Cox, Conor Mullen, Laurence Kinlan, Brendan Gleeson and Eva Birthistle.\\n\\nThe film was released on September 29, 2000, by Buena Vista International.\\nNow Where Did the 7th Company Get To? is a 1973 French- Italian comedy war film directed by Robert Lamoureux.\\n\\nThe film portrays the adventures of a French Army squad lost somewhere on the front in May 1940 during the Battle of France.\\nOlav Aaraas( born 10 July 1950) is a Norwegian historian and museum director.\\n\\nHe was born in Fredrikstad.\\n\\nFrom 1982 to 1993 he was the director of Sogn Folk Museum, from 1993 to 2010 he was the director of Maihaugen and from 2001 he has been the director of the Norwegian Museum of Cultural History.\\n\\nIn 2010 he was decorated with the Royal Norwegian Order of St. Olav.\\nJason Moore( born October 22, 1970) is an American director of film, theatre and television.\\nThe Seventh Company Outdoors is a 1977 French comedy film directed by Robert Lamoureux.\\n\\nIt is a sequel to Now Where Did the 7th Company Get to ?.\\nConor McPherson (born 6 August 1971) is an Irish playwright, screenwriter and director of stage and film.\\n\\nIn recognition of his contribution to world theatre, McPherson was awarded a doctorate of Literature, Honoris Causa, in June 2013 by the University College Dublin.\\nPeter Levin is an American director of film, television and theatre.\\nDana Blankstein- Cohen( born March 3, 1981) is the director of the Israeli Academy of Film and Television.\\n\\nShe is a film director, and an Israeli culture entrepreneur.'\n\nAnswer the following question: Extract all the names of places mentioned in the given text."], "reference": ["'1. Ireland\\n2. Australia\\n3. United States\\n4. Peabody Essex Museum (in the United States)\\n5. Toledo Museum of Art in Ohio\\n6. Hood Museum of Art\\n7. National Gallery of Australia in Canberra\\n8. Fredrikstad, Norway\\n9. Sogn Folk Museum\\n10. Maihaugen\\n11. Norwegian Museum of Cultural History\\n12. Battle of France (in May 1940)\\n13. French Army\\n14. University College Dublin\\n15. Israel\\n16. Israeli Academy of Film and Television'\n"]} +{"qna_file": "compositional_skills/extraction/information/named_entities/person_names/qna.yaml", "question_id": "263331233586072114194311496404253505185", "category": "taxonomy", "turns": ["Given the context below:\n\"Niels Hansen (7 November 1924 \u2013 4 January 2015) was the German ambassador to Israel and the author of \\\"Out of the Shadows of the Catastrophe\\\".\\\\nHubert August Piel (12 July 1892, in D\\xFCsseldorf \u2013 27 March 1963, in Munich), known as Harry Piel, was a prolific German actor, film director, screenwriter and film producer who was involved in over 150 films.\\\\n\\\\nPiel became a director in 1912, turning out such box-office successes as \\\"Mann Gegen Mann\\\" (1928), \\\"Achtung! - Auto-Diebe!\\\"\\\\n\\\\n(1930) and \\\"Artisten\\\" (1935).\\\\n\\\\nHis last directorial effort was 1953's \\\"Gesprengte Gitter (Elephant Fury)\\\", a.k.a. \\\"Panic\\\", which he also produced, wrote, and starred.\\\\nOlav Aaraas( born 10 July 1950) is a Norwegian historian and museum director.\\\\n\\\\nHe was born in Fredrikstad.\\\\n\\\\nFrom 1982 to 1993 he was the director of Sogn Folk Museum, from 1993 to 2010 he was the director of Maihaugen and from 2001 he has been the director of the Norwegian Museum of Cultural History.\\\\n\\\\nIn 2010 he was decorated with the Royal Norwegian Order of St. Olav.\\\\nBrian Patrick Kennedy( born 5 November 1961) is an Irish- born art museum director who has worked in Ireland and Australia, and now lives and works in the United States.\\\\n\\\\nHe is currently the director of the Peabody Essex Museum.\\\\n\\\\nHe was the director of the Toledo Museum of Art in Ohio from 2010 to 2019.\\\\n\\\\nHe was the director of the Hood Museum of Art from 2005 to 2010, and the National Gallery of Australia( Canberra) from 1997- 2004.\\\\nPeter Levin is an American director of film, television and theatre.\\\\nShadows of the Underworld (German:\\\\n\\\\nSchatten der Unterwelt) is a 1931 German action film directed by and starring Harry Piel.\\\\n\\\\nIt also features Dary Holm, Elisabeth Pinajeff and Hans Junkermann.\\\\n\\\\nThe film's sets were designed by the art director Robert A. Dietrich and Emil Hasler.\\\\n\\\\nLocation shooting took place in St. Moritz in Switzerland.\\\\n\\\\nA separate French-language version was also released.\\\\nThe Great Deception is a 1926 American silent drama film starring Basil Rathbone, Ben Lyon, and Aileen Pringle.\\\\n\\\\nIt is based on the 1915 novel\\\" The Yellow Dove\\\" by George Gibbs about World War I era espionage, previously adapted as the 1919 film\\\" Shadows of Suspicion\\\"\\\\n\\\\nThis film is currently a lost film.\\\\n\\\\nA\\\" New York Times\\\" review considered\\\" this photoplay possesses an element of mystery and suspense\\\".\\\\nIan Barry is an Australian director of film and TV.\\\\nShadows of the Orient is a 1935 American film directed by Burt P. Lynwood.\\\\nShadows of Death is a 1945 American western film directed by Sam Newfield.\n\nAnswer the following question: Extract the names of the people mentioned in the given text."], "reference": ["'1. Niels Hansen\\n2. Harry Piel (also known as Hubert August Piel)\\n3. Dary Holm\\n4. Elisabeth Pinajeff\\n5. Hans Junkermann\\n6. Robert A. Dietrich\\n7. Emil Hasler\\n8. Basil Rathbone\\n9. Ben Lyon\\n10. Aileen Pringle\\n11. George Gibbs\\n12. Ian Barry\\n13. Burt P. Lynwood\\n14. Sam Newfield\\n15. Olav Aaraas\\n16. Brian Patrick Kennedy\\n17. Peter Levin'\n"]} +{"qna_file": "compositional_skills/extraction/fda_filing/bullet_points/qna.yaml", "question_id": "166644688768548788820053520638117399743", "category": "taxonomy", "turns": ["Given the context below:\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\n## Performance Data\n\nA retrospective study has been carried out to assess the standalone performance of the image analysis algorithm and notification functionality of Triage ICH. The study evaluated the Triage ICH image analysis algorithm in terms of sensitivity and specificity with respect to a ground truth, as established by experienced US board certified neuroradiologists, in the detection of intracranial hemorrhage (ICH) in the brain. In addition, the study reported and compared the time to notification for the Triage ICH device with respect to the time to notification for the standard of care as established by the predicate device.\n\nA sample size of 341 non-contrast Computed Tomography (NCCT) scans (studies) were obtained from 30 different hospitals and clinics in the U.S. The majority of patients were scanned at Boston Medical Centre (N=237) and the remainder came from 29 different referral hospitals in MassachuseTs State. The patient cohort was enriched to ensure an approximately equal balance of ICH positive and negative studies and to ensure the distribution of clinical and demographic variables (e.g., age, gender, ICH subtype, traumatic vs non traumatic etiology) allows generalizability to the patient population for whom use is intended.\n\nThe cases (n=341) were all successfully processed with the algorithm. The confusion matrix was as follows: True Positives (TP): 149, True Negatives (TN): 159, False Positives (FP): 15, False Negatives (FN): 18. The overall performance can also be summarized with the following metrics: sensitivity (or positive percentage agreement, defined as TP/[TP+FN]) was 89.22%, specificity (or negative percentage agreement, defined as TN/[TN+FP]) was 91.37%. The receiver operating curve (ROC) for the device is shown in Figure 1.\n\nFigure 1. Receiver Opera0ng Curve for the device, with the opera0ng point (sensi0vity 89.2%, specificity 91.4%) shown with the red triangle.\n\nBecause the lower bound of each confidence interval for sensitivity and specificity exceeded 80%, the study met the pre-specified performance goals.\n\nAs part of a secondary analysis, the company stratified the device performance by various confounding variables: ICH subtype (Table 1); by gender (Table 2); by age (Table 3); by slice thickness (Table 4); by clinical site (Table 5); and by ICH Volume (Table 6).\n\nTable 1. Summary of the performance metrics for the subsets of scans stra0fied by ICH Subtype.\n\n| ICH Subtype\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0|\n|-----------------------------------|------------------------|\n| Intraparenchymal Hemorrhage (IPH) | 96.61% (88.29-99.59)\u00a0\u00a0|\n| Intraventricular Hemorrhage (IVH) | 100.00% (59.04-100.00) |\n| Subarachnoid Hemorrhage (SAH)\u00a0\u00a0\u00a0| 35.71% (12.76-64.86)\u00a0\u00a0|\n| Subdural Hemorrhage (SDH)\u00a0\u00a0\u00a0\u00a0\u00a0| 66.67% (40.99-86.66)\u00a0\u00a0|\n| Mul7ple Types\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 98.55% (92.19-99.96)\u00a0\u00a0|\n\nTable 2. Summary of the performance metrics for the subsets of scans stra0fied by gender.\n\n| Gender\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|----------|-----------------------|------------------------|\n| Male\u00a0\u00a0\u00a0| 93.18% (85.75-97.46)\u00a0| 91.30% (83.58-96.17)\u00a0\u00a0|\n| Female\u00a0\u00a0| 84.81% (74.97-91.90)\u00a0| 91.46% (83.20-96.50)\u00a0\u00a0|\n\nTable 3. Summary of the performance metrics for the subsets of scans stra0fied by age group.\n\n| Age\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|---------------|-----------------------|------------------------|\n| 21 < Age < 50 | 83.33% (67.19-93.63)\u00a0| 88.73% (79.00-95.01)\u00a0\u00a0|\n| 50 \u2264 Age < 70 | 92.75% (83.89-97.61)\u00a0| 91.25% (82.80-96.41)\u00a0\u00a0|\n| Age \u2265 70\u00a0\u00a0\u00a0| 88.71% (78.11-95.34)\u00a0| 100.00% (85.18-100.00) |\n\nTable 4. Summary of the performance metrics for the subsets of scans stra0fied by slice thickness\n\n| Slice Thickness\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|--------------------------------|-----------------------|------------------------|\n| Slice Thickness < 1.5 mm\u00a0\u00a0\u00a0\u00a0| 87.50% (79.92-92.99)\u00a0| 94.21% (88.44-97.64)\u00a0\u00a0|\n| 1.5mm \u2264 Slice Thickness < 3 mm | 95.65% (78.05-99.89)\u00a0| 100.00% (82.35-100.00) |\n| Slice Thickness \u2265 3 mm\u00a0\u00a0\u00a0\u00a0\u00a0| 90.62% (74.98-98.02)\u00a0| 76.47% (58.83-89.25)\u00a0\u00a0|\n\nTable 5. Summary of the performance metrics for the subsets of scans stra0fied by referring hospital\n\n| Clinical Site\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|-----------------------|-----------------------|------------------------|\n| Boston Medical Centre | 89.38% (82.18-94.39)\u00a0| 94.35% (88.71-97.70)\u00a0\u00a0|\n| Other\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 88.89% (77.37-95.81)\u00a0| 84.00% (70.89-92.83)\u00a0\u00a0|\n\n| Minimal Volume Threshold (ml)\u00a0\u00a0| Sensi7vity above Threshold (95% CI)\u00a0\u00a0|\n|---------------------------------|---------------------------------------|\n| vol \u2265 0 ml\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 89.22% (83.50-93.49)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 6. Summary of the sensi0vity for the subsets of scans stra0fied by ICH volume\n\n| vol \u2265 0.4ml\u00a0\u00a0| 94.59% (89.63-97.64)\u00a0\u00a0|\n|---------------|------------------------|\n| vol \u2265 1ml\u00a0\u00a0\u00a0| 97.04% (92.59-99.19)\u00a0\u00a0|\n| vol \u2265 5ml\u00a0\u00a0\u00a0| 99.04% (94.76-99.98)\u00a0\u00a0|\n\nTo assess the secondary outcome measure, the device's time to notification was recorded for a sample of cases. This ranged from 50 seconds to 126 seconds, and is substantially lower than the average time to notification seen in the Standard of Care reported by the predicate device (38.2 minutes).\n\nIn summary, the clinical tests demonstrate that the device performs with good sensitivity and specificity to detect acute ICH from non-contrast CT imaging, and met the primary study endpoint of 80% sensitivity and specificity. This acceptability criterion was the same as used for the predicate device (Viz ICH, K193658), supporting the equivalent performance to the predicate. Furthermore, the device's time to notification ranged from 50-126 seconds which was significantly shorter than the average time to notification seen in the Standard of Care of 38.2 minutes reported by the predicate device.\n\n## Brainomix Limited\n\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\nAnswer the following question: Provide a bulleted list of all the hospitals referenced in this study.\n"], "reference": ["Here is a list of all the hospitals referenced in this study:\n- Boston Medical Centre\n\nThis study also referenced 29 different referral hospitals in Massachusetts State, but the exact hospital names were not provided.\n"]} +{"qna_file": "compositional_skills/extraction/fda_filing/bullet_points/qna.yaml", "question_id": "239993195295869993691285139988711303142", "category": "taxonomy", "turns": ["Given the context below:\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\n## Performance Data\n\nA retrospective study has been carried out to assess the standalone performance of the image analysis algorithm and notification functionality of Triage ICH. The study evaluated the Triage ICH image analysis algorithm in terms of sensitivity and specificity with respect to a ground truth, as established by experienced US board certified neuroradiologists, in the detection of intracranial hemorrhage (ICH) in the brain. In addition, the study reported and compared the time to notification for the Triage ICH device with respect to the time to notification for the standard of care as established by the predicate device.\n\nA sample size of 341 non-contrast Computed Tomography (NCCT) scans (studies) were obtained from 30 different hospitals and clinics in the U.S. The majority of patients were scanned at Boston Medical Centre (N=237) and the remainder came from 29 different referral hospitals in MassachuseTs State. The patient cohort was enriched to ensure an approximately equal balance of ICH positive and negative studies and to ensure the distribution of clinical and demographic variables (e.g., age, gender, ICH subtype, traumatic vs non traumatic etiology) allows generalizability to the patient population for whom use is intended.\n\nThe cases (n=341) were all successfully processed with the algorithm. The confusion matrix was as follows: True Positives (TP): 149, True Negatives (TN): 159, False Positives (FP): 15, False Negatives (FN): 18. The overall performance can also be summarized with the following metrics: sensitivity (or positive percentage agreement, defined as TP/[TP+FN]) was 89.22%, specificity (or negative percentage agreement, defined as TN/[TN+FP]) was 91.37%. The receiver operating curve (ROC) for the device is shown in Figure 1.\n\nFigure 1. Receiver Opera0ng Curve for the device, with the opera0ng point (sensi0vity 89.2%, specificity 91.4%) shown with the red triangle.\n\nBecause the lower bound of each confidence interval for sensitivity and specificity exceeded 80%, the study met the pre-specified performance goals.\n\nAs part of a secondary analysis, the company stratified the device performance by various confounding variables: ICH subtype (Table 1); by gender (Table 2); by age (Table 3); by slice thickness (Table 4); by clinical site (Table 5); and by ICH Volume (Table 6).\n\nTable 1. Summary of the performance metrics for the subsets of scans stra0fied by ICH Subtype.\n\n| ICH Subtype\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0|\n|-----------------------------------|------------------------|\n| Intraparenchymal Hemorrhage (IPH) | 96.61% (88.29-99.59)\u00a0\u00a0|\n| Intraventricular Hemorrhage (IVH) | 100.00% (59.04-100.00) |\n| Subarachnoid Hemorrhage (SAH)\u00a0\u00a0\u00a0| 35.71% (12.76-64.86)\u00a0\u00a0|\n| Subdural Hemorrhage (SDH)\u00a0\u00a0\u00a0\u00a0\u00a0| 66.67% (40.99-86.66)\u00a0\u00a0|\n| Mul7ple Types\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 98.55% (92.19-99.96)\u00a0\u00a0|\n\nTable 2. Summary of the performance metrics for the subsets of scans stra0fied by gender.\n\n| Gender\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|----------|-----------------------|------------------------|\n| Male\u00a0\u00a0\u00a0| 93.18% (85.75-97.46)\u00a0| 91.30% (83.58-96.17)\u00a0\u00a0|\n| Female\u00a0\u00a0| 84.81% (74.97-91.90)\u00a0| 91.46% (83.20-96.50)\u00a0\u00a0|\n\nTable 3. Summary of the performance metrics for the subsets of scans stra0fied by age group.\n\n| Age\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|---------------|-----------------------|------------------------|\n| 21 < Age < 50 | 83.33% (67.19-93.63)\u00a0| 88.73% (79.00-95.01)\u00a0\u00a0|\n| 50 \u2264 Age < 70 | 92.75% (83.89-97.61)\u00a0| 91.25% (82.80-96.41)\u00a0\u00a0|\n| Age \u2265 70\u00a0\u00a0\u00a0| 88.71% (78.11-95.34)\u00a0| 100.00% (85.18-100.00) |\n\nTable 4. Summary of the performance metrics for the subsets of scans stra0fied by slice thickness\n\n| Slice Thickness\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|--------------------------------|-----------------------|------------------------|\n| Slice Thickness < 1.5 mm\u00a0\u00a0\u00a0\u00a0| 87.50% (79.92-92.99)\u00a0| 94.21% (88.44-97.64)\u00a0\u00a0|\n| 1.5mm \u2264 Slice Thickness < 3 mm | 95.65% (78.05-99.89)\u00a0| 100.00% (82.35-100.00) |\n| Slice Thickness \u2265 3 mm\u00a0\u00a0\u00a0\u00a0\u00a0| 90.62% (74.98-98.02)\u00a0| 76.47% (58.83-89.25)\u00a0\u00a0|\n\nTable 5. Summary of the performance metrics for the subsets of scans stra0fied by referring hospital\n\n| Clinical Site\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|-----------------------|-----------------------|------------------------|\n| Boston Medical Centre | 89.38% (82.18-94.39)\u00a0| 94.35% (88.71-97.70)\u00a0\u00a0|\n| Other\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 88.89% (77.37-95.81)\u00a0| 84.00% (70.89-92.83)\u00a0\u00a0|\n\n| Minimal Volume Threshold (ml)\u00a0\u00a0| Sensi7vity above Threshold (95% CI)\u00a0\u00a0|\n|---------------------------------|---------------------------------------|\n| vol \u2265 0 ml\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 89.22% (83.50-93.49)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 6. Summary of the sensi0vity for the subsets of scans stra0fied by ICH volume\n\n| vol \u2265 0.4ml\u00a0\u00a0| 94.59% (89.63-97.64)\u00a0\u00a0|\n|---------------|------------------------|\n| vol \u2265 1ml\u00a0\u00a0\u00a0| 97.04% (92.59-99.19)\u00a0\u00a0|\n| vol \u2265 5ml\u00a0\u00a0\u00a0| 99.04% (94.76-99.98)\u00a0\u00a0|\n\nTo assess the secondary outcome measure, the device's time to notification was recorded for a sample of cases. This ranged from 50 seconds to 126 seconds, and is substantially lower than the average time to notification seen in the Standard of Care reported by the predicate device (38.2 minutes).\n\nIn summary, the clinical tests demonstrate that the device performs with good sensitivity and specificity to detect acute ICH from non-contrast CT imaging, and met the primary study endpoint of 80% sensitivity and specificity. This acceptability criterion was the same as used for the predicate device (Viz ICH, K193658), supporting the equivalent performance to the predicate. Furthermore, the device's time to notification ranged from 50-126 seconds which was significantly shorter than the average time to notification seen in the Standard of Care of 38.2 minutes reported by the predicate device.\n\n## Brainomix Limited\n\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\nAnswer the following question: Create a bulleted list of all the variables that were controlled to an ensure the patient cohort could be generalizable to the patient population."], "reference": ["Here are all the variables that the were mentioned:\n- age\n- gender\n- ICH subtype\n- traumatic vs non traumatic etiology\n"]} +{"qna_file": "compositional_skills/extraction/fda_filing/bullet_points/qna.yaml", "question_id": "233649569038669819719360940967084280675", "category": "taxonomy", "turns": ["Given the context below:\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\n## Performance Data\n\nA retrospective study has been carried out to assess the standalone performance of the image analysis algorithm and notification functionality of Triage ICH. The study evaluated the Triage ICH image analysis algorithm in terms of sensitivity and specificity with respect to a ground truth, as established by experienced US board certified neuroradiologists, in the detection of intracranial hemorrhage (ICH) in the brain. In addition, the study reported and compared the time to notification for the Triage ICH device with respect to the time to notification for the standard of care as established by the predicate device.\n\nA sample size of 341 non-contrast Computed Tomography (NCCT) scans (studies) were obtained from 30 different hospitals and clinics in the U.S. The majority of patients were scanned at Boston Medical Centre (N=237) and the remainder came from 29 different referral hospitals in MassachuseTs State. The patient cohort was enriched to ensure an approximately equal balance of ICH positive and negative studies and to ensure the distribution of clinical and demographic variables (e.g., age, gender, ICH subtype, traumatic vs non traumatic etiology) allows generalizability to the patient population for whom use is intended.\n\nThe cases (n=341) were all successfully processed with the algorithm. The confusion matrix was as follows: True Positives (TP): 149, True Negatives (TN): 159, False Positives (FP): 15, False Negatives (FN): 18. The overall performance can also be summarized with the following metrics: sensitivity (or positive percentage agreement, defined as TP/[TP+FN]) was 89.22%, specificity (or negative percentage agreement, defined as TN/[TN+FP]) was 91.37%. The receiver operating curve (ROC) for the device is shown in Figure 1.\n\nFigure 1. Receiver Opera0ng Curve for the device, with the opera0ng point (sensi0vity 89.2%, specificity 91.4%) shown with the red triangle.\n\nBecause the lower bound of each confidence interval for sensitivity and specificity exceeded 80%, the study met the pre-specified performance goals.\n\nAs part of a secondary analysis, the company stratified the device performance by various confounding variables: ICH subtype (Table 1); by gender (Table 2); by age (Table 3); by slice thickness (Table 4); by clinical site (Table 5); and by ICH Volume (Table 6).\n\nTable 1. Summary of the performance metrics for the subsets of scans stra0fied by ICH Subtype.\n\n| ICH Subtype\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0|\n|-----------------------------------|------------------------|\n| Intraparenchymal Hemorrhage (IPH) | 96.61% (88.29-99.59)\u00a0\u00a0|\n| Intraventricular Hemorrhage (IVH) | 100.00% (59.04-100.00) |\n| Subarachnoid Hemorrhage (SAH)\u00a0\u00a0\u00a0| 35.71% (12.76-64.86)\u00a0\u00a0|\n| Subdural Hemorrhage (SDH)\u00a0\u00a0\u00a0\u00a0\u00a0| 66.67% (40.99-86.66)\u00a0\u00a0|\n| Mul7ple Types\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 98.55% (92.19-99.96)\u00a0\u00a0|\n\nTable 2. Summary of the performance metrics for the subsets of scans stra0fied by gender.\n\n| Gender\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|----------|-----------------------|------------------------|\n| Male\u00a0\u00a0\u00a0| 93.18% (85.75-97.46)\u00a0| 91.30% (83.58-96.17)\u00a0\u00a0|\n| Female\u00a0\u00a0| 84.81% (74.97-91.90)\u00a0| 91.46% (83.20-96.50)\u00a0\u00a0|\n\nTable 3. Summary of the performance metrics for the subsets of scans stra0fied by age group.\n\n| Age\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|---------------|-----------------------|------------------------|\n| 21 < Age < 50 | 83.33% (67.19-93.63)\u00a0| 88.73% (79.00-95.01)\u00a0\u00a0|\n| 50 \u2264 Age < 70 | 92.75% (83.89-97.61)\u00a0| 91.25% (82.80-96.41)\u00a0\u00a0|\n| Age \u2265 70\u00a0\u00a0\u00a0| 88.71% (78.11-95.34)\u00a0| 100.00% (85.18-100.00) |\n\nTable 4. Summary of the performance metrics for the subsets of scans stra0fied by slice thickness\n\n| Slice Thickness\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|--------------------------------|-----------------------|------------------------|\n| Slice Thickness < 1.5 mm\u00a0\u00a0\u00a0\u00a0| 87.50% (79.92-92.99)\u00a0| 94.21% (88.44-97.64)\u00a0\u00a0|\n| 1.5mm \u2264 Slice Thickness < 3 mm | 95.65% (78.05-99.89)\u00a0| 100.00% (82.35-100.00) |\n| Slice Thickness \u2265 3 mm\u00a0\u00a0\u00a0\u00a0\u00a0| 90.62% (74.98-98.02)\u00a0| 76.47% (58.83-89.25)\u00a0\u00a0|\n\nTable 5. Summary of the performance metrics for the subsets of scans stra0fied by referring hospital\n\n| Clinical Site\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|-----------------------|-----------------------|------------------------|\n| Boston Medical Centre | 89.38% (82.18-94.39)\u00a0| 94.35% (88.71-97.70)\u00a0\u00a0|\n| Other\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 88.89% (77.37-95.81)\u00a0| 84.00% (70.89-92.83)\u00a0\u00a0|\n\n| Minimal Volume Threshold (ml)\u00a0\u00a0| Sensi7vity above Threshold (95% CI)\u00a0\u00a0|\n|---------------------------------|---------------------------------------|\n| vol \u2265 0 ml\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 89.22% (83.50-93.49)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 6. Summary of the sensi0vity for the subsets of scans stra0fied by ICH volume\n\n| vol \u2265 0.4ml\u00a0\u00a0| 94.59% (89.63-97.64)\u00a0\u00a0|\n|---------------|------------------------|\n| vol \u2265 1ml\u00a0\u00a0\u00a0| 97.04% (92.59-99.19)\u00a0\u00a0|\n| vol \u2265 5ml\u00a0\u00a0\u00a0| 99.04% (94.76-99.98)\u00a0\u00a0|\n\nTo assess the secondary outcome measure, the device's time to notification was recorded for a sample of cases. This ranged from 50 seconds to 126 seconds, and is substantially lower than the average time to notification seen in the Standard of Care reported by the predicate device (38.2 minutes).\n\nIn summary, the clinical tests demonstrate that the device performs with good sensitivity and specificity to detect acute ICH from non-contrast CT imaging, and met the primary study endpoint of 80% sensitivity and specificity. This acceptability criterion was the same as used for the predicate device (Viz ICH, K193658), supporting the equivalent performance to the predicate. Furthermore, the device's time to notification ranged from 50-126 seconds which was significantly shorter than the average time to notification seen in the Standard of Care of 38.2 minutes reported by the predicate device.\n\n## Brainomix Limited\n\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\nAnswer the following question: Find the performance data by Slice Thickness, and return it in a bulleted list.\n"], "reference": ["Here is the performance data by Slice Thickness, taken from Table 4 in the document:\n- Slice Thickness < 1.5 mm\n - Sensitivity (95% CI): 87.50% (79.92-92.99)\n - Specificity (95% CI): 94.21% (88.44-97.64)\n- 1.5mm \u2264 Slice Thickness < 3 mm\n - Sensitivity (95% CI): 95.65% (78.05-99.89)\n - Specificity (95% CI): 100.00% (82.35-100.00)\n- Slice Thickness \u2265 3 mm\n - Sensitivity (95% CI): 90.62% (74.98-98.02)\n - Specificity (95% CI): 76.47% (58.83-89.25)\n"]} +{"qna_file": "compositional_skills/extraction/fda_filing/plain_text/qna.yaml", "question_id": "62680308787734396925620882994845674925", "category": "taxonomy", "turns": ["Given the context below:\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\n## Performance Data\n\nA retrospective study has been carried out to assess the standalone performance of the image analysis algorithm and notification functionality of Triage ICH. The study evaluated the Triage ICH image analysis algorithm in terms of sensitivity and specificity with respect to a ground truth, as established by experienced US board certified neuroradiologists, in the detection of intracranial hemorrhage (ICH) in the brain. In addition, the study reported and compared the time to notification for the Triage ICH device with respect to the time to notification for the standard of care as established by the predicate device.\n\nA sample size of 341 non-contrast Computed Tomography (NCCT) scans (studies) were obtained from 30 different hospitals and clinics in the U.S. The majority of patients were scanned at Boston Medical Centre (N=237) and the remainder came from 29 different referral hospitals in MassachuseTs State. The patient cohort was enriched to ensure an approximately equal balance of ICH positive and negative studies and to ensure the distribution of clinical and demographic variables (e.g., age, gender, ICH subtype, traumatic vs non traumatic etiology) allows generalizability to the patient population for whom use is intended.\n\nThe cases (n=341) were all successfully processed with the algorithm. The confusion matrix was as follows: True Positives (TP): 149, True Negatives (TN): 159, False Positives (FP): 15, False Negatives (FN): 18. The overall performance can also be summarized with the following metrics: sensitivity (or positive percentage agreement, defined as TP/[TP+FN]) was 89.22%, specificity (or negative percentage agreement, defined as TN/[TN+FP]) was 91.37%. The receiver operating curve (ROC) for the device is shown in Figure 1.\n\nFigure 1. Receiver Opera0ng Curve for the device, with the opera0ng point (sensi0vity 89.2%, specificity 91.4%) shown with the red triangle.\n\nBecause the lower bound of each confidence interval for sensitivity and specificity exceeded 80%, the study met the pre-specified performance goals.\n\nAs part of a secondary analysis, the company stratified the device performance by various confounding variables: ICH subtype (Table 1); by gender (Table 2); by age (Table 3); by slice thickness (Table 4); by clinical site (Table 5); and by ICH Volume (Table 6).\n\nTable 1. Summary of the performance metrics for the subsets of scans stra0fied by ICH Subtype.\n\n| ICH Subtype\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0|\n|-----------------------------------|------------------------|\n| Intraparenchymal Hemorrhage (IPH) | 96.61% (88.29-99.59)\u00a0\u00a0|\n| Intraventricular Hemorrhage (IVH) | 100.00% (59.04-100.00) |\n| Subarachnoid Hemorrhage (SAH)\u00a0\u00a0\u00a0| 35.71% (12.76-64.86)\u00a0\u00a0|\n| Subdural Hemorrhage (SDH)\u00a0\u00a0\u00a0\u00a0\u00a0| 66.67% (40.99-86.66)\u00a0\u00a0|\n| Mul7ple Types\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 98.55% (92.19-99.96)\u00a0\u00a0|\n\nTable 2. Summary of the performance metrics for the subsets of scans stra0fied by gender.\n\n| Gender\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|----------|-----------------------|------------------------|\n| Male\u00a0\u00a0\u00a0| 93.18% (85.75-97.46)\u00a0| 91.30% (83.58-96.17)\u00a0\u00a0|\n| Female\u00a0\u00a0| 84.81% (74.97-91.90)\u00a0| 91.46% (83.20-96.50)\u00a0\u00a0|\n\nTable 3. Summary of the performance metrics for the subsets of scans stra0fied by age group.\n\n| Age\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|---------------|-----------------------|------------------------|\n| 21 < Age < 50 | 83.33% (67.19-93.63)\u00a0| 88.73% (79.00-95.01)\u00a0\u00a0|\n| 50 \u2264 Age < 70 | 92.75% (83.89-97.61)\u00a0| 91.25% (82.80-96.41)\u00a0\u00a0|\n| Age \u2265 70\u00a0\u00a0\u00a0| 88.71% (78.11-95.34)\u00a0| 100.00% (85.18-100.00) |\n\nTable 4. Summary of the performance metrics for the subsets of scans stra0fied by slice thickness\n\n| Slice Thickness\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|--------------------------------|-----------------------|------------------------|\n| Slice Thickness < 1.5 mm\u00a0\u00a0\u00a0\u00a0| 87.50% (79.92-92.99)\u00a0| 94.21% (88.44-97.64)\u00a0\u00a0|\n| 1.5mm \u2264 Slice Thickness < 3 mm | 95.65% (78.05-99.89)\u00a0| 100.00% (82.35-100.00) |\n| Slice Thickness \u2265 3 mm\u00a0\u00a0\u00a0\u00a0\u00a0| 90.62% (74.98-98.02)\u00a0| 76.47% (58.83-89.25)\u00a0\u00a0|\n\nTable 5. Summary of the performance metrics for the subsets of scans stra0fied by referring hospital\n\n| Clinical Site\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|-----------------------|-----------------------|------------------------|\n| Boston Medical Centre | 89.38% (82.18-94.39)\u00a0| 94.35% (88.71-97.70)\u00a0\u00a0|\n| Other\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 88.89% (77.37-95.81)\u00a0| 84.00% (70.89-92.83)\u00a0\u00a0|\n\n| Minimal Volume Threshold (ml)\u00a0\u00a0| Sensi7vity above Threshold (95% CI)\u00a0\u00a0|\n|---------------------------------|---------------------------------------|\n| vol \u2265 0 ml\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 89.22% (83.50-93.49)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 6. Summary of the sensi0vity for the subsets of scans stra0fied by ICH volume\n\n| vol \u2265 0.4ml\u00a0\u00a0| 94.59% (89.63-97.64)\u00a0\u00a0|\n|---------------|------------------------|\n| vol \u2265 1ml\u00a0\u00a0\u00a0| 97.04% (92.59-99.19)\u00a0\u00a0|\n| vol \u2265 5ml\u00a0\u00a0\u00a0| 99.04% (94.76-99.98)\u00a0\u00a0|\n\nTo assess the secondary outcome measure, the device's time to notification was recorded for a sample of cases. This ranged from 50 seconds to 126 seconds, and is substantially lower than the average time to notification seen in the Standard of Care reported by the predicate device (38.2 minutes).\n\nIn summary, the clinical tests demonstrate that the device performs with good sensitivity and specificity to detect acute ICH from non-contrast CT imaging, and met the primary study endpoint of 80% sensitivity and specificity. This acceptability criterion was the same as used for the predicate device (Viz ICH, K193658), supporting the equivalent performance to the predicate. Furthermore, the device's time to notification ranged from 50-126 seconds which was significantly shorter than the average time to notification seen in the Standard of Care of 38.2 minutes reported by the predicate device.\n\n## Brainomix Limited\n\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\nAnswer the following question: Return a list of all locations mentioned\n"], "reference": ["Oxford, United Kingdom, U.S., Massachusetts\n"]} +{"qna_file": "compositional_skills/extraction/fda_filing/plain_text/qna.yaml", "question_id": "171334666228487631059576958985489300677", "category": "taxonomy", "turns": ["Given the context below:\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\n## Performance Data\n\nA retrospective study has been carried out to assess the standalone performance of the image analysis algorithm and notification functionality of Triage ICH. The study evaluated the Triage ICH image analysis algorithm in terms of sensitivity and specificity with respect to a ground truth, as established by experienced US board certified neuroradiologists, in the detection of intracranial hemorrhage (ICH) in the brain. In addition, the study reported and compared the time to notification for the Triage ICH device with respect to the time to notification for the standard of care as established by the predicate device.\n\nA sample size of 341 non-contrast Computed Tomography (NCCT) scans (studies) were obtained from 30 different hospitals and clinics in the U.S. The majority of patients were scanned at Boston Medical Centre (N=237) and the remainder came from 29 different referral hospitals in MassachuseTs State. The patient cohort was enriched to ensure an approximately equal balance of ICH positive and negative studies and to ensure the distribution of clinical and demographic variables (e.g., age, gender, ICH subtype, traumatic vs non traumatic etiology) allows generalizability to the patient population for whom use is intended.\n\nThe cases (n=341) were all successfully processed with the algorithm. The confusion matrix was as follows: True Positives (TP): 149, True Negatives (TN): 159, False Positives (FP): 15, False Negatives (FN): 18. The overall performance can also be summarized with the following metrics: sensitivity (or positive percentage agreement, defined as TP/[TP+FN]) was 89.22%, specificity (or negative percentage agreement, defined as TN/[TN+FP]) was 91.37%. The receiver operating curve (ROC) for the device is shown in Figure 1.\n\nFigure 1. Receiver Opera0ng Curve for the device, with the opera0ng point (sensi0vity 89.2%, specificity 91.4%) shown with the red triangle.\n\nBecause the lower bound of each confidence interval for sensitivity and specificity exceeded 80%, the study met the pre-specified performance goals.\n\nAs part of a secondary analysis, the company stratified the device performance by various confounding variables: ICH subtype (Table 1); by gender (Table 2); by age (Table 3); by slice thickness (Table 4); by clinical site (Table 5); and by ICH Volume (Table 6).\n\nTable 1. Summary of the performance metrics for the subsets of scans stra0fied by ICH Subtype.\n\n| ICH Subtype\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0|\n|-----------------------------------|------------------------|\n| Intraparenchymal Hemorrhage (IPH) | 96.61% (88.29-99.59)\u00a0\u00a0|\n| Intraventricular Hemorrhage (IVH) | 100.00% (59.04-100.00) |\n| Subarachnoid Hemorrhage (SAH)\u00a0\u00a0\u00a0| 35.71% (12.76-64.86)\u00a0\u00a0|\n| Subdural Hemorrhage (SDH)\u00a0\u00a0\u00a0\u00a0\u00a0| 66.67% (40.99-86.66)\u00a0\u00a0|\n| Mul7ple Types\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 98.55% (92.19-99.96)\u00a0\u00a0|\n\nTable 2. Summary of the performance metrics for the subsets of scans stra0fied by gender.\n\n| Gender\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|----------|-----------------------|------------------------|\n| Male\u00a0\u00a0\u00a0| 93.18% (85.75-97.46)\u00a0| 91.30% (83.58-96.17)\u00a0\u00a0|\n| Female\u00a0\u00a0| 84.81% (74.97-91.90)\u00a0| 91.46% (83.20-96.50)\u00a0\u00a0|\n\nTable 3. Summary of the performance metrics for the subsets of scans stra0fied by age group.\n\n| Age\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|---------------|-----------------------|------------------------|\n| 21 < Age < 50 | 83.33% (67.19-93.63)\u00a0| 88.73% (79.00-95.01)\u00a0\u00a0|\n| 50 \u2264 Age < 70 | 92.75% (83.89-97.61)\u00a0| 91.25% (82.80-96.41)\u00a0\u00a0|\n| Age \u2265 70\u00a0\u00a0\u00a0| 88.71% (78.11-95.34)\u00a0| 100.00% (85.18-100.00) |\n\nTable 4. Summary of the performance metrics for the subsets of scans stra0fied by slice thickness\n\n| Slice Thickness\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|--------------------------------|-----------------------|------------------------|\n| Slice Thickness < 1.5 mm\u00a0\u00a0\u00a0\u00a0| 87.50% (79.92-92.99)\u00a0| 94.21% (88.44-97.64)\u00a0\u00a0|\n| 1.5mm \u2264 Slice Thickness < 3 mm | 95.65% (78.05-99.89)\u00a0| 100.00% (82.35-100.00) |\n| Slice Thickness \u2265 3 mm\u00a0\u00a0\u00a0\u00a0\u00a0| 90.62% (74.98-98.02)\u00a0| 76.47% (58.83-89.25)\u00a0\u00a0|\n\nTable 5. Summary of the performance metrics for the subsets of scans stra0fied by referring hospital\n\n| Clinical Site\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|-----------------------|-----------------------|------------------------|\n| Boston Medical Centre | 89.38% (82.18-94.39)\u00a0| 94.35% (88.71-97.70)\u00a0\u00a0|\n| Other\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 88.89% (77.37-95.81)\u00a0| 84.00% (70.89-92.83)\u00a0\u00a0|\n\n| Minimal Volume Threshold (ml)\u00a0\u00a0| Sensi7vity above Threshold (95% CI)\u00a0\u00a0|\n|---------------------------------|---------------------------------------|\n| vol \u2265 0 ml\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 89.22% (83.50-93.49)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 6. Summary of the sensi0vity for the subsets of scans stra0fied by ICH volume\n\n| vol \u2265 0.4ml\u00a0\u00a0| 94.59% (89.63-97.64)\u00a0\u00a0|\n|---------------|------------------------|\n| vol \u2265 1ml\u00a0\u00a0\u00a0| 97.04% (92.59-99.19)\u00a0\u00a0|\n| vol \u2265 5ml\u00a0\u00a0\u00a0| 99.04% (94.76-99.98)\u00a0\u00a0|\n\nTo assess the secondary outcome measure, the device's time to notification was recorded for a sample of cases. This ranged from 50 seconds to 126 seconds, and is substantially lower than the average time to notification seen in the Standard of Care reported by the predicate device (38.2 minutes).\n\nIn summary, the clinical tests demonstrate that the device performs with good sensitivity and specificity to detect acute ICH from non-contrast CT imaging, and met the primary study endpoint of 80% sensitivity and specificity. This acceptability criterion was the same as used for the predicate device (Viz ICH, K193658), supporting the equivalent performance to the predicate. Furthermore, the device's time to notification ranged from 50-126 seconds which was significantly shorter than the average time to notification seen in the Standard of Care of 38.2 minutes reported by the predicate device.\n\n## Brainomix Limited\n\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\nAnswer the following question: Give me all of the acronyms mentioned in the document, along with what the acronym stands for."], "reference": ["ICH, intracranial hemorrhage; NCCT, non-contrast Computed Tomography; TP, True Positives; TN, True Negatives; FP, False Positives; FN, False Negatives; ROC, receiver operating curve; IPH, Intraparenchymal Hemorrhage; IVH, Intraventricular Hemorrhage; SAH, Subarachnoid Hemorrhage; SDH, Subdural Hemorrhage\n"]} +{"qna_file": "compositional_skills/extraction/fda_filing/plain_text/qna.yaml", "question_id": "83494279020672662581197475480100653450", "category": "taxonomy", "turns": ["Given the context below:\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\n## Performance Data\n\nA retrospective study has been carried out to assess the standalone performance of the image analysis algorithm and notification functionality of Triage ICH. The study evaluated the Triage ICH image analysis algorithm in terms of sensitivity and specificity with respect to a ground truth, as established by experienced US board certified neuroradiologists, in the detection of intracranial hemorrhage (ICH) in the brain. In addition, the study reported and compared the time to notification for the Triage ICH device with respect to the time to notification for the standard of care as established by the predicate device.\n\nA sample size of 341 non-contrast Computed Tomography (NCCT) scans (studies) were obtained from 30 different hospitals and clinics in the U.S. The majority of patients were scanned at Boston Medical Centre (N=237) and the remainder came from 29 different referral hospitals in MassachuseTs State. The patient cohort was enriched to ensure an approximately equal balance of ICH positive and negative studies and to ensure the distribution of clinical and demographic variables (e.g., age, gender, ICH subtype, traumatic vs non traumatic etiology) allows generalizability to the patient population for whom use is intended.\n\nThe cases (n=341) were all successfully processed with the algorithm. The confusion matrix was as follows: True Positives (TP): 149, True Negatives (TN): 159, False Positives (FP): 15, False Negatives (FN): 18. The overall performance can also be summarized with the following metrics: sensitivity (or positive percentage agreement, defined as TP/[TP+FN]) was 89.22%, specificity (or negative percentage agreement, defined as TN/[TN+FP]) was 91.37%. The receiver operating curve (ROC) for the device is shown in Figure 1.\n\nFigure 1. Receiver Opera0ng Curve for the device, with the opera0ng point (sensi0vity 89.2%, specificity 91.4%) shown with the red triangle.\n\nBecause the lower bound of each confidence interval for sensitivity and specificity exceeded 80%, the study met the pre-specified performance goals.\n\nAs part of a secondary analysis, the company stratified the device performance by various confounding variables: ICH subtype (Table 1); by gender (Table 2); by age (Table 3); by slice thickness (Table 4); by clinical site (Table 5); and by ICH Volume (Table 6).\n\nTable 1. Summary of the performance metrics for the subsets of scans stra0fied by ICH Subtype.\n\n| ICH Subtype\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0|\n|-----------------------------------|------------------------|\n| Intraparenchymal Hemorrhage (IPH) | 96.61% (88.29-99.59)\u00a0\u00a0|\n| Intraventricular Hemorrhage (IVH) | 100.00% (59.04-100.00) |\n| Subarachnoid Hemorrhage (SAH)\u00a0\u00a0\u00a0| 35.71% (12.76-64.86)\u00a0\u00a0|\n| Subdural Hemorrhage (SDH)\u00a0\u00a0\u00a0\u00a0\u00a0| 66.67% (40.99-86.66)\u00a0\u00a0|\n| Mul7ple Types\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 98.55% (92.19-99.96)\u00a0\u00a0|\n\nTable 2. Summary of the performance metrics for the subsets of scans stra0fied by gender.\n\n| Gender\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|----------|-----------------------|------------------------|\n| Male\u00a0\u00a0\u00a0| 93.18% (85.75-97.46)\u00a0| 91.30% (83.58-96.17)\u00a0\u00a0|\n| Female\u00a0\u00a0| 84.81% (74.97-91.90)\u00a0| 91.46% (83.20-96.50)\u00a0\u00a0|\n\nTable 3. Summary of the performance metrics for the subsets of scans stra0fied by age group.\n\n| Age\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|---------------|-----------------------|------------------------|\n| 21 < Age < 50 | 83.33% (67.19-93.63)\u00a0| 88.73% (79.00-95.01)\u00a0\u00a0|\n| 50 \u2264 Age < 70 | 92.75% (83.89-97.61)\u00a0| 91.25% (82.80-96.41)\u00a0\u00a0|\n| Age \u2265 70\u00a0\u00a0\u00a0| 88.71% (78.11-95.34)\u00a0| 100.00% (85.18-100.00) |\n\nTable 4. Summary of the performance metrics for the subsets of scans stra0fied by slice thickness\n\n| Slice Thickness\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|--------------------------------|-----------------------|------------------------|\n| Slice Thickness < 1.5 mm\u00a0\u00a0\u00a0\u00a0| 87.50% (79.92-92.99)\u00a0| 94.21% (88.44-97.64)\u00a0\u00a0|\n| 1.5mm \u2264 Slice Thickness < 3 mm | 95.65% (78.05-99.89)\u00a0| 100.00% (82.35-100.00) |\n| Slice Thickness \u2265 3 mm\u00a0\u00a0\u00a0\u00a0\u00a0| 90.62% (74.98-98.02)\u00a0| 76.47% (58.83-89.25)\u00a0\u00a0|\n\nTable 5. Summary of the performance metrics for the subsets of scans stra0fied by referring hospital\n\n| Clinical Site\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|-----------------------|-----------------------|------------------------|\n| Boston Medical Centre | 89.38% (82.18-94.39)\u00a0| 94.35% (88.71-97.70)\u00a0\u00a0|\n| Other\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 88.89% (77.37-95.81)\u00a0| 84.00% (70.89-92.83)\u00a0\u00a0|\n\n| Minimal Volume Threshold (ml)\u00a0\u00a0| Sensi7vity above Threshold (95% CI)\u00a0\u00a0|\n|---------------------------------|---------------------------------------|\n| vol \u2265 0 ml\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 89.22% (83.50-93.49)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 6. Summary of the sensi0vity for the subsets of scans stra0fied by ICH volume\n\n| vol \u2265 0.4ml\u00a0\u00a0| 94.59% (89.63-97.64)\u00a0\u00a0|\n|---------------|------------------------|\n| vol \u2265 1ml\u00a0\u00a0\u00a0| 97.04% (92.59-99.19)\u00a0\u00a0|\n| vol \u2265 5ml\u00a0\u00a0\u00a0| 99.04% (94.76-99.98)\u00a0\u00a0|\n\nTo assess the secondary outcome measure, the device's time to notification was recorded for a sample of cases. This ranged from 50 seconds to 126 seconds, and is substantially lower than the average time to notification seen in the Standard of Care reported by the predicate device (38.2 minutes).\n\nIn summary, the clinical tests demonstrate that the device performs with good sensitivity and specificity to detect acute ICH from non-contrast CT imaging, and met the primary study endpoint of 80% sensitivity and specificity. This acceptability criterion was the same as used for the predicate device (Viz ICH, K193658), supporting the equivalent performance to the predicate. Furthermore, the device's time to notification ranged from 50-126 seconds which was significantly shorter than the average time to notification seen in the Standard of Care of 38.2 minutes reported by the predicate device.\n\n## Brainomix Limited\n\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\nAnswer the following question: What was the performance of the algorithm on Males?\n"], "reference": ["Males had a sensitivity of 93.18% (85.75-97.46)\u00a0and specificity of 91.30% (83.58-96.17).\n"]} +{"qna_file": "compositional_skills/extraction/fda_filing/markdown/qna.yaml", "question_id": "337596308285671025544428380592587653878", "category": "taxonomy", "turns": ["Given the context below:\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\n## Performance Data\n\nA retrospective study has been carried out to assess the standalone performance of the image analysis algorithm and notification functionality of Triage ICH. The study evaluated the Triage ICH image analysis algorithm in terms of sensitivity and specificity with respect to a ground truth, as established by experienced US board certified neuroradiologists, in the detection of intracranial hemorrhage (ICH) in the brain. In addition, the study reported and compared the time to notification for the Triage ICH device with respect to the time to notification for the standard of care as established by the predicate device.\n\nA sample size of 341 non-contrast Computed Tomography (NCCT) scans (studies) were obtained from 30 different hospitals and clinics in the U.S. The majority of patients were scanned at Boston Medical Centre (N=237) and the remainder came from 29 different referral hospitals in MassachuseTs State. The patient cohort was enriched to ensure an approximately equal balance of ICH positive and negative studies and to ensure the distribution of clinical and demographic variables (e.g., age, gender, ICH subtype, traumatic vs non traumatic etiology) allows generalizability to the patient population for whom use is intended.\n\nThe cases (n=341) were all successfully processed with the algorithm. The confusion matrix was as follows: True Positives (TP): 149, True Negatives (TN): 159, False Positives (FP): 15, False Negatives (FN): 18. The overall performance can also be summarized with the following metrics: sensitivity (or positive percentage agreement, defined as TP/[TP+FN]) was 89.22%, specificity (or negative percentage agreement, defined as TN/[TN+FP]) was 91.37%. The receiver operating curve (ROC) for the device is shown in Figure 1.\n\nFigure 1. Receiver Opera0ng Curve for the device, with the opera0ng point (sensi0vity 89.2%, specificity 91.4%) shown with the red triangle.\n\nBecause the lower bound of each confidence interval for sensitivity and specificity exceeded 80%, the study met the pre-specified performance goals.\n\nAs part of a secondary analysis, the company stratified the device performance by various confounding variables: ICH subtype (Table 1); by gender (Table 2); by age (Table 3); by slice thickness (Table 4); by clinical site (Table 5); and by ICH Volume (Table 6).\n\nTable 1. Summary of the performance metrics for the subsets of scans stra0fied by ICH Subtype.\n\n| ICH Subtype\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0|\n|-----------------------------------|------------------------|\n| Intraparenchymal Hemorrhage (IPH) | 96.61% (88.29-99.59)\u00a0\u00a0|\n| Intraventricular Hemorrhage (IVH) | 100.00% (59.04-100.00) |\n| Subarachnoid Hemorrhage (SAH)\u00a0\u00a0\u00a0| 35.71% (12.76-64.86)\u00a0\u00a0|\n| Subdural Hemorrhage (SDH)\u00a0\u00a0\u00a0\u00a0\u00a0| 66.67% (40.99-86.66)\u00a0\u00a0|\n| Mul7ple Types\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 98.55% (92.19-99.96)\u00a0\u00a0|\n\nTable 2. Summary of the performance metrics for the subsets of scans stra0fied by gender.\n\n| Gender\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|----------|-----------------------|------------------------|\n| Male\u00a0\u00a0\u00a0| 93.18% (85.75-97.46)\u00a0| 91.30% (83.58-96.17)\u00a0\u00a0|\n| Female\u00a0\u00a0| 84.81% (74.97-91.90)\u00a0| 91.46% (83.20-96.50)\u00a0\u00a0|\n\nTable 3. Summary of the performance metrics for the subsets of scans stra0fied by age group.\n\n| Age\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|---------------|-----------------------|------------------------|\n| 21 < Age < 50 | 83.33% (67.19-93.63)\u00a0| 88.73% (79.00-95.01)\u00a0\u00a0|\n| 50 \u2264 Age < 70 | 92.75% (83.89-97.61)\u00a0| 91.25% (82.80-96.41)\u00a0\u00a0|\n| Age \u2265 70\u00a0\u00a0\u00a0| 88.71% (78.11-95.34)\u00a0| 100.00% (85.18-100.00) |\n\nTable 4. Summary of the performance metrics for the subsets of scans stra0fied by slice thickness\n\n| Slice Thickness\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|--------------------------------|-----------------------|------------------------|\n| Slice Thickness < 1.5 mm\u00a0\u00a0\u00a0\u00a0| 87.50% (79.92-92.99)\u00a0| 94.21% (88.44-97.64)\u00a0\u00a0|\n| 1.5mm \u2264 Slice Thickness < 3 mm | 95.65% (78.05-99.89)\u00a0| 100.00% (82.35-100.00) |\n| Slice Thickness \u2265 3 mm\u00a0\u00a0\u00a0\u00a0\u00a0| 90.62% (74.98-98.02)\u00a0| 76.47% (58.83-89.25)\u00a0\u00a0|\n\nTable 5. Summary of the performance metrics for the subsets of scans stra0fied by referring hospital\n\n| Clinical Site\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|-----------------------|-----------------------|------------------------|\n| Boston Medical Centre | 89.38% (82.18-94.39)\u00a0| 94.35% (88.71-97.70)\u00a0\u00a0|\n| Other\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 88.89% (77.37-95.81)\u00a0| 84.00% (70.89-92.83)\u00a0\u00a0|\n\n| Minimal Volume Threshold (ml)\u00a0\u00a0| Sensi7vity above Threshold (95% CI)\u00a0\u00a0|\n|---------------------------------|---------------------------------------|\n| vol \u2265 0 ml\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 89.22% (83.50-93.49)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 6. Summary of the sensi0vity for the subsets of scans stra0fied by ICH volume\n\n| vol \u2265 0.4ml\u00a0\u00a0| 94.59% (89.63-97.64)\u00a0\u00a0|\n|---------------|------------------------|\n| vol \u2265 1ml\u00a0\u00a0\u00a0| 97.04% (92.59-99.19)\u00a0\u00a0|\n| vol \u2265 5ml\u00a0\u00a0\u00a0| 99.04% (94.76-99.98)\u00a0\u00a0|\n\nTo assess the secondary outcome measure, the device's time to notification was recorded for a sample of cases. This ranged from 50 seconds to 126 seconds, and is substantially lower than the average time to notification seen in the Standard of Care reported by the predicate device (38.2 minutes).\n\nIn summary, the clinical tests demonstrate that the device performs with good sensitivity and specificity to detect acute ICH from non-contrast CT imaging, and met the primary study endpoint of 80% sensitivity and specificity. This acceptability criterion was the same as used for the predicate device (Viz ICH, K193658), supporting the equivalent performance to the predicate. Furthermore, the device's time to notification ranged from 50-126 seconds which was significantly shorter than the average time to notification seen in the Standard of Care of 38.2 minutes reported by the predicate device.\n\n## Brainomix Limited\n\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\nAnswer the following question: Extract all the named entities that belong to the following entity types: Location, Institution, Company. Return the entities and their corresponding types in a markdown table with two columns: Entity Name and Entity Type.\n"], "reference": ["| \\*\\*Entity Name\\*\\*\u00a0\u00a0\u00a0\u00a0| \\*\\*Entity Type\\*\\* |\n|-----------------------|-----------------|\n| Brainomix\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Institution\u00a0\u00a0\u00a0|\n| Oxford\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Location\u00a0\u00a0\u00a0\u00a0|\n| United Kingdom\u00a0\u00a0\u00a0\u00a0| Location\u00a0\u00a0\u00a0\u00a0|\n| US board\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Institution\u00a0\u00a0\u00a0|\n| U.S.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Location\u00a0\u00a0\u00a0\u00a0|\n| Boston Medical Centre | Institution\u00a0\u00a0\u00a0|\n"]} +{"qna_file": "compositional_skills/extraction/fda_filing/markdown/qna.yaml", "question_id": "154891701021275387378643570418796269442", "category": "taxonomy", "turns": ["Given the context below:\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\n## Performance Data\n\nA retrospective study has been carried out to assess the standalone performance of the image analysis algorithm and notification functionality of Triage ICH. The study evaluated the Triage ICH image analysis algorithm in terms of sensitivity and specificity with respect to a ground truth, as established by experienced US board certified neuroradiologists, in the detection of intracranial hemorrhage (ICH) in the brain. In addition, the study reported and compared the time to notification for the Triage ICH device with respect to the time to notification for the standard of care as established by the predicate device.\n\nA sample size of 341 non-contrast Computed Tomography (NCCT) scans (studies) were obtained from 30 different hospitals and clinics in the U.S. The majority of patients were scanned at Boston Medical Centre (N=237) and the remainder came from 29 different referral hospitals in MassachuseTs State. The patient cohort was enriched to ensure an approximately equal balance of ICH positive and negative studies and to ensure the distribution of clinical and demographic variables (e.g., age, gender, ICH subtype, traumatic vs non traumatic etiology) allows generalizability to the patient population for whom use is intended.\n\nThe cases (n=341) were all successfully processed with the algorithm. The confusion matrix was as follows: True Positives (TP): 149, True Negatives (TN): 159, False Positives (FP): 15, False Negatives (FN): 18. The overall performance can also be summarized with the following metrics: sensitivity (or positive percentage agreement, defined as TP/[TP+FN]) was 89.22%, specificity (or negative percentage agreement, defined as TN/[TN+FP]) was 91.37%. The receiver operating curve (ROC) for the device is shown in Figure 1.\n\nFigure 1. Receiver Opera0ng Curve for the device, with the opera0ng point (sensi0vity 89.2%, specificity 91.4%) shown with the red triangle.\n\nBecause the lower bound of each confidence interval for sensitivity and specificity exceeded 80%, the study met the pre-specified performance goals.\n\nAs part of a secondary analysis, the company stratified the device performance by various confounding variables: ICH subtype (Table 1); by gender (Table 2); by age (Table 3); by slice thickness (Table 4); by clinical site (Table 5); and by ICH Volume (Table 6).\n\nTable 1. Summary of the performance metrics for the subsets of scans stra0fied by ICH Subtype.\n\n| ICH Subtype\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0|\n|-----------------------------------|------------------------|\n| Intraparenchymal Hemorrhage (IPH) | 96.61% (88.29-99.59)\u00a0\u00a0|\n| Intraventricular Hemorrhage (IVH) | 100.00% (59.04-100.00) |\n| Subarachnoid Hemorrhage (SAH)\u00a0\u00a0\u00a0| 35.71% (12.76-64.86)\u00a0\u00a0|\n| Subdural Hemorrhage (SDH)\u00a0\u00a0\u00a0\u00a0\u00a0| 66.67% (40.99-86.66)\u00a0\u00a0|\n| Mul7ple Types\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 98.55% (92.19-99.96)\u00a0\u00a0|\n\nTable 2. Summary of the performance metrics for the subsets of scans stra0fied by gender.\n\n| Gender\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|----------|-----------------------|------------------------|\n| Male\u00a0\u00a0\u00a0| 93.18% (85.75-97.46)\u00a0| 91.30% (83.58-96.17)\u00a0\u00a0|\n| Female\u00a0\u00a0| 84.81% (74.97-91.90)\u00a0| 91.46% (83.20-96.50)\u00a0\u00a0|\n\nTable 3. Summary of the performance metrics for the subsets of scans stra0fied by age group.\n\n| Age\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|---------------|-----------------------|------------------------|\n| 21 < Age < 50 | 83.33% (67.19-93.63)\u00a0| 88.73% (79.00-95.01)\u00a0\u00a0|\n| 50 \u2264 Age < 70 | 92.75% (83.89-97.61)\u00a0| 91.25% (82.80-96.41)\u00a0\u00a0|\n| Age \u2265 70\u00a0\u00a0\u00a0| 88.71% (78.11-95.34)\u00a0| 100.00% (85.18-100.00) |\n\nTable 4. Summary of the performance metrics for the subsets of scans stra0fied by slice thickness\n\n| Slice Thickness\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|--------------------------------|-----------------------|------------------------|\n| Slice Thickness < 1.5 mm\u00a0\u00a0\u00a0\u00a0| 87.50% (79.92-92.99)\u00a0| 94.21% (88.44-97.64)\u00a0\u00a0|\n| 1.5mm \u2264 Slice Thickness < 3 mm | 95.65% (78.05-99.89)\u00a0| 100.00% (82.35-100.00) |\n| Slice Thickness \u2265 3 mm\u00a0\u00a0\u00a0\u00a0\u00a0| 90.62% (74.98-98.02)\u00a0| 76.47% (58.83-89.25)\u00a0\u00a0|\n\nTable 5. Summary of the performance metrics for the subsets of scans stra0fied by referring hospital\n\n| Clinical Site\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|-----------------------|-----------------------|------------------------|\n| Boston Medical Centre | 89.38% (82.18-94.39)\u00a0| 94.35% (88.71-97.70)\u00a0\u00a0|\n| Other\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 88.89% (77.37-95.81)\u00a0| 84.00% (70.89-92.83)\u00a0\u00a0|\n\n| Minimal Volume Threshold (ml)\u00a0\u00a0| Sensi7vity above Threshold (95% CI)\u00a0\u00a0|\n|---------------------------------|---------------------------------------|\n| vol \u2265 0 ml\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 89.22% (83.50-93.49)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 6. Summary of the sensi0vity for the subsets of scans stra0fied by ICH volume\n\n| vol \u2265 0.4ml\u00a0\u00a0| 94.59% (89.63-97.64)\u00a0\u00a0|\n|---------------|------------------------|\n| vol \u2265 1ml\u00a0\u00a0\u00a0| 97.04% (92.59-99.19)\u00a0\u00a0|\n| vol \u2265 5ml\u00a0\u00a0\u00a0| 99.04% (94.76-99.98)\u00a0\u00a0|\n\nTo assess the secondary outcome measure, the device's time to notification was recorded for a sample of cases. This ranged from 50 seconds to 126 seconds, and is substantially lower than the average time to notification seen in the Standard of Care reported by the predicate device (38.2 minutes).\n\nIn summary, the clinical tests demonstrate that the device performs with good sensitivity and specificity to detect acute ICH from non-contrast CT imaging, and met the primary study endpoint of 80% sensitivity and specificity. This acceptability criterion was the same as used for the predicate device (Viz ICH, K193658), supporting the equivalent performance to the predicate. Furthermore, the device's time to notification ranged from 50-126 seconds which was significantly shorter than the average time to notification seen in the Standard of Care of 38.2 minutes reported by the predicate device.\n\n## Brainomix Limited\n\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\nAnswer the following question: In markdown format, show me a table with all the results by age."], "reference": ["| Age\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensitivity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|---------------|-----------------------|------------------------|\n| 21 < Age < 50 | 83.33% (67.19-93.63)\u00a0| 88.73% (79.00-95.01)\u00a0\u00a0|\n| 50 \u2264 Age < 70 | 92.75% (83.89-97.61)\u00a0| 91.25% (82.80-96.41)\u00a0\u00a0|\n| Age \u2265 70\u00a0\u00a0\u00a0| 88.71% (78.11-95.34)\u00a0| 100.00% (85.18-100.00) |\n\n"]} +{"qna_file": "compositional_skills/extraction/fda_filing/markdown/qna.yaml", "question_id": "176632790082150003971260114482566702269", "category": "taxonomy", "turns": ["Given the context below:\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\n## Performance Data\n\nA retrospective study has been carried out to assess the standalone performance of the image analysis algorithm and notification functionality of Triage ICH. The study evaluated the Triage ICH image analysis algorithm in terms of sensitivity and specificity with respect to a ground truth, as established by experienced US board certified neuroradiologists, in the detection of intracranial hemorrhage (ICH) in the brain. In addition, the study reported and compared the time to notification for the Triage ICH device with respect to the time to notification for the standard of care as established by the predicate device.\n\nA sample size of 341 non-contrast Computed Tomography (NCCT) scans (studies) were obtained from 30 different hospitals and clinics in the U.S. The majority of patients were scanned at Boston Medical Centre (N=237) and the remainder came from 29 different referral hospitals in MassachuseTs State. The patient cohort was enriched to ensure an approximately equal balance of ICH positive and negative studies and to ensure the distribution of clinical and demographic variables (e.g., age, gender, ICH subtype, traumatic vs non traumatic etiology) allows generalizability to the patient population for whom use is intended.\n\nThe cases (n=341) were all successfully processed with the algorithm. The confusion matrix was as follows: True Positives (TP): 149, True Negatives (TN): 159, False Positives (FP): 15, False Negatives (FN): 18. The overall performance can also be summarized with the following metrics: sensitivity (or positive percentage agreement, defined as TP/[TP+FN]) was 89.22%, specificity (or negative percentage agreement, defined as TN/[TN+FP]) was 91.37%. The receiver operating curve (ROC) for the device is shown in Figure 1.\n\nFigure 1. Receiver Opera0ng Curve for the device, with the opera0ng point (sensi0vity 89.2%, specificity 91.4%) shown with the red triangle.\n\nBecause the lower bound of each confidence interval for sensitivity and specificity exceeded 80%, the study met the pre-specified performance goals.\n\nAs part of a secondary analysis, the company stratified the device performance by various confounding variables: ICH subtype (Table 1); by gender (Table 2); by age (Table 3); by slice thickness (Table 4); by clinical site (Table 5); and by ICH Volume (Table 6).\n\nTable 1. Summary of the performance metrics for the subsets of scans stra0fied by ICH Subtype.\n\n| ICH Subtype\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0|\n|-----------------------------------|------------------------|\n| Intraparenchymal Hemorrhage (IPH) | 96.61% (88.29-99.59)\u00a0\u00a0|\n| Intraventricular Hemorrhage (IVH) | 100.00% (59.04-100.00) |\n| Subarachnoid Hemorrhage (SAH)\u00a0\u00a0\u00a0| 35.71% (12.76-64.86)\u00a0\u00a0|\n| Subdural Hemorrhage (SDH)\u00a0\u00a0\u00a0\u00a0\u00a0| 66.67% (40.99-86.66)\u00a0\u00a0|\n| Mul7ple Types\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 98.55% (92.19-99.96)\u00a0\u00a0|\n\nTable 2. Summary of the performance metrics for the subsets of scans stra0fied by gender.\n\n| Gender\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|----------|-----------------------|------------------------|\n| Male\u00a0\u00a0\u00a0| 93.18% (85.75-97.46)\u00a0| 91.30% (83.58-96.17)\u00a0\u00a0|\n| Female\u00a0\u00a0| 84.81% (74.97-91.90)\u00a0| 91.46% (83.20-96.50)\u00a0\u00a0|\n\nTable 3. Summary of the performance metrics for the subsets of scans stra0fied by age group.\n\n| Age\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|---------------|-----------------------|------------------------|\n| 21 < Age < 50 | 83.33% (67.19-93.63)\u00a0| 88.73% (79.00-95.01)\u00a0\u00a0|\n| 50 \u2264 Age < 70 | 92.75% (83.89-97.61)\u00a0| 91.25% (82.80-96.41)\u00a0\u00a0|\n| Age \u2265 70\u00a0\u00a0\u00a0| 88.71% (78.11-95.34)\u00a0| 100.00% (85.18-100.00) |\n\nTable 4. Summary of the performance metrics for the subsets of scans stra0fied by slice thickness\n\n| Slice Thickness\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|--------------------------------|-----------------------|------------------------|\n| Slice Thickness < 1.5 mm\u00a0\u00a0\u00a0\u00a0| 87.50% (79.92-92.99)\u00a0| 94.21% (88.44-97.64)\u00a0\u00a0|\n| 1.5mm \u2264 Slice Thickness < 3 mm | 95.65% (78.05-99.89)\u00a0| 100.00% (82.35-100.00) |\n| Slice Thickness \u2265 3 mm\u00a0\u00a0\u00a0\u00a0\u00a0| 90.62% (74.98-98.02)\u00a0| 76.47% (58.83-89.25)\u00a0\u00a0|\n\nTable 5. Summary of the performance metrics for the subsets of scans stra0fied by referring hospital\n\n| Clinical Site\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|-----------------------|-----------------------|------------------------|\n| Boston Medical Centre | 89.38% (82.18-94.39)\u00a0| 94.35% (88.71-97.70)\u00a0\u00a0|\n| Other\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 88.89% (77.37-95.81)\u00a0| 84.00% (70.89-92.83)\u00a0\u00a0|\n\n| Minimal Volume Threshold (ml)\u00a0\u00a0| Sensi7vity above Threshold (95% CI)\u00a0\u00a0|\n|---------------------------------|---------------------------------------|\n| vol \u2265 0 ml\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 89.22% (83.50-93.49)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 6. Summary of the sensi0vity for the subsets of scans stra0fied by ICH volume\n\n| vol \u2265 0.4ml\u00a0\u00a0| 94.59% (89.63-97.64)\u00a0\u00a0|\n|---------------|------------------------|\n| vol \u2265 1ml\u00a0\u00a0\u00a0| 97.04% (92.59-99.19)\u00a0\u00a0|\n| vol \u2265 5ml\u00a0\u00a0\u00a0| 99.04% (94.76-99.98)\u00a0\u00a0|\n\nTo assess the secondary outcome measure, the device's time to notification was recorded for a sample of cases. This ranged from 50 seconds to 126 seconds, and is substantially lower than the average time to notification seen in the Standard of Care reported by the predicate device (38.2 minutes).\n\nIn summary, the clinical tests demonstrate that the device performs with good sensitivity and specificity to detect acute ICH from non-contrast CT imaging, and met the primary study endpoint of 80% sensitivity and specificity. This acceptability criterion was the same as used for the predicate device (Viz ICH, K193658), supporting the equivalent performance to the predicate. Furthermore, the device's time to notification ranged from 50-126 seconds which was significantly shorter than the average time to notification seen in the Standard of Care of 38.2 minutes reported by the predicate device.\n\n## Brainomix Limited\n\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\nAnswer the following question: Create a table that is an index of all the tables in this document. The column names should be \"Table Number\" and \"Table Description\". Use markdown formatting\n"], "reference": ["| \\*\\*Table Number\\*\\* | \\*\\*Table Description\\*\\*\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|------------------|---------------------------------------------------------------------------------------------|\n| Table 1\u00a0\u00a0\u00a0\u00a0\u00a0| Summary of the performance metrics for the subsets of scans stratified by ICH Subtype.\u00a0\u00a0\u00a0\u00a0|\n| Table 2\u00a0\u00a0\u00a0\u00a0\u00a0| Summary of the performance metrics for the subsets of scans stratified by gender.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Table 3\u00a0\u00a0\u00a0\u00a0\u00a0| Summary of the performance metrics for the subsets of scans stratified by age group.\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Table 4\u00a0\u00a0\u00a0\u00a0\u00a0| Summary of the performance metrics for the subsets of scans stratified by slice thickness\u00a0\u00a0|\n| Table 5\u00a0\u00a0\u00a0\u00a0\u00a0| Summary of the performance metrics for the subsets of scans stratified by referring hospital |\n| Table 6\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0Summary of the sensitivity for the subsets of scans stratified by ICH volume\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n"]} +{"qna_file": "compositional_skills/extraction/fda_filing/reasoning/qna.yaml", "question_id": "13190131703934170923390151936927251349", "category": "taxonomy", "turns": ["Given the context below:\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\n## Performance Data\n\nA retrospective study has been carried out to assess the standalone performance of the image analysis algorithm and notification functionality of Triage ICH. The study evaluated the Triage ICH image analysis algorithm in terms of sensitivity and specificity with respect to a ground truth, as established by experienced US board certified neuroradiologists, in the detection of intracranial hemorrhage (ICH) in the brain. In addition, the study reported and compared the time to notification for the Triage ICH device with respect to the time to notification for the standard of care as established by the predicate device.\n\nA sample size of 341 non-contrast Computed Tomography (NCCT) scans (studies) were obtained from 30 different hospitals and clinics in the U.S. The majority of patients were scanned at Boston Medical Centre (N=237) and the remainder came from 29 different referral hospitals in MassachuseTs State. The patient cohort was enriched to ensure an approximately equal balance of ICH positive and negative studies and to ensure the distribution of clinical and demographic variables (e.g., age, gender, ICH subtype, traumatic vs non traumatic etiology) allows generalizability to the patient population for whom use is intended.\n\nThe cases (n=341) were all successfully processed with the algorithm. The confusion matrix was as follows: True Positives (TP): 149, True Negatives (TN): 159, False Positives (FP): 15, False Negatives (FN): 18. The overall performance can also be summarized with the following metrics: sensitivity (or positive percentage agreement, defined as TP/[TP+FN]) was 89.22%, specificity (or negative percentage agreement, defined as TN/[TN+FP]) was 91.37%. The receiver operating curve (ROC) for the device is shown in Figure 1.\n\nFigure 1. Receiver Opera0ng Curve for the device, with the opera0ng point (sensi0vity 89.2%, specificity 91.4%) shown with the red triangle.\n\nBecause the lower bound of each confidence interval for sensitivity and specificity exceeded 80%, the study met the pre-specified performance goals.\n\nAs part of a secondary analysis, the company stratified the device performance by various confounding variables: ICH subtype (Table 1); by gender (Table 2); by age (Table 3); by slice thickness (Table 4); by clinical site (Table 5); and by ICH Volume (Table 6).\n\nTable 1. Summary of the performance metrics for the subsets of scans stra0fied by ICH Subtype.\n\n| ICH Subtype\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0|\n|-----------------------------------|------------------------|\n| Intraparenchymal Hemorrhage (IPH) | 96.61% (88.29-99.59)\u00a0\u00a0|\n| Intraventricular Hemorrhage (IVH) | 100.00% (59.04-100.00) |\n| Subarachnoid Hemorrhage (SAH)\u00a0\u00a0\u00a0| 35.71% (12.76-64.86)\u00a0\u00a0|\n| Subdural Hemorrhage (SDH)\u00a0\u00a0\u00a0\u00a0\u00a0| 66.67% (40.99-86.66)\u00a0\u00a0|\n| Mul7ple Types\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 98.55% (92.19-99.96)\u00a0\u00a0|\n\nTable 2. Summary of the performance metrics for the subsets of scans stra0fied by gender.\n\n| Gender\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|----------|-----------------------|------------------------|\n| Male\u00a0\u00a0\u00a0| 93.18% (85.75-97.46)\u00a0| 91.30% (83.58-96.17)\u00a0\u00a0|\n| Female\u00a0\u00a0| 84.81% (74.97-91.90)\u00a0| 91.46% (83.20-96.50)\u00a0\u00a0|\n\nTable 3. Summary of the performance metrics for the subsets of scans stra0fied by age group.\n\n| Age\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|---------------|-----------------------|------------------------|\n| 21 < Age < 50 | 83.33% (67.19-93.63)\u00a0| 88.73% (79.00-95.01)\u00a0\u00a0|\n| 50 \u2264 Age < 70 | 92.75% (83.89-97.61)\u00a0| 91.25% (82.80-96.41)\u00a0\u00a0|\n| Age \u2265 70\u00a0\u00a0\u00a0| 88.71% (78.11-95.34)\u00a0| 100.00% (85.18-100.00) |\n\nTable 4. Summary of the performance metrics for the subsets of scans stra0fied by slice thickness\n\n| Slice Thickness\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|--------------------------------|-----------------------|------------------------|\n| Slice Thickness < 1.5 mm\u00a0\u00a0\u00a0\u00a0| 87.50% (79.92-92.99)\u00a0| 94.21% (88.44-97.64)\u00a0\u00a0|\n| 1.5mm \u2264 Slice Thickness < 3 mm | 95.65% (78.05-99.89)\u00a0| 100.00% (82.35-100.00) |\n| Slice Thickness \u2265 3 mm\u00a0\u00a0\u00a0\u00a0\u00a0| 90.62% (74.98-98.02)\u00a0| 76.47% (58.83-89.25)\u00a0\u00a0|\n\nTable 5. Summary of the performance metrics for the subsets of scans stra0fied by referring hospital\n\n| Clinical Site\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|-----------------------|-----------------------|------------------------|\n| Boston Medical Centre | 89.38% (82.18-94.39)\u00a0| 94.35% (88.71-97.70)\u00a0\u00a0|\n| Other\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 88.89% (77.37-95.81)\u00a0| 84.00% (70.89-92.83)\u00a0\u00a0|\n\n| Minimal Volume Threshold (ml)\u00a0\u00a0| Sensi7vity above Threshold (95% CI)\u00a0\u00a0|\n|---------------------------------|---------------------------------------|\n| vol \u2265 0 ml\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 89.22% (83.50-93.49)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 6. Summary of the sensi0vity for the subsets of scans stra0fied by ICH volume\n\n| vol \u2265 0.4ml\u00a0\u00a0| 94.59% (89.63-97.64)\u00a0\u00a0|\n|---------------|------------------------|\n| vol \u2265 1ml\u00a0\u00a0\u00a0| 97.04% (92.59-99.19)\u00a0\u00a0|\n| vol \u2265 5ml\u00a0\u00a0\u00a0| 99.04% (94.76-99.98)\u00a0\u00a0|\n\nTo assess the secondary outcome measure, the device's time to notification was recorded for a sample of cases. This ranged from 50 seconds to 126 seconds, and is substantially lower than the average time to notification seen in the Standard of Care reported by the predicate device (38.2 minutes).\n\nIn summary, the clinical tests demonstrate that the device performs with good sensitivity and specificity to detect acute ICH from non-contrast CT imaging, and met the primary study endpoint of 80% sensitivity and specificity. This acceptability criterion was the same as used for the predicate device (Viz ICH, K193658), supporting the equivalent performance to the predicate. Furthermore, the device's time to notification ranged from 50-126 seconds which was significantly shorter than the average time to notification seen in the Standard of Care of 38.2 minutes reported by the predicate device.\n\n## Brainomix Limited\n\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\nAnswer the following question: Does the image analysis algorithm work well for all studied types of ICH?\n"], "reference": ["According to this document, the acceptability criterion for the study was 80% sensitivity and specificity. The study breaks down sensitivity by ICH subtype in Table 1, where subtypes of IPH, IVH, and Multiple Types are shown to exceed 80% sensitivity, but SAH and SDH have subpar performance with 35.71% and 66.67%, respectively. Therefore it does not appear that this image analysis algorithm works well for all studied types of ICH.\n"]} +{"qna_file": "compositional_skills/extraction/fda_filing/reasoning/qna.yaml", "question_id": "192001875883867509792645995035423322628", "category": "taxonomy", "turns": ["Given the context below:\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\n## Performance Data\n\nA retrospective study has been carried out to assess the standalone performance of the image analysis algorithm and notification functionality of Triage ICH. The study evaluated the Triage ICH image analysis algorithm in terms of sensitivity and specificity with respect to a ground truth, as established by experienced US board certified neuroradiologists, in the detection of intracranial hemorrhage (ICH) in the brain. In addition, the study reported and compared the time to notification for the Triage ICH device with respect to the time to notification for the standard of care as established by the predicate device.\n\nA sample size of 341 non-contrast Computed Tomography (NCCT) scans (studies) were obtained from 30 different hospitals and clinics in the U.S. The majority of patients were scanned at Boston Medical Centre (N=237) and the remainder came from 29 different referral hospitals in MassachuseTs State. The patient cohort was enriched to ensure an approximately equal balance of ICH positive and negative studies and to ensure the distribution of clinical and demographic variables (e.g., age, gender, ICH subtype, traumatic vs non traumatic etiology) allows generalizability to the patient population for whom use is intended.\n\nThe cases (n=341) were all successfully processed with the algorithm. The confusion matrix was as follows: True Positives (TP): 149, True Negatives (TN): 159, False Positives (FP): 15, False Negatives (FN): 18. The overall performance can also be summarized with the following metrics: sensitivity (or positive percentage agreement, defined as TP/[TP+FN]) was 89.22%, specificity (or negative percentage agreement, defined as TN/[TN+FP]) was 91.37%. The receiver operating curve (ROC) for the device is shown in Figure 1.\n\nFigure 1. Receiver Opera0ng Curve for the device, with the opera0ng point (sensi0vity 89.2%, specificity 91.4%) shown with the red triangle.\n\nBecause the lower bound of each confidence interval for sensitivity and specificity exceeded 80%, the study met the pre-specified performance goals.\n\nAs part of a secondary analysis, the company stratified the device performance by various confounding variables: ICH subtype (Table 1); by gender (Table 2); by age (Table 3); by slice thickness (Table 4); by clinical site (Table 5); and by ICH Volume (Table 6).\n\nTable 1. Summary of the performance metrics for the subsets of scans stra0fied by ICH Subtype.\n\n| ICH Subtype\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0|\n|-----------------------------------|------------------------|\n| Intraparenchymal Hemorrhage (IPH) | 96.61% (88.29-99.59)\u00a0\u00a0|\n| Intraventricular Hemorrhage (IVH) | 100.00% (59.04-100.00) |\n| Subarachnoid Hemorrhage (SAH)\u00a0\u00a0\u00a0| 35.71% (12.76-64.86)\u00a0\u00a0|\n| Subdural Hemorrhage (SDH)\u00a0\u00a0\u00a0\u00a0\u00a0| 66.67% (40.99-86.66)\u00a0\u00a0|\n| Mul7ple Types\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 98.55% (92.19-99.96)\u00a0\u00a0|\n\nTable 2. Summary of the performance metrics for the subsets of scans stra0fied by gender.\n\n| Gender\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|----------|-----------------------|------------------------|\n| Male\u00a0\u00a0\u00a0| 93.18% (85.75-97.46)\u00a0| 91.30% (83.58-96.17)\u00a0\u00a0|\n| Female\u00a0\u00a0| 84.81% (74.97-91.90)\u00a0| 91.46% (83.20-96.50)\u00a0\u00a0|\n\nTable 3. Summary of the performance metrics for the subsets of scans stra0fied by age group.\n\n| Age\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|---------------|-----------------------|------------------------|\n| 21 < Age < 50 | 83.33% (67.19-93.63)\u00a0| 88.73% (79.00-95.01)\u00a0\u00a0|\n| 50 \u2264 Age < 70 | 92.75% (83.89-97.61)\u00a0| 91.25% (82.80-96.41)\u00a0\u00a0|\n| Age \u2265 70\u00a0\u00a0\u00a0| 88.71% (78.11-95.34)\u00a0| 100.00% (85.18-100.00) |\n\nTable 4. Summary of the performance metrics for the subsets of scans stra0fied by slice thickness\n\n| Slice Thickness\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|--------------------------------|-----------------------|------------------------|\n| Slice Thickness < 1.5 mm\u00a0\u00a0\u00a0\u00a0| 87.50% (79.92-92.99)\u00a0| 94.21% (88.44-97.64)\u00a0\u00a0|\n| 1.5mm \u2264 Slice Thickness < 3 mm | 95.65% (78.05-99.89)\u00a0| 100.00% (82.35-100.00) |\n| Slice Thickness \u2265 3 mm\u00a0\u00a0\u00a0\u00a0\u00a0| 90.62% (74.98-98.02)\u00a0| 76.47% (58.83-89.25)\u00a0\u00a0|\n\nTable 5. Summary of the performance metrics for the subsets of scans stra0fied by referring hospital\n\n| Clinical Site\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|-----------------------|-----------------------|------------------------|\n| Boston Medical Centre | 89.38% (82.18-94.39)\u00a0| 94.35% (88.71-97.70)\u00a0\u00a0|\n| Other\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 88.89% (77.37-95.81)\u00a0| 84.00% (70.89-92.83)\u00a0\u00a0|\n\n| Minimal Volume Threshold (ml)\u00a0\u00a0| Sensi7vity above Threshold (95% CI)\u00a0\u00a0|\n|---------------------------------|---------------------------------------|\n| vol \u2265 0 ml\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 89.22% (83.50-93.49)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 6. Summary of the sensi0vity for the subsets of scans stra0fied by ICH volume\n\n| vol \u2265 0.4ml\u00a0\u00a0| 94.59% (89.63-97.64)\u00a0\u00a0|\n|---------------|------------------------|\n| vol \u2265 1ml\u00a0\u00a0\u00a0| 97.04% (92.59-99.19)\u00a0\u00a0|\n| vol \u2265 5ml\u00a0\u00a0\u00a0| 99.04% (94.76-99.98)\u00a0\u00a0|\n\nTo assess the secondary outcome measure, the device's time to notification was recorded for a sample of cases. This ranged from 50 seconds to 126 seconds, and is substantially lower than the average time to notification seen in the Standard of Care reported by the predicate device (38.2 minutes).\n\nIn summary, the clinical tests demonstrate that the device performs with good sensitivity and specificity to detect acute ICH from non-contrast CT imaging, and met the primary study endpoint of 80% sensitivity and specificity. This acceptability criterion was the same as used for the predicate device (Viz ICH, K193658), supporting the equivalent performance to the predicate. Furthermore, the device's time to notification ranged from 50-126 seconds which was significantly shorter than the average time to notification seen in the Standard of Care of 38.2 minutes reported by the predicate device.\n\n## Brainomix Limited\n\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\nAnswer the following question: Did the clinical site have an impact on the algorithm's performance?"], "reference": ["According to Table 5, Boston Medical Centre had a similar sensitivity of results, 89.38% (82.18-94.39), compared to other hospitals, 88.89% (77.37-95.81). However, it had a slightly higher specificity of results, 94.35% (88.71-97.70), compared to other hospitals, 84.00% (70.89-92.83). This difference in specificity does not appear to be significant at the 95% level as there is overlap in the confidence intervals of the two specificities.\n"]} +{"qna_file": "compositional_skills/extraction/fda_filing/reasoning/qna.yaml", "question_id": "291359785972007148714346311538552655819", "category": "taxonomy", "turns": ["Given the context below:\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\n## Performance Data\n\nA retrospective study has been carried out to assess the standalone performance of the image analysis algorithm and notification functionality of Triage ICH. The study evaluated the Triage ICH image analysis algorithm in terms of sensitivity and specificity with respect to a ground truth, as established by experienced US board certified neuroradiologists, in the detection of intracranial hemorrhage (ICH) in the brain. In addition, the study reported and compared the time to notification for the Triage ICH device with respect to the time to notification for the standard of care as established by the predicate device.\n\nA sample size of 341 non-contrast Computed Tomography (NCCT) scans (studies) were obtained from 30 different hospitals and clinics in the U.S. The majority of patients were scanned at Boston Medical Centre (N=237) and the remainder came from 29 different referral hospitals in MassachuseTs State. The patient cohort was enriched to ensure an approximately equal balance of ICH positive and negative studies and to ensure the distribution of clinical and demographic variables (e.g., age, gender, ICH subtype, traumatic vs non traumatic etiology) allows generalizability to the patient population for whom use is intended.\n\nThe cases (n=341) were all successfully processed with the algorithm. The confusion matrix was as follows: True Positives (TP): 149, True Negatives (TN): 159, False Positives (FP): 15, False Negatives (FN): 18. The overall performance can also be summarized with the following metrics: sensitivity (or positive percentage agreement, defined as TP/[TP+FN]) was 89.22%, specificity (or negative percentage agreement, defined as TN/[TN+FP]) was 91.37%. The receiver operating curve (ROC) for the device is shown in Figure 1.\n\nFigure 1. Receiver Opera0ng Curve for the device, with the opera0ng point (sensi0vity 89.2%, specificity 91.4%) shown with the red triangle.\n\nBecause the lower bound of each confidence interval for sensitivity and specificity exceeded 80%, the study met the pre-specified performance goals.\n\nAs part of a secondary analysis, the company stratified the device performance by various confounding variables: ICH subtype (Table 1); by gender (Table 2); by age (Table 3); by slice thickness (Table 4); by clinical site (Table 5); and by ICH Volume (Table 6).\n\nTable 1. Summary of the performance metrics for the subsets of scans stra0fied by ICH Subtype.\n\n| ICH Subtype\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0|\n|-----------------------------------|------------------------|\n| Intraparenchymal Hemorrhage (IPH) | 96.61% (88.29-99.59)\u00a0\u00a0|\n| Intraventricular Hemorrhage (IVH) | 100.00% (59.04-100.00) |\n| Subarachnoid Hemorrhage (SAH)\u00a0\u00a0\u00a0| 35.71% (12.76-64.86)\u00a0\u00a0|\n| Subdural Hemorrhage (SDH)\u00a0\u00a0\u00a0\u00a0\u00a0| 66.67% (40.99-86.66)\u00a0\u00a0|\n| Mul7ple Types\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 98.55% (92.19-99.96)\u00a0\u00a0|\n\nTable 2. Summary of the performance metrics for the subsets of scans stra0fied by gender.\n\n| Gender\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|----------|-----------------------|------------------------|\n| Male\u00a0\u00a0\u00a0| 93.18% (85.75-97.46)\u00a0| 91.30% (83.58-96.17)\u00a0\u00a0|\n| Female\u00a0\u00a0| 84.81% (74.97-91.90)\u00a0| 91.46% (83.20-96.50)\u00a0\u00a0|\n\nTable 3. Summary of the performance metrics for the subsets of scans stra0fied by age group.\n\n| Age\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|---------------|-----------------------|------------------------|\n| 21 < Age < 50 | 83.33% (67.19-93.63)\u00a0| 88.73% (79.00-95.01)\u00a0\u00a0|\n| 50 \u2264 Age < 70 | 92.75% (83.89-97.61)\u00a0| 91.25% (82.80-96.41)\u00a0\u00a0|\n| Age \u2265 70\u00a0\u00a0\u00a0| 88.71% (78.11-95.34)\u00a0| 100.00% (85.18-100.00) |\n\nTable 4. Summary of the performance metrics for the subsets of scans stra0fied by slice thickness\n\n| Slice Thickness\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|--------------------------------|-----------------------|------------------------|\n| Slice Thickness < 1.5 mm\u00a0\u00a0\u00a0\u00a0| 87.50% (79.92-92.99)\u00a0| 94.21% (88.44-97.64)\u00a0\u00a0|\n| 1.5mm \u2264 Slice Thickness < 3 mm | 95.65% (78.05-99.89)\u00a0| 100.00% (82.35-100.00) |\n| Slice Thickness \u2265 3 mm\u00a0\u00a0\u00a0\u00a0\u00a0| 90.62% (74.98-98.02)\u00a0| 76.47% (58.83-89.25)\u00a0\u00a0|\n\nTable 5. Summary of the performance metrics for the subsets of scans stra0fied by referring hospital\n\n| Clinical Site\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|-----------------------|-----------------------|------------------------|\n| Boston Medical Centre | 89.38% (82.18-94.39)\u00a0| 94.35% (88.71-97.70)\u00a0\u00a0|\n| Other\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 88.89% (77.37-95.81)\u00a0| 84.00% (70.89-92.83)\u00a0\u00a0|\n\n| Minimal Volume Threshold (ml)\u00a0\u00a0| Sensi7vity above Threshold (95% CI)\u00a0\u00a0|\n|---------------------------------|---------------------------------------|\n| vol \u2265 0 ml\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 89.22% (83.50-93.49)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 6. Summary of the sensi0vity for the subsets of scans stra0fied by ICH volume\n\n| vol \u2265 0.4ml\u00a0\u00a0| 94.59% (89.63-97.64)\u00a0\u00a0|\n|---------------|------------------------|\n| vol \u2265 1ml\u00a0\u00a0\u00a0| 97.04% (92.59-99.19)\u00a0\u00a0|\n| vol \u2265 5ml\u00a0\u00a0\u00a0| 99.04% (94.76-99.98)\u00a0\u00a0|\n\nTo assess the secondary outcome measure, the device's time to notification was recorded for a sample of cases. This ranged from 50 seconds to 126 seconds, and is substantially lower than the average time to notification seen in the Standard of Care reported by the predicate device (38.2 minutes).\n\nIn summary, the clinical tests demonstrate that the device performs with good sensitivity and specificity to detect acute ICH from non-contrast CT imaging, and met the primary study endpoint of 80% sensitivity and specificity. This acceptability criterion was the same as used for the predicate device (Viz ICH, K193658), supporting the equivalent performance to the predicate. Furthermore, the device's time to notification ranged from 50-126 seconds which was significantly shorter than the average time to notification seen in the Standard of Care of 38.2 minutes reported by the predicate device.\n\n## Brainomix Limited\n\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\nAnswer the following question: Which slice thickness in this study was best?\n"], "reference": ["Slice thicknesses between 1.5mm and 3mm appear to have performed the best, with a sensitivity of 95.65% (78.05-99.89)\u00a0and specificity of 100.00% (82.35-100.00).\n"]} +{"qna_file": "compositional_skills/extraction/fda_filing/csv/qna.yaml", "question_id": "287444913151913100716199558424586008573", "category": "taxonomy", "turns": ["Given the context below:\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\n## Performance Data\n\nA retrospective study has been carried out to assess the standalone performance of the image analysis algorithm and notification functionality of Triage ICH. The study evaluated the Triage ICH image analysis algorithm in terms of sensitivity and specificity with respect to a ground truth, as established by experienced US board certified neuroradiologists, in the detection of intracranial hemorrhage (ICH) in the brain. In addition, the study reported and compared the time to notification for the Triage ICH device with respect to the time to notification for the standard of care as established by the predicate device.\n\nA sample size of 341 non-contrast Computed Tomography (NCCT) scans (studies) were obtained from 30 different hospitals and clinics in the U.S. The majority of patients were scanned at Boston Medical Centre (N=237) and the remainder came from 29 different referral hospitals in MassachuseTs State. The patient cohort was enriched to ensure an approximately equal balance of ICH positive and negative studies and to ensure the distribution of clinical and demographic variables (e.g., age, gender, ICH subtype, traumatic vs non traumatic etiology) allows generalizability to the patient population for whom use is intended.\n\nThe cases (n=341) were all successfully processed with the algorithm. The confusion matrix was as follows: True Positives (TP): 149, True Negatives (TN): 159, False Positives (FP): 15, False Negatives (FN): 18. The overall performance can also be summarized with the following metrics: sensitivity (or positive percentage agreement, defined as TP/[TP+FN]) was 89.22%, specificity (or negative percentage agreement, defined as TN/[TN+FP]) was 91.37%. The receiver operating curve (ROC) for the device is shown in Figure 1.\n\nFigure 1. Receiver Opera0ng Curve for the device, with the opera0ng point (sensi0vity 89.2%, specificity 91.4%) shown with the red triangle.\n\nBecause the lower bound of each confidence interval for sensitivity and specificity exceeded 80%, the study met the pre-specified performance goals.\n\nAs part of a secondary analysis, the company stratified the device performance by various confounding variables: ICH subtype (Table 1); by gender (Table 2); by age (Table 3); by slice thickness (Table 4); by clinical site (Table 5); and by ICH Volume (Table 6).\n\nTable 1. Summary of the performance metrics for the subsets of scans stra0fied by ICH Subtype.\n\n| ICH Subtype\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0|\n|-----------------------------------|------------------------|\n| Intraparenchymal Hemorrhage (IPH) | 96.61% (88.29-99.59)\u00a0\u00a0|\n| Intraventricular Hemorrhage (IVH) | 100.00% (59.04-100.00) |\n| Subarachnoid Hemorrhage (SAH)\u00a0\u00a0\u00a0| 35.71% (12.76-64.86)\u00a0\u00a0|\n| Subdural Hemorrhage (SDH)\u00a0\u00a0\u00a0\u00a0\u00a0| 66.67% (40.99-86.66)\u00a0\u00a0|\n| Mul7ple Types\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 98.55% (92.19-99.96)\u00a0\u00a0|\n\nTable 2. Summary of the performance metrics for the subsets of scans stra0fied by gender.\n\n| Gender\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|----------|-----------------------|------------------------|\n| Male\u00a0\u00a0\u00a0| 93.18% (85.75-97.46)\u00a0| 91.30% (83.58-96.17)\u00a0\u00a0|\n| Female\u00a0\u00a0| 84.81% (74.97-91.90)\u00a0| 91.46% (83.20-96.50)\u00a0\u00a0|\n\nTable 3. Summary of the performance metrics for the subsets of scans stra0fied by age group.\n\n| Age\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|---------------|-----------------------|------------------------|\n| 21 < Age < 50 | 83.33% (67.19-93.63)\u00a0| 88.73% (79.00-95.01)\u00a0\u00a0|\n| 50 \u2264 Age < 70 | 92.75% (83.89-97.61)\u00a0| 91.25% (82.80-96.41)\u00a0\u00a0|\n| Age \u2265 70\u00a0\u00a0\u00a0| 88.71% (78.11-95.34)\u00a0| 100.00% (85.18-100.00) |\n\nTable 4. Summary of the performance metrics for the subsets of scans stra0fied by slice thickness\n\n| Slice Thickness\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|--------------------------------|-----------------------|------------------------|\n| Slice Thickness < 1.5 mm\u00a0\u00a0\u00a0\u00a0| 87.50% (79.92-92.99)\u00a0| 94.21% (88.44-97.64)\u00a0\u00a0|\n| 1.5mm \u2264 Slice Thickness < 3 mm | 95.65% (78.05-99.89)\u00a0| 100.00% (82.35-100.00) |\n| Slice Thickness \u2265 3 mm\u00a0\u00a0\u00a0\u00a0\u00a0| 90.62% (74.98-98.02)\u00a0| 76.47% (58.83-89.25)\u00a0\u00a0|\n\nTable 5. Summary of the performance metrics for the subsets of scans stra0fied by referring hospital\n\n| Clinical Site\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|-----------------------|-----------------------|------------------------|\n| Boston Medical Centre | 89.38% (82.18-94.39)\u00a0| 94.35% (88.71-97.70)\u00a0\u00a0|\n| Other\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 88.89% (77.37-95.81)\u00a0| 84.00% (70.89-92.83)\u00a0\u00a0|\n\n| Minimal Volume Threshold (ml)\u00a0\u00a0| Sensi7vity above Threshold (95% CI)\u00a0\u00a0|\n|---------------------------------|---------------------------------------|\n| vol \u2265 0 ml\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 89.22% (83.50-93.49)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 6. Summary of the sensi0vity for the subsets of scans stra0fied by ICH volume\n\n| vol \u2265 0.4ml\u00a0\u00a0| 94.59% (89.63-97.64)\u00a0\u00a0|\n|---------------|------------------------|\n| vol \u2265 1ml\u00a0\u00a0\u00a0| 97.04% (92.59-99.19)\u00a0\u00a0|\n| vol \u2265 5ml\u00a0\u00a0\u00a0| 99.04% (94.76-99.98)\u00a0\u00a0|\n\nTo assess the secondary outcome measure, the device's time to notification was recorded for a sample of cases. This ranged from 50 seconds to 126 seconds, and is substantially lower than the average time to notification seen in the Standard of Care reported by the predicate device (38.2 minutes).\n\nIn summary, the clinical tests demonstrate that the device performs with good sensitivity and specificity to detect acute ICH from non-contrast CT imaging, and met the primary study endpoint of 80% sensitivity and specificity. This acceptability criterion was the same as used for the predicate device (Viz ICH, K193658), supporting the equivalent performance to the predicate. Furthermore, the device's time to notification ranged from 50-126 seconds which was significantly shorter than the average time to notification seen in the Standard of Care of 38.2 minutes reported by the predicate device.\n\n## Brainomix Limited\n\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\nAnswer the following question: Give me a .csv version of table 2,\n"], "reference": ["Gender,Sensitivity (95% CI),Specificity (95% CI)\nMale,93.18% (85.75-97.46),91.30% (83.58-96.17)\nFemale,84.81% (74.97-91.90),91.46% (83.20-96.50)\n"]} +{"qna_file": "compositional_skills/extraction/fda_filing/csv/qna.yaml", "question_id": "247083351197015360806905978416384233925", "category": "taxonomy", "turns": ["Given the context below:\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\n## Performance Data\n\nA retrospective study has been carried out to assess the standalone performance of the image analysis algorithm and notification functionality of Triage ICH. The study evaluated the Triage ICH image analysis algorithm in terms of sensitivity and specificity with respect to a ground truth, as established by experienced US board certified neuroradiologists, in the detection of intracranial hemorrhage (ICH) in the brain. In addition, the study reported and compared the time to notification for the Triage ICH device with respect to the time to notification for the standard of care as established by the predicate device.\n\nA sample size of 341 non-contrast Computed Tomography (NCCT) scans (studies) were obtained from 30 different hospitals and clinics in the U.S. The majority of patients were scanned at Boston Medical Centre (N=237) and the remainder came from 29 different referral hospitals in MassachuseTs State. The patient cohort was enriched to ensure an approximately equal balance of ICH positive and negative studies and to ensure the distribution of clinical and demographic variables (e.g., age, gender, ICH subtype, traumatic vs non traumatic etiology) allows generalizability to the patient population for whom use is intended.\n\nThe cases (n=341) were all successfully processed with the algorithm. The confusion matrix was as follows: True Positives (TP): 149, True Negatives (TN): 159, False Positives (FP): 15, False Negatives (FN): 18. The overall performance can also be summarized with the following metrics: sensitivity (or positive percentage agreement, defined as TP/[TP+FN]) was 89.22%, specificity (or negative percentage agreement, defined as TN/[TN+FP]) was 91.37%. The receiver operating curve (ROC) for the device is shown in Figure 1.\n\nFigure 1. Receiver Opera0ng Curve for the device, with the opera0ng point (sensi0vity 89.2%, specificity 91.4%) shown with the red triangle.\n\nBecause the lower bound of each confidence interval for sensitivity and specificity exceeded 80%, the study met the pre-specified performance goals.\n\nAs part of a secondary analysis, the company stratified the device performance by various confounding variables: ICH subtype (Table 1); by gender (Table 2); by age (Table 3); by slice thickness (Table 4); by clinical site (Table 5); and by ICH Volume (Table 6).\n\nTable 1. Summary of the performance metrics for the subsets of scans stra0fied by ICH Subtype.\n\n| ICH Subtype\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0|\n|-----------------------------------|------------------------|\n| Intraparenchymal Hemorrhage (IPH) | 96.61% (88.29-99.59)\u00a0\u00a0|\n| Intraventricular Hemorrhage (IVH) | 100.00% (59.04-100.00) |\n| Subarachnoid Hemorrhage (SAH)\u00a0\u00a0\u00a0| 35.71% (12.76-64.86)\u00a0\u00a0|\n| Subdural Hemorrhage (SDH)\u00a0\u00a0\u00a0\u00a0\u00a0| 66.67% (40.99-86.66)\u00a0\u00a0|\n| Mul7ple Types\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 98.55% (92.19-99.96)\u00a0\u00a0|\n\nTable 2. Summary of the performance metrics for the subsets of scans stra0fied by gender.\n\n| Gender\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|----------|-----------------------|------------------------|\n| Male\u00a0\u00a0\u00a0| 93.18% (85.75-97.46)\u00a0| 91.30% (83.58-96.17)\u00a0\u00a0|\n| Female\u00a0\u00a0| 84.81% (74.97-91.90)\u00a0| 91.46% (83.20-96.50)\u00a0\u00a0|\n\nTable 3. Summary of the performance metrics for the subsets of scans stra0fied by age group.\n\n| Age\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|---------------|-----------------------|------------------------|\n| 21 < Age < 50 | 83.33% (67.19-93.63)\u00a0| 88.73% (79.00-95.01)\u00a0\u00a0|\n| 50 \u2264 Age < 70 | 92.75% (83.89-97.61)\u00a0| 91.25% (82.80-96.41)\u00a0\u00a0|\n| Age \u2265 70\u00a0\u00a0\u00a0| 88.71% (78.11-95.34)\u00a0| 100.00% (85.18-100.00) |\n\nTable 4. Summary of the performance metrics for the subsets of scans stra0fied by slice thickness\n\n| Slice Thickness\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|--------------------------------|-----------------------|------------------------|\n| Slice Thickness < 1.5 mm\u00a0\u00a0\u00a0\u00a0| 87.50% (79.92-92.99)\u00a0| 94.21% (88.44-97.64)\u00a0\u00a0|\n| 1.5mm \u2264 Slice Thickness < 3 mm | 95.65% (78.05-99.89)\u00a0| 100.00% (82.35-100.00) |\n| Slice Thickness \u2265 3 mm\u00a0\u00a0\u00a0\u00a0\u00a0| 90.62% (74.98-98.02)\u00a0| 76.47% (58.83-89.25)\u00a0\u00a0|\n\nTable 5. Summary of the performance metrics for the subsets of scans stra0fied by referring hospital\n\n| Clinical Site\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|-----------------------|-----------------------|------------------------|\n| Boston Medical Centre | 89.38% (82.18-94.39)\u00a0| 94.35% (88.71-97.70)\u00a0\u00a0|\n| Other\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 88.89% (77.37-95.81)\u00a0| 84.00% (70.89-92.83)\u00a0\u00a0|\n\n| Minimal Volume Threshold (ml)\u00a0\u00a0| Sensi7vity above Threshold (95% CI)\u00a0\u00a0|\n|---------------------------------|---------------------------------------|\n| vol \u2265 0 ml\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 89.22% (83.50-93.49)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 6. Summary of the sensi0vity for the subsets of scans stra0fied by ICH volume\n\n| vol \u2265 0.4ml\u00a0\u00a0| 94.59% (89.63-97.64)\u00a0\u00a0|\n|---------------|------------------------|\n| vol \u2265 1ml\u00a0\u00a0\u00a0| 97.04% (92.59-99.19)\u00a0\u00a0|\n| vol \u2265 5ml\u00a0\u00a0\u00a0| 99.04% (94.76-99.98)\u00a0\u00a0|\n\nTo assess the secondary outcome measure, the device's time to notification was recorded for a sample of cases. This ranged from 50 seconds to 126 seconds, and is substantially lower than the average time to notification seen in the Standard of Care reported by the predicate device (38.2 minutes).\n\nIn summary, the clinical tests demonstrate that the device performs with good sensitivity and specificity to detect acute ICH from non-contrast CT imaging, and met the primary study endpoint of 80% sensitivity and specificity. This acceptability criterion was the same as used for the predicate device (Viz ICH, K193658), supporting the equivalent performance to the predicate. Furthermore, the device's time to notification ranged from 50-126 seconds which was significantly shorter than the average time to notification seen in the Standard of Care of 38.2 minutes reported by the predicate device.\n\n## Brainomix Limited\n\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\nAnswer the following question: Grab the table that describes the summary of performance metrics for scans, stratified by slice thickness, return it in .csv format."], "reference": ["Slice Thickness,Sensitivity (95% CI),Specificity (95% CI)\nSlice Thickness < 1.5 mm,87.50% (79.92-92.99),94.21% (88.44-97.64)\n1.5mm \u2264 Slice Thickness < 3 mm,95.65% (78.05-99.89),100.00% (82.35-100.00)\nSlice Thickness \u2265 3 mm,90.62% (74.98-98.02),76.47% (58.83-89.25)\n"]} +{"qna_file": "compositional_skills/extraction/fda_filing/csv/qna.yaml", "question_id": "309271686497587448058906964691281009453", "category": "taxonomy", "turns": ["Given the context below:\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\n## Performance Data\n\nA retrospective study has been carried out to assess the standalone performance of the image analysis algorithm and notification functionality of Triage ICH. The study evaluated the Triage ICH image analysis algorithm in terms of sensitivity and specificity with respect to a ground truth, as established by experienced US board certified neuroradiologists, in the detection of intracranial hemorrhage (ICH) in the brain. In addition, the study reported and compared the time to notification for the Triage ICH device with respect to the time to notification for the standard of care as established by the predicate device.\n\nA sample size of 341 non-contrast Computed Tomography (NCCT) scans (studies) were obtained from 30 different hospitals and clinics in the U.S. The majority of patients were scanned at Boston Medical Centre (N=237) and the remainder came from 29 different referral hospitals in MassachuseTs State. The patient cohort was enriched to ensure an approximately equal balance of ICH positive and negative studies and to ensure the distribution of clinical and demographic variables (e.g., age, gender, ICH subtype, traumatic vs non traumatic etiology) allows generalizability to the patient population for whom use is intended.\n\nThe cases (n=341) were all successfully processed with the algorithm. The confusion matrix was as follows: True Positives (TP): 149, True Negatives (TN): 159, False Positives (FP): 15, False Negatives (FN): 18. The overall performance can also be summarized with the following metrics: sensitivity (or positive percentage agreement, defined as TP/[TP+FN]) was 89.22%, specificity (or negative percentage agreement, defined as TN/[TN+FP]) was 91.37%. The receiver operating curve (ROC) for the device is shown in Figure 1.\n\nFigure 1. Receiver Opera0ng Curve for the device, with the opera0ng point (sensi0vity 89.2%, specificity 91.4%) shown with the red triangle.\n\nBecause the lower bound of each confidence interval for sensitivity and specificity exceeded 80%, the study met the pre-specified performance goals.\n\nAs part of a secondary analysis, the company stratified the device performance by various confounding variables: ICH subtype (Table 1); by gender (Table 2); by age (Table 3); by slice thickness (Table 4); by clinical site (Table 5); and by ICH Volume (Table 6).\n\nTable 1. Summary of the performance metrics for the subsets of scans stra0fied by ICH Subtype.\n\n| ICH Subtype\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0|\n|-----------------------------------|------------------------|\n| Intraparenchymal Hemorrhage (IPH) | 96.61% (88.29-99.59)\u00a0\u00a0|\n| Intraventricular Hemorrhage (IVH) | 100.00% (59.04-100.00) |\n| Subarachnoid Hemorrhage (SAH)\u00a0\u00a0\u00a0| 35.71% (12.76-64.86)\u00a0\u00a0|\n| Subdural Hemorrhage (SDH)\u00a0\u00a0\u00a0\u00a0\u00a0| 66.67% (40.99-86.66)\u00a0\u00a0|\n| Mul7ple Types\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 98.55% (92.19-99.96)\u00a0\u00a0|\n\nTable 2. Summary of the performance metrics for the subsets of scans stra0fied by gender.\n\n| Gender\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|----------|-----------------------|------------------------|\n| Male\u00a0\u00a0\u00a0| 93.18% (85.75-97.46)\u00a0| 91.30% (83.58-96.17)\u00a0\u00a0|\n| Female\u00a0\u00a0| 84.81% (74.97-91.90)\u00a0| 91.46% (83.20-96.50)\u00a0\u00a0|\n\nTable 3. Summary of the performance metrics for the subsets of scans stra0fied by age group.\n\n| Age\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|---------------|-----------------------|------------------------|\n| 21 < Age < 50 | 83.33% (67.19-93.63)\u00a0| 88.73% (79.00-95.01)\u00a0\u00a0|\n| 50 \u2264 Age < 70 | 92.75% (83.89-97.61)\u00a0| 91.25% (82.80-96.41)\u00a0\u00a0|\n| Age \u2265 70\u00a0\u00a0\u00a0| 88.71% (78.11-95.34)\u00a0| 100.00% (85.18-100.00) |\n\nTable 4. Summary of the performance metrics for the subsets of scans stra0fied by slice thickness\n\n| Slice Thickness\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|--------------------------------|-----------------------|------------------------|\n| Slice Thickness < 1.5 mm\u00a0\u00a0\u00a0\u00a0| 87.50% (79.92-92.99)\u00a0| 94.21% (88.44-97.64)\u00a0\u00a0|\n| 1.5mm \u2264 Slice Thickness < 3 mm | 95.65% (78.05-99.89)\u00a0| 100.00% (82.35-100.00) |\n| Slice Thickness \u2265 3 mm\u00a0\u00a0\u00a0\u00a0\u00a0| 90.62% (74.98-98.02)\u00a0| 76.47% (58.83-89.25)\u00a0\u00a0|\n\nTable 5. Summary of the performance metrics for the subsets of scans stra0fied by referring hospital\n\n| Clinical Site\u00a0\u00a0\u00a0\u00a0\u00a0| Sensi7vity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|-----------------------|-----------------------|------------------------|\n| Boston Medical Centre | 89.38% (82.18-94.39)\u00a0| 94.35% (88.71-97.70)\u00a0\u00a0|\n| Other\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 88.89% (77.37-95.81)\u00a0| 84.00% (70.89-92.83)\u00a0\u00a0|\n\n| Minimal Volume Threshold (ml)\u00a0\u00a0| Sensi7vity above Threshold (95% CI)\u00a0\u00a0|\n|---------------------------------|---------------------------------------|\n| vol \u2265 0 ml\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 89.22% (83.50-93.49)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 6. Summary of the sensi0vity for the subsets of scans stra0fied by ICH volume\n\n| vol \u2265 0.4ml\u00a0\u00a0| 94.59% (89.63-97.64)\u00a0\u00a0|\n|---------------|------------------------|\n| vol \u2265 1ml\u00a0\u00a0\u00a0| 97.04% (92.59-99.19)\u00a0\u00a0|\n| vol \u2265 5ml\u00a0\u00a0\u00a0| 99.04% (94.76-99.98)\u00a0\u00a0|\n\nTo assess the secondary outcome measure, the device's time to notification was recorded for a sample of cases. This ranged from 50 seconds to 126 seconds, and is substantially lower than the average time to notification seen in the Standard of Care reported by the predicate device (38.2 minutes).\n\nIn summary, the clinical tests demonstrate that the device performs with good sensitivity and specificity to detect acute ICH from non-contrast CT imaging, and met the primary study endpoint of 80% sensitivity and specificity. This acceptability criterion was the same as used for the predicate device (Viz ICH, K193658), supporting the equivalent performance to the predicate. Furthermore, the device's time to notification ranged from 50-126 seconds which was significantly shorter than the average time to notification seen in the Standard of Care of 38.2 minutes reported by the predicate device.\n\n## Brainomix Limited\n\nFirst Floor, Seacourt Tower, West Way Oxford OX2 0JJ, United Kingdom\n\nAnswer the following question: Create a table, in csv format, that captures the true positives, true negatives, false positives, and false negatives from the algorithm.\n"], "reference": ["True Positives,False Positives,True Negatives,False Negatives\n149,15,159,18\n"]} +{"qna_file": "compositional_skills/extraction/abstractive/abstract/qna.yaml", "question_id": "52416929612810531569891224675802917530", "category": "taxonomy", "turns": ["Given the context below:\nKanye Omari West (born June 8, 1977) is an American rapper, singer, songwriter, record producer, entrepreneur, and fashion designer.\\n\\nHis music has spanned a broad range of styles, incorporating an eclectic range of influences including hip hop, soul, baroque pop, electro, indie rock, synth-pop, industrial, and gospel.\\n\\nBorn in Atlanta and raised in Chicago, West first became known as a producer for Roc-A-Fella Records in the early 2000s, producing singles for recording artists such as Jay-Z, Ludacris, and Alicia Keys.\\n\\nIntent on pursuing a solo career as a rapper, West released his debut album \"The College Dropout\" in 2004 to critical and commercial success, and founded the record label GOOD Music.\\n\\nHe went on to experiment with a variety of musical genres on subsequent acclaimed studio albums, including \"Late Registration\" (2005), \"Graduation\" (2007), and the polarizing but influential \"808s & Heartbreak\" (2008).\\n\\nReleased in 2010, West\\'s fifth album \"\\n\\nMy Beautiful Dark Twisted Fantasy\" became his most critically acclaimed album and is widely regarded by critics as the best album of the 2010s.\\n\\nHe has since succeeded the album with \"Yeezus\" (2013), \"The Life of Pablo\" (2016), \"Ye\" (2018), and\\n\\n\"Jesus Is King\" (2019), as well as full-length collaborations \"Watch the Throne\" (2011) and \"Kids See Ghosts\" (2018) with Jay-Z and Kid Cudi respectively.\\n\\nWest\\'s outspoken views and life outside of music have received significant media attention.\\n\\nWest is a practicing Christian and his religious views have garnered widespread curiosity.\\n\\nHe has been a frequent source of controversy for his conduct at award shows, on social media, and in other public settings, as well as his comments on the music and fashion industries, U.S. politics, and race.\\n\\nHis marriage to television personality Kim Kardashian has also been a source of substantial media attention.\\n\\nAs a fashion designer, he has collaborated with Nike, Louis Vuitton, and A.P.C. on both clothing and footwear, and have most prominently resulted in the Yeezy collaboration with Adidas beginning in 2013.\\n\\nHe is the founder and head of the creative content company DONDA.\\n\\nWest is among the most critically acclaimed musicians of the 21st century and one of the best-selling music artists of all time with over 100 million records sold worldwide.\\n\\nHe has won a total of 21 Grammy Awards, making him one of the most awarded artists of all time.\\n\\nThree of his albums have been included and ranked on \"Rolling Stone\"s\\n\\n2012 update of the \"500 Greatest Albums of All Time\" list\\n\\nand he is tied with Bob Dylan for the most albums topping the annual Pazz & Jop critic poll with four.\\n\\n\"Time\" magazine named him one of the 100 most influential people in the world in 2005 and 2015.\\nEtan Boritzer( born 1950) is an American writer of children \u2019s literature who is best known for his book\" What is God?\" first published in 1989.\\n\\nHis best selling\" What is?\" illustrated children\\'s book series on character education and difficult subjects for children is a popular teaching guide for parents, teachers and child- life professionals.\\n\\nBoritzer gained national critical acclaim after\" What is God?\" was published in 1989 although the book has caused controversy from religious fundamentalists for its universalist views.\\n\\nThe other current books in the\" What is?\" series include\\n\\nWhat is Love?,\\n\\nWhat is Death?,\\n\\nWhat is Beautiful?,\\n\\nWhat is Funny?,\\n\\nWhat is Right?,\\n\\nWhat is Peace?,\\n\\nWhat is Money?,\\n\\nWhat is Dreaming?,\\n\\nWhat is a Friend?,\\n\\nWhat is True?,\\n\\nWhat is a Family?,\\n\\nWhat is a Feeling?\"\\n\\nThe series is now also translated into 15 languages.\\n\\nBoritzer was first published in 1963 at the age of 13 when he wrote an essay in his English class at Wade Junior High School in the Bronx, New York on the assassination of John F. Kennedy.\\n\\nHis essay was included in a special anthology by New York City public school children compiled and published by the New York City Department of Education.\\n\\nBoritzer now lives in Venice, California and maintains his publishing office there also.\\n\\nHe has helped numerous other authors to get published through\" How to Get Your Book Published!\" programs.\\n\\nBoritzer is also a yoga teacher who teaches regular classes locally and guest- teaches nationally.\\n\\nHe is also recognized nationally as an erudite speaker on\" The Teachings of the Buddha.\"\\n\"Run This Town\" is a song by American rapper Jay-Z.\\n\\nThe song also features Rihanna and Kanye West.\\n\\nIt was written by the performing trio, with assistance from Ernest Wilson and Jeff Bhasker.\\n\\nProduction of the track was made by West and Wilson (also known as \"No I.D.Run\\n\\nThis Town\" was released as the second single from Jay-Z\\'s eleventh studio album \"The Blueprint 3\", and is used as the theme song of \"Battlefield 4\".\\n\\n\"Run This Town\" received mixed reviews from music critics; some praised what each member of the trio brought to the track, whilst others thought that Rihanna and Kanye West out-performed\\n\\nJay-Z.\\n\\nThe song was a commercial success, reaching number two on the \"Billboard\" Hot 100 and topping the charts in the United Kingdom.\\n\\nIt additionally made top ten chartings in ten other countries including in Australia, Norway, Sweden, and Switzerland.\\n\\n\"Run This Town\" won Best Rap Song and Best Rap/Sung Collaboration at the 52nd Annual Grammy Awards.\\n\\nThe song\\'s accompanying music video, directed by Anthony Mandler, depicts the trio in a post-apocalyptic environment, featuring scenes involving angry protesters surrounding them. \"\\n\\nRun This Town\" was performed by Jay-Z, West, and Rihanna on the series premiere of \"The Jay Leno Show\" in the United States.\\nWalter Robinson is an African American composer of the late 20th century.\\n\\nHe is most notable for his 1977 song\" Harriet Tubman\", which has been recorded by folk musicians such as Holly Near, John McCutcheon, and others.\\n\\nHe is also the composer of several operas.\\nBrian Saunders( date of birth and death unknown) was a male weightlifter who competed for England.\\nTheodred II was a medieval Bishop of Elmham.\\n\\nThe date of Theodred\\'s consecration unknown, but the date of his death was sometime between 995 and 997.\\nAlonso Mudarra( c. 1510 \u2013 April 1, 1580) was a Spanish composer of the Renaissance, and also played the vihuela, a guitar- shaped string instrument.\n\nAnswer the following question: Could you generate an abstract for the given text?"], "reference": ["Kanye Omari West is a renowned American rapper, singer, songwriter, record producer, entrepreneur, and fashion designer, known for his broad range of musical styles and incorporation of various genres. He first gained recognition as a producer for Roc-A-Fella Records, producing hits for Jay-Z, Ludacris, and Alicia Keys, before embarking on a successful solo career with albums like \"The College Dropout\" and \"My Beautiful Dark Twisted Fantasy.\" West is also a fashion designer, collaborating with Nike, Louis Vuitton, and Adidas, and is the founder of DONDA. Etan Boritzer is an American children\\'s literature author, best known for his universalist book \"What is God?\" and the popular \"What is?\" series, which has caused controversy among religious fundamentalists. Brian Saunders and Terence D. Robinson were male weightlifters and wrestlers, respectively, who competed for England. Walter Robinson is an African American composer known for his 1977 song \"Harriet Tubman.\" Les Richards is an Australian rules footballer who played with North Melbourne in the VFL. Pamela Jain is an Indian playback singer, and Alonso Mudarra was a Spanish Renaissance composer and vihuela player who composed the earliest surviving music for the guitar. Theodred II was a medieval Bishop of Elmham.\n"]} +{"qna_file": "compositional_skills/extraction/abstractive/key_points/qna.yaml", "question_id": "201095040296849433371521986221743432226", "category": "taxonomy", "turns": ["Given the context below:\nEtan Boritzer( born 1950) is an American writer of children \u2019s literature who is best known for his book\" What is God?\" first published in 1989.\\n\\nHis best selling\" What is?\" illustrated children\\'s book series on character education and difficult subjects for children is a popular teaching guide for parents, teachers and child- life professionals.\\n\\nBoritzer gained national critical acclaim after\" What is God?\" was published in 1989 although the book has caused controversy from religious fundamentalists for its universalist views.\\n\\nThe other current books in the\" What is?\" series include\\n\\nWhat is Love?,\\n\\nWhat is Death?,\\n\\nWhat is Beautiful?,\\n\\nWhat is Funny?,\\n\\nWhat is Right?,\\n\\nWhat is Peace?,\\n\\nWhat is Money?,\\n\\nWhat is Dreaming?,\\n\\nWhat is a Friend?,\\n\\nWhat is True?,\\n\\nWhat is a Family?,\\n\\nWhat is a Feeling?\"\\n\\nThe series is now also translated into 15 languages.\\n\\nBoritzer was first published in 1963 at the age of 13 when he wrote an essay in his English class at Wade Junior High School in the Bronx, New York on the assassination of John F. Kennedy.\\n\\nHis essay was included in a special anthology by New York City public school children compiled and published by the New York City Department of Education.\\n\\nBoritzer now lives in Venice, California and maintains his publishing office there also.\\n\\nHe has helped numerous other authors to get published through\" How to Get Your Book Published!\" programs.\\n\\nBoritzer is also a yoga teacher who teaches regular classes locally and guest- teaches nationally.\\n\\nHe is also recognized nationally as an erudite speaker on\" The Teachings of the Buddha.\"\\nHarry S. Webb (October 15, 1892 \u2013 July 4, 1959) was an American film producer, director and screenwriter.\\n\\nHe produced 100 films between 1924 and 1940.\\n\\nHe also directed 55 films between 1924 and 1940.\\n\\nHe was the brother of \"B\"-film producer and director Ira S. Webb and the husband of screenwriter Rose Gordon, who wrote many of his films.\\n\\nIn 1933 Webb and Bernard B. Ray created Reliable Pictures Corporation with a studio at Beachwood and Sunset Boulevard in Hollywood.\\n\\nReliable produced and released many Westerns, starting with \"Girl Trouble\" (1933), until the company closed in 1937.\\n\\nIts final release was \"The Silver Trail\".\\n\\nWebb and Ray then started Metropolitan Pictures Corporation in 1938, which produced and released several films until 1940, its last being \"Pinto Canyon\".\\n\\nWebb then produced Westerns for Monogram Pictures.\\n\\nHe was born in Pennsylvania and died in Hollywood, from a heart attack.\\nIan Barry is an Australian director of film and TV.\\nPinto Rustlers is a 1936 American western film directed by Harry S. Webb and starring Tom Tyler, George Walsh and Al St. John.\\nLes Richards( date of birth unknown) was an Australian rules footballer who played with North Melbourne in the Victorian Football League( VFL).\\nBrian Saunders( date of birth and death unknown) was a male weightlifter who competed for England.\\nTheodred II was a medieval Bishop of Elmham.\\n\\nThe date of Theodred\\'s consecration unknown, but the date of his death was sometime between 995 and 997.\\nTerence D. Robinson( date of birth and death unknown) was a male wrestler who competed for England.\\nPamela Jain is an Indian playback singer.\\n\\nDate of Birth:16th March.\\nPeter Levin is an American director of film, television and theatre.\n\nAnswer the following question: Generate the key points from the given text."], "reference": ["1. Etan Boritzer is an American writer of children\\'s literature, best known for his book \"What is God?\" published in 1989.\\n2. His \"What is?\" series, which includes books like \"What is Love?\", \"What is Death?\", \"What is Beautiful?\", etc., is a popular teaching guide for parents, teachers, and child-life professionals.\\n3. The series has caused controversy due to its universalist views and has been translated into 15 languages.\\n4. Boritzer was first published at the age of 13 and now lives in Venice, California, where he maintains his publishing office.\\n5. He has helped numerous other authors get published and is also a yoga teacher and an erudite speaker on \"The Teachings of the Buddha.\"\\n6. Harry S. Webb was an American film producer, director, and screenwriter who produced and directed 100 films between 1924 and 1940.\\n7. Webb and his wife, Rose Gordon, created Reliable Pictures Corporation in 1933, which produced Westerns until 1937.\\n8. Webb then started Metropolitan Pictures Corporation in 1938, which produced several films until 1940.\\n9. Ian Barry is an Australian director of film and TV.\\n10. \"Pinto Rustlers\" is a 1936 American western film directed by Harry S. Webb and starring Tom Tyler, George Walsh, and Al St. John.\\n11. Les Richards was an Australian rules footballer who played with North Melbourne in the Victorian Football League (VFL).\\n12. Brian Saunders was a male weightlifter who competed for England.\\n13. Theodred II was a medieval Bishop of Elmham, whose date of consecration is unknown, but the date of his death was between 995 and 997.\\n14. Terence D. Robinson was a male wrestler who competed for England.\\n15. Pamela Jain is an Indian playback singer, born on 16th March.\\n16. Peter Levin is an American director of film, television, and theatre.\n"]} +{"qna_file": "compositional_skills/extraction/abstractive/title/qna.yaml", "question_id": "132279763738675785809084335313852430269", "category": "taxonomy", "turns": ["Given the context below:\nDana Blankstein- Cohen( born March 3, 1981) is the director of the Israeli Academy of Film and Television.\\n\\nShe is a film director, and an Israeli culture entrepreneur.\\nLuciano Salce (25 September 1922, in Rome \u2013 17 December 1989, in Rome) was an Italian film director, actor and lyricist.\\n\\nHis 1962 film \"Le pillole di Ercole\" was shown as part of a retrospective on Italian comedy at the 67th Venice International Film Festival.\\n\\nAs a writer of pop music, he used the pseudonym Pilantra.\\n\\nDuring World War II, he was a prisoner in Germany.\\n\\nHe later worked for several years in Brazil.\\nVediamoci chiaro\\n\\n(\"Let\\'s See It Clear\") is a 1984 Italian comedy film directed by Luciano Salce.\\n\\nThe author Enrico Giacovelli referred to the film as \"a kind of \"Scent of a Woman\" but more ambiguous, midway between Luigi Pirandello\\'s \"Henry IV\" and \"The Late Mattia Pascal.\\nPeter Levin is an American director of film, television and theatre.\\nIan Barry is an Australian director of film and TV.\\nJesse Edward Hobson( May 2, 1911 \u2013 November 5, 1970) was the director of SRI International from 1947 to 1955.\\n\\nPrior to SRI, he was the director of the Armour Research Foundation.\\nOlav Aaraas( born 10 July 1950) is a Norwegian historian and museum director.\\n\\nHe was born in Fredrikstad.\\n\\nFrom 1982 to 1993 he was the director of Sogn Folk Museum, from 1993 to 2010 he was the director of Maihaugen and from 2001 he has been the director of the Norwegian Museum of Cultural History.\\n\\nIn 2010 he was decorated with the Royal Norwegian Order of St. Olav.\\nBrian O\u2019 Malley is an Irish film director known for the horror film\" Let Us Prey\" and the ghost story\" The Lodgers\".\\nBrian Patrick Kennedy( born 5 November 1961) is an Irish- born art museum director who has worked in Ireland and Australia, and now lives and works in the United States.\\n\\nHe is currently the director of the Peabody Essex Museum.\\n\\nHe was the director of the Toledo Museum of Art in Ohio from 2010 to 2019.\\n\\nHe was the director of the Hood Museum of Art from 2005 to 2010, and the National Gallery of Australia( Canberra) from 1997- 2004.\n\nAnswer the following question: Generate a title for the given text."], "reference": ["Directors Across Borders: A Comparative Study of International Film and Museum Directors, from Luciano Salce to Brian Patrick Kennedy"]} +{"qna_file": "compositional_skills/extraction/abstractive/outline/qna.yaml", "question_id": "307319685060050356725520955919296230149", "category": "taxonomy", "turns": ["Given the context below:\nHanro Smitsman, born in 1967 in Breda( Netherlands), is a writer and director of film and television.\\nRachel Feldman( born August 22, 1954) is an American director of film and television and screenwriter of television films.\\nLucas Belvaux( born 14 November 1961) is a Belgian actor and film director.\\n\\nHis directing credits include the\" Trilogie\", consisting of three films with interlocking stories and characters, each of which was filmed in a different genre.\\n\\nThe three films are\" Cavale\", a thriller;\" Un couple \u00e9patant\", a comedy; and\" Apr\u00e8s la vie\", a melodrama.\\n\\nThe\" Trilogie\" received the Andr\u00e9 Cavens Award.\\n\\nHis film\" La Raison du plus faible\" was entered into the 2006 Cannes Film Festival.\\n\\nHis film\" One Night( 38 t\u00e9moins)\" was nominated for seven Magritte Awards, winning Best Screenplay.\\n\\nHe also appeared as an actor in the film\" Merry Christmas\"( 2005).\\n\\nHe is the brother of R\u00e9my Belvaux and Bruno Belvaux.\\nJoseph Kaufman\\n\\n( 1882 \u2013 February 1, 1918) was an American silent film actor and director prominent during the World War I years.\\n\\nHe was born in Washington, D.C. and died in New York City very early on during the influenza pandemic of 1918.\\n\\nHis wife was silent film star Ethel Clayton.\\n\\nKaufman started out as an actor appearing on Broadway in 1903 with Maude Adams in\" The Pretty Sister of Jose\".\\n\\nHe eventually changed to acting in and then directing silent films, his output being numerous short films.\\n\\nAs American films began to expand to feature length Kaufman followed suit and proved particularly adept and popular at directing beautiful Broadway actresses including his wife Ethel Clayton.\\n\\nHe directed Pauline Frederick, Marguerite Clark, and Billie Burke.\\n\\nHis last film,\" The Song of Songs\"( 1918), starred Elsie Ferguson.\\n\\nAs actor he is reported to have had three teeth knocked out in a screen fight with actor Earl Metcalfe.\\n\\nFollowing his death in February 1918, both of his parents died\u2014his mother on April 9, 1918 and his father on April 18, 1918.\\nThe Amazons is a 1917 American silent comedy film directed by Joseph Kaufman and starred Marguerite Clark, Elsie Lawson, and Helen Greene.\\n\\nThe film was based on the 1883 play of the same name by Sir Arthur Wing Pinero, and adapted for the screen by Frances Marion.\\n\\nIt was produced and distributed by Famous Players- Lasky and distributed by Paramount Pictures under the Famous Players- Lasky name.\\n\\nThe film is now presumed lost.\\nApr\u00e8s la vie\\n\\n( After the Life; also known in UK as Trilogy: Three) is a 2002 Lucas Belvaux film with his own script.\\n\\nIt is the final installment of a series\" Trilogy\", which constitutes a melodrama preceded by\" Un couple \u00e9patant\", a comedy and\" Cavale\", a thriller.\\n\\nBelvaux referred in the DVD commentary that main idea behind\" Trilogy\" is that the main characters in a particular story are the secondary characters of others, in such sense the three films happen at the same time and share a series of common scenes and plot points, complementing each other, but also have their own perspective and style.\\n\\nThe audience is left with piecing the films together, which Belvaux avoided, since editing the three films into one single narrative would have resulted in a very long film with no style of its own.\\nBrian Johnson( born 1939 or 1940) is a British designer and director of film and television special effects.\\nHoward Winchel Koch( April 11, 1916 \u2013 February 16, 2001) was an American producer and director of film and television.\\nIan Barry is an Australian director of film and TV.\\nPeter Levin is an American director of film, television and theatre.\n\nAnswer the following question: For the given text, could you generate an outline?"], "reference": ["I. Hanro Smitsman\\n\\n* Born in 1967 in Breda, Netherlands\\n* Writer and director of film and television\\n* Known for \"Trilogie,\" consisting of three films with interlocking stories and characters, each filmed in a different genre:\\n\\t1. \"Cavale\" - thriller\\n\\t2. \"Un couple \u00e9patant\" - comedy\\n\\t3. \"Apr\u00e8s la vie\" - melodrama\\n* \"Trilogie\" received the Andr\u00e9 Cavens Award\\n* \"La Raison du plus faible\" was entered into the 2006 Cannes Film Festival\\n* \"One Night (38 t\u00e9moins)\" was nominated for seven Magritte Awards, winning Best Screenplay\\n* Acted in the film \"Merry Christmas\" (2005)\\n* Brother of R\u00e9my Belvaux and Bruno Belvaux\\n\\nII. Joseph Kaufman\\n\\n* American silent film actor and director (1882 \u2013 February 1, 1918)\\n* Born in Washington, D.C., died in New York City during the influenza pandemic of 1918\\n* Wife was silent film star Ethel Clayton\\n* Started as an actor, appearing on Broadway in 1903\\n* Transitioned to acting in and directing silent films\\n* Directed many short films and feature-length films\\n* Directed Pauline Frederick, Marguerite Clark, Billie Burke, and Ethel Clayton\\n* Last film, \"The Song of Songs\" (1918), starred Elsie Ferguson\\n* Actor in \"The Amazons\" (1917), a silent comedy film directed by Kaufman\\n\\nIII. Lucas Belvaux\\n\\n* Belgian actor and film director\\n* Known for \"Trilogie\" and \"Apr\u00e8s la vie\"\\n* \"Apr\u00e8s la vie\" is the final installment of \"Trilogy,\" a series of three films with interlocking stories and characters\\n* \"Trilogy\" includes:\\n\\t1. \"Cavale\" - thriller\\n\\t2. \"Un couple \u00e9patant\" - comedy\\n\\t3. \"Apr\u00e8s la vie\" - melodrama\\n* \"Trilogy\" shares common scenes and plot points, with each film having its own perspective and style\\n* \"La Raison du plus faible\" was entered into the 2006 Cannes Film Festival\\n* \"One Night (38 t\u00e9moins)\" was nominated for seven Magritte Awards, winning Best Screenplay\\n\\nIV. Brian Johnson\\n\\n* British designer and director of film and television special effects\\n\\nV. Howard Winchel Koch\\n\\n* American producer and director of film and television (April 11, 1916 \u2013 February 16, 2001)\\n\\nVI. Ian Barry\\n\\n* Australian director of film and TV\\n\\nVII. Peter Levin\\n\\n* American director of film, television, and theatre\n"]} +{"qna_file": "compositional_skills/extraction/abstractive/main_takeaway/qna.yaml", "question_id": "144105919578348809585932607258950036003", "category": "taxonomy", "turns": ["Given the context below:\nUp Front is a 1951 comedy film directed by Alexander Hall and starring Tom Ewell and David Wayne very loosely based on Bill Mauldin\\'s World War II characters \"Willie and Joe\".\\n\\nMauldin repudiated it and refused his advising fee; he claimed never to have seen it.\\nTheodred II was a medieval Bishop of Elmham.\\n\\nThe date of Theodred\\'s consecration unknown, but the date of his death was sometime between 995 and 997.\\nMark Maxey( born May 13, 1969) is an American producer, writer and director of film and television best known for the documentary film\" Up to Snuff\"( 2019), about musician/ composer W. G. Snuffy Walden.\\nEtan Boritzer( born 1950) is an American writer of children \u2019s literature who is best known for his book\" What is God?\" first published in 1989.\\n\\nHis best selling\" What is?\" illustrated children\\'s book series on character education and difficult subjects for children is a popular teaching guide for parents, teachers and child- life professionals.\\n\\nBoritzer gained national critical acclaim after\" What is God?\" was published in 1989 although the book has caused controversy from religious fundamentalists for its universalist views.\\n\\nThe other current books in the\" What is?\" series include\\n\\nWhat is Love?,\\n\\nWhat is Death?,\\n\\nWhat is Beautiful?,\\n\\nWhat is Funny?,\\n\\nWhat is Right?,\\n\\nWhat is Peace?,\\n\\nWhat is Money?,\\n\\nWhat is Dreaming?,\\n\\nWhat is a Friend?,\\n\\nWhat is True?,\\n\\nWhat is a Family?,\\n\\nWhat is a Feeling?\"\\n\\nThe series is now also translated into 15 languages.\\n\\nBoritzer was first published in 1963 at the age of 13 when he wrote an essay in his English class at Wade Junior High School in the Bronx, New York on the assassination of John F. Kennedy.\\n\\nHis essay was included in a special anthology by New York City public school children compiled and published by the New York City Department of Education.\\n\\nBoritzer now lives in Venice, California and maintains his publishing office there also.\\n\\nHe has helped numerous other authors to get published through\" How to Get Your Book Published!\" programs.\\n\\nBoritzer is also a yoga teacher who teaches regular classes locally and guest- teaches nationally.\\n\\nHe is also recognized nationally as an erudite speaker on\" The Teachings of the Buddha.\"\\nTakagi is represented by Up- Front Promotion.\\nUemura is represented by Up- Front Promotion.\\nKanazawa is represented by Up- Front Promotion.\\nA Family Affair is a 2001 lesbian romantic comedy directed by Helen Lesnick.\\n\\nThe director followed the film up with\" Inescapable\" in 2003.\\nMiyazaki is represented by Up- Front Promotion.\\nAlexander Hall (January 11, 1894 \u2013 July 30, 1968) was an American film director and theatre actor.\n\nAnswer the following question: Generate the main takeaways from the given text."], "reference": ["The main takeaways from the above text are:\\n\\n1. Up Front is a 1951 comedy film directed by Alexander Hall, starring Tom Ewell and David Wayne, based on Bill Mauldin\\'s World War II characters \"Willie and Joe.\" However, Mauldin disowned the film and refused his advising fee.\\n2. Theodred II was a Bishop of Elmham in the medieval period, but the exact date of his consecration is unknown. He died between 995 and 997.\\n3. Mark Maxey is an American producer, writer, and director, best known for the documentary \"Up to Snuff\" (2019), about musician/composer W.G. Snuffy Walden.\\n4. Etan Boritzer is an American writer of children\\'s literature, best known for his book \"What is God?\" published in 1989. His \"What is?\" series is a popular teaching guide for parents, teachers, and child-life professionals, and has been translated into 15 languages. Boritzer gained national critical acclaim after \"What is God?\" was published, but the book has also caused controversy from religious fundamentalists for its universalist views.\\n5. Boritzer was first published in 1963 at the age of 13 and now lives in Venice, California, where he maintains his publishing office. He also teaches yoga and is recognized nationally as an erudite speaker on \"The Teachings of the Buddha.\"\\n6. Takagi, Uemura, and Kanazawa are represented by Up-Front Promotion.\\n7. \"A Family Affair\" is a 2001 lesbian romantic comedy directed by Helen Lesnick, who followed it up with \"Inescapable\" in 2003.\\n8. Alexander Hall was an American film director and theatre actor.\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/abstract/bullet_points/qna.yaml", "question_id": "9996609669891759148819061852905674599", "category": "taxonomy", "turns": ["Given the context below:\n## Post-processing Private Synthetic Data for Improving Utility on Selected Measures\n\nHao Wang, Shivchander Sudalairaj, John Henning, Kristjan Greenewald, Akash Srivastava \u2217 MIT-IBM Watson AI Lab\n\n## Abstract\n\nExisting private synthetic data generation algorithms are agnostic to downstream tasks. However, end users may have specific requirements that the synthetic data must satisfy. Failure to meet these requirements could significantly reduce the utility of the data for downstream use. We introduce a post-processing technique that improves the utility of the synthetic data with respect to measures selected by the end user, while preserving strong privacy guarantees and dataset quality. Our technique involves resampling from the synthetic data to filter out samples that do not meet the selected utility measures, using an efficient stochastic first-order algorithm to find optimal resampling weights. Through comprehensive numerical experiments, we demonstrate that our approach consistently improves the utility of synthetic data across multiple benchmark datasets and state-of-the-art synthetic data generation algorithms.\n\n## 1 Introduction\n\nThe advancement of machine learning (ML) techniques relies on large amounts of training data. However, data collection also poses a significant risk of exposing private information. In recent years, several instances of privacy breaches have surfaced [NS06, Con18], making it urgent to find a reliable way to share data. Today, the de facto standard for privacy protection is differential privacy (DP) [DR14]. DP ensures that useful information of a private dataset can be released while simultaneously preventing adversaries from identifying individuals' personal data. DP has been utilized effectively in a wide variety of settings, by actors including the US Census [Abo18] and various large corporations [App17, Fac20, RE19, HBMAL19, IBM23].\n\nGenerating private synthetic data is a crucial application of DP. It allows data scientists to train their ML models on the synthetic data while preserving a certain level of utility when deploying these models on real test data. The U.S. National Institute of Standards and Technology (NIST) has emphasized its significance by hosting a series of competitions [RTMT21, MMS21]. There is also significant work introducing new DP synthetic data generation mechanisms, including GAN-based [XLW + 18, BJWW + 19, JYVDS19, TWB + 19], marginal-based [ZCP + 17, MSM19, MMS21], and workload-based [VTB + 20, ABK + 21, LVW21, MMSM22, VAA + 22] methods. Existing methods for generating private synthetic data are task-agnostic-they do not take into account the downstream use cases of the synthetic data in the data generation process. However, end users often have specific requirements for synthetic datasets to be successfully analyzed by their existing data science pipelines, which are often well-established, well-understood, heavily vetted, and difficult or expensive to change. Unfortunately, synthetic data generated by existing methods may not always meet these requirements, resulting in reduced utility for their downstream use cases. This raises a fundamental question:\n\nAnswer the following question: Give a summary of the abstract in bullets.\n"], "reference": ["Abstract summary:\n- Synthetic data generation requires attention to the downstream user requirements to have full utility.\n- The authors introduce a post-processing technique that improves utility while preserving data privacy and quality.\n- The approach consistently improves the utility of synthetic data across multiple benchmark datasets and state-of-the-art synthetic data generation algorithms.\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/abstract/bullet_points/qna.yaml", "question_id": "281014580809199924646945703084605091396", "category": "taxonomy", "turns": ["Given the context below:\n## Post-processing Private Synthetic Data for Improving Utility on Selected Measures\n\nHao Wang, Shivchander Sudalairaj, John Henning, Kristjan Greenewald, Akash Srivastava \u2217 MIT-IBM Watson AI Lab\n\n## Abstract\n\nExisting private synthetic data generation algorithms are agnostic to downstream tasks. However, end users may have specific requirements that the synthetic data must satisfy. Failure to meet these requirements could significantly reduce the utility of the data for downstream use. We introduce a post-processing technique that improves the utility of the synthetic data with respect to measures selected by the end user, while preserving strong privacy guarantees and dataset quality. Our technique involves resampling from the synthetic data to filter out samples that do not meet the selected utility measures, using an efficient stochastic first-order algorithm to find optimal resampling weights. Through comprehensive numerical experiments, we demonstrate that our approach consistently improves the utility of synthetic data across multiple benchmark datasets and state-of-the-art synthetic data generation algorithms.\n\n## 1 Introduction\n\nThe advancement of machine learning (ML) techniques relies on large amounts of training data. However, data collection also poses a significant risk of exposing private information. In recent years, several instances of privacy breaches have surfaced [NS06, Con18], making it urgent to find a reliable way to share data. Today, the de facto standard for privacy protection is differential privacy (DP) [DR14]. DP ensures that useful information of a private dataset can be released while simultaneously preventing adversaries from identifying individuals' personal data. DP has been utilized effectively in a wide variety of settings, by actors including the US Census [Abo18] and various large corporations [App17, Fac20, RE19, HBMAL19, IBM23].\n\nGenerating private synthetic data is a crucial application of DP. It allows data scientists to train their ML models on the synthetic data while preserving a certain level of utility when deploying these models on real test data. The U.S. National Institute of Standards and Technology (NIST) has emphasized its significance by hosting a series of competitions [RTMT21, MMS21]. There is also significant work introducing new DP synthetic data generation mechanisms, including GAN-based [XLW + 18, BJWW + 19, JYVDS19, TWB + 19], marginal-based [ZCP + 17, MSM19, MMS21], and workload-based [VTB + 20, ABK + 21, LVW21, MMSM22, VAA + 22] methods. Existing methods for generating private synthetic data are task-agnostic-they do not take into account the downstream use cases of the synthetic data in the data generation process. However, end users often have specific requirements for synthetic datasets to be successfully analyzed by their existing data science pipelines, which are often well-established, well-understood, heavily vetted, and difficult or expensive to change. Unfortunately, synthetic data generated by existing methods may not always meet these requirements, resulting in reduced utility for their downstream use cases. This raises a fundamental question:\n\nAnswer the following question: Provide a bulleted list of the author's emails.\n"], "reference": ["Here are the author's emails:\n- hao@ibm.com\n- shiv.sr@ibm.com\n- john.l.henning@ibm.com\n- kristjan.h.greenewald@ibm.com\n- akash.srivastava@ibm.com\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/abstract/bullet_points/qna.yaml", "question_id": "137243001175003410439804752262228281673", "category": "taxonomy", "turns": ["Given the context below:\n## Post-processing Private Synthetic Data for Improving Utility on Selected Measures\n\nHao Wang, Shivchander Sudalairaj, John Henning, Kristjan Greenewald, Akash Srivastava \u2217 MIT-IBM Watson AI Lab\n\n## Abstract\n\nExisting private synthetic data generation algorithms are agnostic to downstream tasks. However, end users may have specific requirements that the synthetic data must satisfy. Failure to meet these requirements could significantly reduce the utility of the data for downstream use. We introduce a post-processing technique that improves the utility of the synthetic data with respect to measures selected by the end user, while preserving strong privacy guarantees and dataset quality. Our technique involves resampling from the synthetic data to filter out samples that do not meet the selected utility measures, using an efficient stochastic first-order algorithm to find optimal resampling weights. Through comprehensive numerical experiments, we demonstrate that our approach consistently improves the utility of synthetic data across multiple benchmark datasets and state-of-the-art synthetic data generation algorithms.\n\n## 1 Introduction\n\nThe advancement of machine learning (ML) techniques relies on large amounts of training data. However, data collection also poses a significant risk of exposing private information. In recent years, several instances of privacy breaches have surfaced [NS06, Con18], making it urgent to find a reliable way to share data. Today, the de facto standard for privacy protection is differential privacy (DP) [DR14]. DP ensures that useful information of a private dataset can be released while simultaneously preventing adversaries from identifying individuals' personal data. DP has been utilized effectively in a wide variety of settings, by actors including the US Census [Abo18] and various large corporations [App17, Fac20, RE19, HBMAL19, IBM23].\n\nGenerating private synthetic data is a crucial application of DP. It allows data scientists to train their ML models on the synthetic data while preserving a certain level of utility when deploying these models on real test data. The U.S. National Institute of Standards and Technology (NIST) has emphasized its significance by hosting a series of competitions [RTMT21, MMS21]. There is also significant work introducing new DP synthetic data generation mechanisms, including GAN-based [XLW + 18, BJWW + 19, JYVDS19, TWB + 19], marginal-based [ZCP + 17, MSM19, MMS21], and workload-based [VTB + 20, ABK + 21, LVW21, MMSM22, VAA + 22] methods. Existing methods for generating private synthetic data are task-agnostic-they do not take into account the downstream use cases of the synthetic data in the data generation process. However, end users often have specific requirements for synthetic datasets to be successfully analyzed by their existing data science pipelines, which are often well-established, well-understood, heavily vetted, and difficult or expensive to change. Unfortunately, synthetic data generated by existing methods may not always meet these requirements, resulting in reduced utility for their downstream use cases. This raises a fundamental question:\n\nAnswer the following question: What has been done recently in the area of synthetic data generation? Give it to me in bullets with their associated citations.\n"], "reference": ["Here's some recent work in the area of synthetic data generation:\n- The U.S. National Institute of Standards and Technology (NIST) has emphasized its significance by hosting a series of competitions [RTMT21, MMS21].\n- There is also significant work introducing new DP synthetic data generation mechanisms. Methods include\n - GAN-based [XLW+18, BJWW+19, JYVDS19, TWB+19]\n - Marginal-based [ZCP+17, MSM19, MMS21]\n - Workload-based [VTB+20, ABK+21, LVW21, MMSM22, VAA+22]\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/abstract/plain_text/qna.yaml", "question_id": "149145430129109704764226017070049879526", "category": "taxonomy", "turns": ["Given the context below:\n## Post-processing Private Synthetic Data for Improving Utility on Selected Measures\n\nHao Wang, Shivchander Sudalairaj, John Henning, Kristjan Greenewald, Akash Srivastava \u2217 MIT-IBM Watson AI Lab\n\n## Abstract\n\nExisting private synthetic data generation algorithms are agnostic to downstream tasks. However, end users may have specific requirements that the synthetic data must satisfy. Failure to meet these requirements could significantly reduce the utility of the data for downstream use. We introduce a post-processing technique that improves the utility of the synthetic data with respect to measures selected by the end user, while preserving strong privacy guarantees and dataset quality. Our technique involves resampling from the synthetic data to filter out samples that do not meet the selected utility measures, using an efficient stochastic first-order algorithm to find optimal resampling weights. Through comprehensive numerical experiments, we demonstrate that our approach consistently improves the utility of synthetic data across multiple benchmark datasets and state-of-the-art synthetic data generation algorithms.\n\n## 1 Introduction\n\nThe advancement of machine learning (ML) techniques relies on large amounts of training data. However, data collection also poses a significant risk of exposing private information. In recent years, several instances of privacy breaches have surfaced [NS06, Con18], making it urgent to find a reliable way to share data. Today, the de facto standard for privacy protection is differential privacy (DP) [DR14]. DP ensures that useful information of a private dataset can be released while simultaneously preventing adversaries from identifying individuals' personal data. DP has been utilized effectively in a wide variety of settings, by actors including the US Census [Abo18] and various large corporations [App17, Fac20, RE19, HBMAL19, IBM23].\n\nGenerating private synthetic data is a crucial application of DP. It allows data scientists to train their ML models on the synthetic data while preserving a certain level of utility when deploying these models on real test data. The U.S. National Institute of Standards and Technology (NIST) has emphasized its significance by hosting a series of competitions [RTMT21, MMS21]. There is also significant work introducing new DP synthetic data generation mechanisms, including GAN-based [XLW + 18, BJWW + 19, JYVDS19, TWB + 19], marginal-based [ZCP + 17, MSM19, MMS21], and workload-based [VTB + 20, ABK + 21, LVW21, MMSM22, VAA + 22] methods. Existing methods for generating private synthetic data are task-agnostic-they do not take into account the downstream use cases of the synthetic data in the data generation process. However, end users often have specific requirements for synthetic datasets to be successfully analyzed by their existing data science pipelines, which are often well-established, well-understood, heavily vetted, and difficult or expensive to change. Unfortunately, synthetic data generated by existing methods may not always meet these requirements, resulting in reduced utility for their downstream use cases. This raises a fundamental question:\n\nAnswer the following question: What is the main contribution of this paper in 2 sentences?\n"], "reference": ["This paper introduces a post-processing technique to improve the utility of the synthetic data with respect to measures selected by the end user, while preserving strong privacy guarantees and dataset quality. This advancement improves the utility of data for downstream use.\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/abstract/plain_text/qna.yaml", "question_id": "92434087613919199160038524320138085442", "category": "taxonomy", "turns": ["Given the context below:\n## Post-processing Private Synthetic Data for Improving Utility on Selected Measures\n\nHao Wang, Shivchander Sudalairaj, John Henning, Kristjan Greenewald, Akash Srivastava \u2217 MIT-IBM Watson AI Lab\n\n## Abstract\n\nExisting private synthetic data generation algorithms are agnostic to downstream tasks. However, end users may have specific requirements that the synthetic data must satisfy. Failure to meet these requirements could significantly reduce the utility of the data for downstream use. We introduce a post-processing technique that improves the utility of the synthetic data with respect to measures selected by the end user, while preserving strong privacy guarantees and dataset quality. Our technique involves resampling from the synthetic data to filter out samples that do not meet the selected utility measures, using an efficient stochastic first-order algorithm to find optimal resampling weights. Through comprehensive numerical experiments, we demonstrate that our approach consistently improves the utility of synthetic data across multiple benchmark datasets and state-of-the-art synthetic data generation algorithms.\n\n## 1 Introduction\n\nThe advancement of machine learning (ML) techniques relies on large amounts of training data. However, data collection also poses a significant risk of exposing private information. In recent years, several instances of privacy breaches have surfaced [NS06, Con18], making it urgent to find a reliable way to share data. Today, the de facto standard for privacy protection is differential privacy (DP) [DR14]. DP ensures that useful information of a private dataset can be released while simultaneously preventing adversaries from identifying individuals' personal data. DP has been utilized effectively in a wide variety of settings, by actors including the US Census [Abo18] and various large corporations [App17, Fac20, RE19, HBMAL19, IBM23].\n\nGenerating private synthetic data is a crucial application of DP. It allows data scientists to train their ML models on the synthetic data while preserving a certain level of utility when deploying these models on real test data. The U.S. National Institute of Standards and Technology (NIST) has emphasized its significance by hosting a series of competitions [RTMT21, MMS21]. There is also significant work introducing new DP synthetic data generation mechanisms, including GAN-based [XLW + 18, BJWW + 19, JYVDS19, TWB + 19], marginal-based [ZCP + 17, MSM19, MMS21], and workload-based [VTB + 20, ABK + 21, LVW21, MMSM22, VAA + 22] methods. Existing methods for generating private synthetic data are task-agnostic-they do not take into account the downstream use cases of the synthetic data in the data generation process. However, end users often have specific requirements for synthetic datasets to be successfully analyzed by their existing data science pipelines, which are often well-established, well-understood, heavily vetted, and difficult or expensive to change. Unfortunately, synthetic data generated by existing methods may not always meet these requirements, resulting in reduced utility for their downstream use cases. This raises a fundamental question:\n\nAnswer the following question: What makes it better than other techniques?"], "reference": ["The authors resampled from the synthetic data to filter out samples that do not meet the selected utility measures. They used an efficient stochastic first-order algorithm to find optimal resampling weights.\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/abstract/plain_text/qna.yaml", "question_id": "329410939538440337641053457179341173384", "category": "taxonomy", "turns": ["Given the context below:\n## Post-processing Private Synthetic Data for Improving Utility on Selected Measures\n\nHao Wang, Shivchander Sudalairaj, John Henning, Kristjan Greenewald, Akash Srivastava \u2217 MIT-IBM Watson AI Lab\n\n## Abstract\n\nExisting private synthetic data generation algorithms are agnostic to downstream tasks. However, end users may have specific requirements that the synthetic data must satisfy. Failure to meet these requirements could significantly reduce the utility of the data for downstream use. We introduce a post-processing technique that improves the utility of the synthetic data with respect to measures selected by the end user, while preserving strong privacy guarantees and dataset quality. Our technique involves resampling from the synthetic data to filter out samples that do not meet the selected utility measures, using an efficient stochastic first-order algorithm to find optimal resampling weights. Through comprehensive numerical experiments, we demonstrate that our approach consistently improves the utility of synthetic data across multiple benchmark datasets and state-of-the-art synthetic data generation algorithms.\n\n## 1 Introduction\n\nThe advancement of machine learning (ML) techniques relies on large amounts of training data. However, data collection also poses a significant risk of exposing private information. In recent years, several instances of privacy breaches have surfaced [NS06, Con18], making it urgent to find a reliable way to share data. Today, the de facto standard for privacy protection is differential privacy (DP) [DR14]. DP ensures that useful information of a private dataset can be released while simultaneously preventing adversaries from identifying individuals' personal data. DP has been utilized effectively in a wide variety of settings, by actors including the US Census [Abo18] and various large corporations [App17, Fac20, RE19, HBMAL19, IBM23].\n\nGenerating private synthetic data is a crucial application of DP. It allows data scientists to train their ML models on the synthetic data while preserving a certain level of utility when deploying these models on real test data. The U.S. National Institute of Standards and Technology (NIST) has emphasized its significance by hosting a series of competitions [RTMT21, MMS21]. There is also significant work introducing new DP synthetic data generation mechanisms, including GAN-based [XLW + 18, BJWW + 19, JYVDS19, TWB + 19], marginal-based [ZCP + 17, MSM19, MMS21], and workload-based [VTB + 20, ABK + 21, LVW21, MMSM22, VAA + 22] methods. Existing methods for generating private synthetic data are task-agnostic-they do not take into account the downstream use cases of the synthetic data in the data generation process. However, end users often have specific requirements for synthetic datasets to be successfully analyzed by their existing data science pipelines, which are often well-established, well-understood, heavily vetted, and difficult or expensive to change. Unfortunately, synthetic data generated by existing methods may not always meet these requirements, resulting in reduced utility for their downstream use cases. This raises a fundamental question:\n\nAnswer the following question: How did they demonstrate the success of this method?\n"], "reference": ["Through comprehensive numerical experiments, the authors demonstrated that the approach they outlined improves the utility of synthetic data across multiple benchmark datasets and state-of-the-art synthetic data generation algorithms.\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/abstract/markdown/qna.yaml", "question_id": "216297385644928817206333275875023284164", "category": "taxonomy", "turns": ["Given the context below:\n## Post-processing Private Synthetic Data for Improving Utility on Selected Measures\n\nHao Wang, Shivchander Sudalairaj, John Henning, Kristjan Greenewald, Akash Srivastava \u2217 MIT-IBM Watson AI Lab\n\n## Abstract\n\nExisting private synthetic data generation algorithms are agnostic to downstream tasks. However, end users may have specific requirements that the synthetic data must satisfy. Failure to meet these requirements could significantly reduce the utility of the data for downstream use. We introduce a post-processing technique that improves the utility of the synthetic data with respect to measures selected by the end user, while preserving strong privacy guarantees and dataset quality. Our technique involves resampling from the synthetic data to filter out samples that do not meet the selected utility measures, using an efficient stochastic first-order algorithm to find optimal resampling weights. Through comprehensive numerical experiments, we demonstrate that our approach consistently improves the utility of synthetic data across multiple benchmark datasets and state-of-the-art synthetic data generation algorithms.\n\n## 1 Introduction\n\nThe advancement of machine learning (ML) techniques relies on large amounts of training data. However, data collection also poses a significant risk of exposing private information. In recent years, several instances of privacy breaches have surfaced [NS06, Con18], making it urgent to find a reliable way to share data. Today, the de facto standard for privacy protection is differential privacy (DP) [DR14]. DP ensures that useful information of a private dataset can be released while simultaneously preventing adversaries from identifying individuals' personal data. DP has been utilized effectively in a wide variety of settings, by actors including the US Census [Abo18] and various large corporations [App17, Fac20, RE19, HBMAL19, IBM23].\n\nGenerating private synthetic data is a crucial application of DP. It allows data scientists to train their ML models on the synthetic data while preserving a certain level of utility when deploying these models on real test data. The U.S. National Institute of Standards and Technology (NIST) has emphasized its significance by hosting a series of competitions [RTMT21, MMS21]. There is also significant work introducing new DP synthetic data generation mechanisms, including GAN-based [XLW + 18, BJWW + 19, JYVDS19, TWB + 19], marginal-based [ZCP + 17, MSM19, MMS21], and workload-based [VTB + 20, ABK + 21, LVW21, MMSM22, VAA + 22] methods. Existing methods for generating private synthetic data are task-agnostic-they do not take into account the downstream use cases of the synthetic data in the data generation process. However, end users often have specific requirements for synthetic datasets to be successfully analyzed by their existing data science pipelines, which are often well-established, well-understood, heavily vetted, and difficult or expensive to change. Unfortunately, synthetic data generated by existing methods may not always meet these requirements, resulting in reduced utility for their downstream use cases. This raises a fundamental question:\n\nAnswer the following question: Create a table in markdown of the authors and where they're from.\n"], "reference": ["```\n| Author | Affiliation |\n| ---------------------- | --------------------- |\n| Hao Wang | MIT-IBM Watson AI Lab |\n| Shivchander Sudalairaj | MIT-IBM Watson AI Lab |\n| John Henning | MIT-IBM Watson AI Lab |\n| Kristjan Greenewald | MIT-IBM Watson AI Lab |\n| Akash Srivastava | MIT-IBM Watson AI Lab |\n```\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/abstract/markdown/qna.yaml", "question_id": "93302405522548825168795297289045637921", "category": "taxonomy", "turns": ["Given the context below:\n## Post-processing Private Synthetic Data for Improving Utility on Selected Measures\n\nHao Wang, Shivchander Sudalairaj, John Henning, Kristjan Greenewald, Akash Srivastava \u2217 MIT-IBM Watson AI Lab\n\n## Abstract\n\nExisting private synthetic data generation algorithms are agnostic to downstream tasks. However, end users may have specific requirements that the synthetic data must satisfy. Failure to meet these requirements could significantly reduce the utility of the data for downstream use. We introduce a post-processing technique that improves the utility of the synthetic data with respect to measures selected by the end user, while preserving strong privacy guarantees and dataset quality. Our technique involves resampling from the synthetic data to filter out samples that do not meet the selected utility measures, using an efficient stochastic first-order algorithm to find optimal resampling weights. Through comprehensive numerical experiments, we demonstrate that our approach consistently improves the utility of synthetic data across multiple benchmark datasets and state-of-the-art synthetic data generation algorithms.\n\n## 1 Introduction\n\nThe advancement of machine learning (ML) techniques relies on large amounts of training data. However, data collection also poses a significant risk of exposing private information. In recent years, several instances of privacy breaches have surfaced [NS06, Con18], making it urgent to find a reliable way to share data. Today, the de facto standard for privacy protection is differential privacy (DP) [DR14]. DP ensures that useful information of a private dataset can be released while simultaneously preventing adversaries from identifying individuals' personal data. DP has been utilized effectively in a wide variety of settings, by actors including the US Census [Abo18] and various large corporations [App17, Fac20, RE19, HBMAL19, IBM23].\n\nGenerating private synthetic data is a crucial application of DP. It allows data scientists to train their ML models on the synthetic data while preserving a certain level of utility when deploying these models on real test data. The U.S. National Institute of Standards and Technology (NIST) has emphasized its significance by hosting a series of competitions [RTMT21, MMS21]. There is also significant work introducing new DP synthetic data generation mechanisms, including GAN-based [XLW + 18, BJWW + 19, JYVDS19, TWB + 19], marginal-based [ZCP + 17, MSM19, MMS21], and workload-based [VTB + 20, ABK + 21, LVW21, MMSM22, VAA + 22] methods. Existing methods for generating private synthetic data are task-agnostic-they do not take into account the downstream use cases of the synthetic data in the data generation process. However, end users often have specific requirements for synthetic datasets to be successfully analyzed by their existing data science pipelines, which are often well-established, well-understood, heavily vetted, and difficult or expensive to change. Unfortunately, synthetic data generated by existing methods may not always meet these requirements, resulting in reduced utility for their downstream use cases. This raises a fundamental question:\n\nAnswer the following question: Create a table in markdown listing the place and citations related to where differential privacy has been used."], "reference": ["```\n| Location | Citations |\n| -------------------------- | ------------------------------------ |\n| US Census | [Abo18] |\n| Various Large Corporations | [App17, Fac20, RE19, HBMAL19, IBM23] |\n```\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/abstract/markdown/qna.yaml", "question_id": "130769870589161654695899255277827228849", "category": "taxonomy", "turns": ["Given the context below:\n## Post-processing Private Synthetic Data for Improving Utility on Selected Measures\n\nHao Wang, Shivchander Sudalairaj, John Henning, Kristjan Greenewald, Akash Srivastava \u2217 MIT-IBM Watson AI Lab\n\n## Abstract\n\nExisting private synthetic data generation algorithms are agnostic to downstream tasks. However, end users may have specific requirements that the synthetic data must satisfy. Failure to meet these requirements could significantly reduce the utility of the data for downstream use. We introduce a post-processing technique that improves the utility of the synthetic data with respect to measures selected by the end user, while preserving strong privacy guarantees and dataset quality. Our technique involves resampling from the synthetic data to filter out samples that do not meet the selected utility measures, using an efficient stochastic first-order algorithm to find optimal resampling weights. Through comprehensive numerical experiments, we demonstrate that our approach consistently improves the utility of synthetic data across multiple benchmark datasets and state-of-the-art synthetic data generation algorithms.\n\n## 1 Introduction\n\nThe advancement of machine learning (ML) techniques relies on large amounts of training data. However, data collection also poses a significant risk of exposing private information. In recent years, several instances of privacy breaches have surfaced [NS06, Con18], making it urgent to find a reliable way to share data. Today, the de facto standard for privacy protection is differential privacy (DP) [DR14]. DP ensures that useful information of a private dataset can be released while simultaneously preventing adversaries from identifying individuals' personal data. DP has been utilized effectively in a wide variety of settings, by actors including the US Census [Abo18] and various large corporations [App17, Fac20, RE19, HBMAL19, IBM23].\n\nGenerating private synthetic data is a crucial application of DP. It allows data scientists to train their ML models on the synthetic data while preserving a certain level of utility when deploying these models on real test data. The U.S. National Institute of Standards and Technology (NIST) has emphasized its significance by hosting a series of competitions [RTMT21, MMS21]. There is also significant work introducing new DP synthetic data generation mechanisms, including GAN-based [XLW + 18, BJWW + 19, JYVDS19, TWB + 19], marginal-based [ZCP + 17, MSM19, MMS21], and workload-based [VTB + 20, ABK + 21, LVW21, MMSM22, VAA + 22] methods. Existing methods for generating private synthetic data are task-agnostic-they do not take into account the downstream use cases of the synthetic data in the data generation process. However, end users often have specific requirements for synthetic datasets to be successfully analyzed by their existing data science pipelines, which are often well-established, well-understood, heavily vetted, and difficult or expensive to change. Unfortunately, synthetic data generated by existing methods may not always meet these requirements, resulting in reduced utility for their downstream use cases. This raises a fundamental question:\n\nAnswer the following question: Create a table in markdown of each type of synthetic data generation technique listed in the literature review. Make sure the table has 2 columns. The first is the technique and the second is the citations. Make sure all the citations for each technique fit in the 2nd column with a comma separating each - don't make a new column for each citation.\n"], "reference": ["```\n| Technique | Citation |\n| -------------- | --------------------------------------- |\n| GAN-based | [XLW+18, BJWW+19, JYVDS19, TWB+19] |\n| Marginal-based | [ZCP+17, MSM19, MMS21] |\n| Workload-based | [VTB+20, ABK+21, LVW21, MMSM22, VAA+22] |\n```\n\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/abstract/reasoning/qna.yaml", "question_id": "206353909755124922772338273890504728318", "category": "taxonomy", "turns": ["Given the context below:\n## Post-processing Private Synthetic Data for Improving Utility on Selected Measures\n\nHao Wang, Shivchander Sudalairaj, John Henning, Kristjan Greenewald, Akash Srivastava \u2217 MIT-IBM Watson AI Lab\n\n## Abstract\n\nExisting private synthetic data generation algorithms are agnostic to downstream tasks. However, end users may have specific requirements that the synthetic data must satisfy. Failure to meet these requirements could significantly reduce the utility of the data for downstream use. We introduce a post-processing technique that improves the utility of the synthetic data with respect to measures selected by the end user, while preserving strong privacy guarantees and dataset quality. Our technique involves resampling from the synthetic data to filter out samples that do not meet the selected utility measures, using an efficient stochastic first-order algorithm to find optimal resampling weights. Through comprehensive numerical experiments, we demonstrate that our approach consistently improves the utility of synthetic data across multiple benchmark datasets and state-of-the-art synthetic data generation algorithms.\n\n## 1 Introduction\n\nThe advancement of machine learning (ML) techniques relies on large amounts of training data. However, data collection also poses a significant risk of exposing private information. In recent years, several instances of privacy breaches have surfaced [NS06, Con18], making it urgent to find a reliable way to share data. Today, the de facto standard for privacy protection is differential privacy (DP) [DR14]. DP ensures that useful information of a private dataset can be released while simultaneously preventing adversaries from identifying individuals' personal data. DP has been utilized effectively in a wide variety of settings, by actors including the US Census [Abo18] and various large corporations [App17, Fac20, RE19, HBMAL19, IBM23].\n\nGenerating private synthetic data is a crucial application of DP. It allows data scientists to train their ML models on the synthetic data while preserving a certain level of utility when deploying these models on real test data. The U.S. National Institute of Standards and Technology (NIST) has emphasized its significance by hosting a series of competitions [RTMT21, MMS21]. There is also significant work introducing new DP synthetic data generation mechanisms, including GAN-based [XLW + 18, BJWW + 19, JYVDS19, TWB + 19], marginal-based [ZCP + 17, MSM19, MMS21], and workload-based [VTB + 20, ABK + 21, LVW21, MMSM22, VAA + 22] methods. Existing methods for generating private synthetic data are task-agnostic-they do not take into account the downstream use cases of the synthetic data in the data generation process. However, end users often have specific requirements for synthetic datasets to be successfully analyzed by their existing data science pipelines, which are often well-established, well-understood, heavily vetted, and difficult or expensive to change. Unfortunately, synthetic data generated by existing methods may not always meet these requirements, resulting in reduced utility for their downstream use cases. This raises a fundamental question:\n\nAnswer the following question: What is the structure of the abstract?\n"], "reference": ["This abstract has several parts. First, the authors describe the problems with existing approaches. They then outline why it is a problem. The next step is to describe the novel approach the paper takes. The authors describe their results and their implications.\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/abstract/reasoning/qna.yaml", "question_id": "334743913775917281476168325726955815409", "category": "taxonomy", "turns": ["Given the context below:\n## Post-processing Private Synthetic Data for Improving Utility on Selected Measures\n\nHao Wang, Shivchander Sudalairaj, John Henning, Kristjan Greenewald, Akash Srivastava \u2217 MIT-IBM Watson AI Lab\n\n## Abstract\n\nExisting private synthetic data generation algorithms are agnostic to downstream tasks. However, end users may have specific requirements that the synthetic data must satisfy. Failure to meet these requirements could significantly reduce the utility of the data for downstream use. We introduce a post-processing technique that improves the utility of the synthetic data with respect to measures selected by the end user, while preserving strong privacy guarantees and dataset quality. Our technique involves resampling from the synthetic data to filter out samples that do not meet the selected utility measures, using an efficient stochastic first-order algorithm to find optimal resampling weights. Through comprehensive numerical experiments, we demonstrate that our approach consistently improves the utility of synthetic data across multiple benchmark datasets and state-of-the-art synthetic data generation algorithms.\n\n## 1 Introduction\n\nThe advancement of machine learning (ML) techniques relies on large amounts of training data. However, data collection also poses a significant risk of exposing private information. In recent years, several instances of privacy breaches have surfaced [NS06, Con18], making it urgent to find a reliable way to share data. Today, the de facto standard for privacy protection is differential privacy (DP) [DR14]. DP ensures that useful information of a private dataset can be released while simultaneously preventing adversaries from identifying individuals' personal data. DP has been utilized effectively in a wide variety of settings, by actors including the US Census [Abo18] and various large corporations [App17, Fac20, RE19, HBMAL19, IBM23].\n\nGenerating private synthetic data is a crucial application of DP. It allows data scientists to train their ML models on the synthetic data while preserving a certain level of utility when deploying these models on real test data. The U.S. National Institute of Standards and Technology (NIST) has emphasized its significance by hosting a series of competitions [RTMT21, MMS21]. There is also significant work introducing new DP synthetic data generation mechanisms, including GAN-based [XLW + 18, BJWW + 19, JYVDS19, TWB + 19], marginal-based [ZCP + 17, MSM19, MMS21], and workload-based [VTB + 20, ABK + 21, LVW21, MMSM22, VAA + 22] methods. Existing methods for generating private synthetic data are task-agnostic-they do not take into account the downstream use cases of the synthetic data in the data generation process. However, end users often have specific requirements for synthetic datasets to be successfully analyzed by their existing data science pipelines, which are often well-established, well-understood, heavily vetted, and difficult or expensive to change. Unfortunately, synthetic data generated by existing methods may not always meet these requirements, resulting in reduced utility for their downstream use cases. This raises a fundamental question:\n\nAnswer the following question: List 3 tags for this paper."], "reference": ["Synthetic data generation, post-processing techniques, data privacy\nThe primary topic of this paper is about synthetic data generation. The novel approach is around using a novel post-processing technique for the synthetic data generation. The benefit of this approach is to maintain data privacy while maintaining utility.\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/abstract/reasoning/qna.yaml", "question_id": "154346823130677374317780084435178670580", "category": "taxonomy", "turns": ["Given the context below:\n## Post-processing Private Synthetic Data for Improving Utility on Selected Measures\n\nHao Wang, Shivchander Sudalairaj, John Henning, Kristjan Greenewald, Akash Srivastava \u2217 MIT-IBM Watson AI Lab\n\n## Abstract\n\nExisting private synthetic data generation algorithms are agnostic to downstream tasks. However, end users may have specific requirements that the synthetic data must satisfy. Failure to meet these requirements could significantly reduce the utility of the data for downstream use. We introduce a post-processing technique that improves the utility of the synthetic data with respect to measures selected by the end user, while preserving strong privacy guarantees and dataset quality. Our technique involves resampling from the synthetic data to filter out samples that do not meet the selected utility measures, using an efficient stochastic first-order algorithm to find optimal resampling weights. Through comprehensive numerical experiments, we demonstrate that our approach consistently improves the utility of synthetic data across multiple benchmark datasets and state-of-the-art synthetic data generation algorithms.\n\n## 1 Introduction\n\nThe advancement of machine learning (ML) techniques relies on large amounts of training data. However, data collection also poses a significant risk of exposing private information. In recent years, several instances of privacy breaches have surfaced [NS06, Con18], making it urgent to find a reliable way to share data. Today, the de facto standard for privacy protection is differential privacy (DP) [DR14]. DP ensures that useful information of a private dataset can be released while simultaneously preventing adversaries from identifying individuals' personal data. DP has been utilized effectively in a wide variety of settings, by actors including the US Census [Abo18] and various large corporations [App17, Fac20, RE19, HBMAL19, IBM23].\n\nGenerating private synthetic data is a crucial application of DP. It allows data scientists to train their ML models on the synthetic data while preserving a certain level of utility when deploying these models on real test data. The U.S. National Institute of Standards and Technology (NIST) has emphasized its significance by hosting a series of competitions [RTMT21, MMS21]. There is also significant work introducing new DP synthetic data generation mechanisms, including GAN-based [XLW + 18, BJWW + 19, JYVDS19, TWB + 19], marginal-based [ZCP + 17, MSM19, MMS21], and workload-based [VTB + 20, ABK + 21, LVW21, MMSM22, VAA + 22] methods. Existing methods for generating private synthetic data are task-agnostic-they do not take into account the downstream use cases of the synthetic data in the data generation process. However, end users often have specific requirements for synthetic datasets to be successfully analyzed by their existing data science pipelines, which are often well-established, well-understood, heavily vetted, and difficult or expensive to change. Unfortunately, synthetic data generated by existing methods may not always meet these requirements, resulting in reduced utility for their downstream use cases. This raises a fundamental question:\n\nAnswer the following question: Who might want to read this paper that isn't specifically doing research in this area?\n"], "reference": ["People interested in synthetic data generation because of privacy concerns. \nThe authors describe interested parties as broad as the US Census and large corporations because those groups have interest in protecting data privacy. \n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/abstract/csv/qna.yaml", "question_id": "79901913399679660614872524204723845770", "category": "taxonomy", "turns": ["Given the context below:\n## Post-processing Private Synthetic Data for Improving Utility on Selected Measures\n\nHao Wang, Shivchander Sudalairaj, John Henning, Kristjan Greenewald, Akash Srivastava \u2217 MIT-IBM Watson AI Lab\n\n## Abstract\n\nExisting private synthetic data generation algorithms are agnostic to downstream tasks. However, end users may have specific requirements that the synthetic data must satisfy. Failure to meet these requirements could significantly reduce the utility of the data for downstream use. We introduce a post-processing technique that improves the utility of the synthetic data with respect to measures selected by the end user, while preserving strong privacy guarantees and dataset quality. Our technique involves resampling from the synthetic data to filter out samples that do not meet the selected utility measures, using an efficient stochastic first-order algorithm to find optimal resampling weights. Through comprehensive numerical experiments, we demonstrate that our approach consistently improves the utility of synthetic data across multiple benchmark datasets and state-of-the-art synthetic data generation algorithms.\n\n## 1 Introduction\n\nThe advancement of machine learning (ML) techniques relies on large amounts of training data. However, data collection also poses a significant risk of exposing private information. In recent years, several instances of privacy breaches have surfaced [NS06, Con18], making it urgent to find a reliable way to share data. Today, the de facto standard for privacy protection is differential privacy (DP) [DR14]. DP ensures that useful information of a private dataset can be released while simultaneously preventing adversaries from identifying individuals' personal data. DP has been utilized effectively in a wide variety of settings, by actors including the US Census [Abo18] and various large corporations [App17, Fac20, RE19, HBMAL19, IBM23].\n\nGenerating private synthetic data is a crucial application of DP. It allows data scientists to train their ML models on the synthetic data while preserving a certain level of utility when deploying these models on real test data. The U.S. National Institute of Standards and Technology (NIST) has emphasized its significance by hosting a series of competitions [RTMT21, MMS21]. There is also significant work introducing new DP synthetic data generation mechanisms, including GAN-based [XLW + 18, BJWW + 19, JYVDS19, TWB + 19], marginal-based [ZCP + 17, MSM19, MMS21], and workload-based [VTB + 20, ABK + 21, LVW21, MMSM22, VAA + 22] methods. Existing methods for generating private synthetic data are task-agnostic-they do not take into account the downstream use cases of the synthetic data in the data generation process. However, end users often have specific requirements for synthetic datasets to be successfully analyzed by their existing data science pipelines, which are often well-established, well-understood, heavily vetted, and difficult or expensive to change. Unfortunately, synthetic data generated by existing methods may not always meet these requirements, resulting in reduced utility for their downstream use cases. This raises a fundamental question:\n\nAnswer the following question: Create a table of the authors, their affiliations, and emails. Return it in csv format.\n"], "reference": ["Author,Affiliation,Email\nHao Wang,MIT-IBM Watson AI Lab,hao@ibm.com\nShivchander Sudalairaj,MIT-IBM Watson AI Lab,shiv.sr@ibm.com\nJohn Henning,MIT-IBM Watson AI Lab,john.l.henning@ibm.com\nKristjan Greenewald,MIT-IBM Watson AI Lab,kristjan.h.greenewald@ibm.com\nAkash Srivastava,MIT-IBM Watson AI Lab,akash.srivastava@ibm.com\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/abstract/csv/qna.yaml", "question_id": "7884975629872746087812279157971190540", "category": "taxonomy", "turns": ["Given the context below:\n## Post-processing Private Synthetic Data for Improving Utility on Selected Measures\n\nHao Wang, Shivchander Sudalairaj, John Henning, Kristjan Greenewald, Akash Srivastava \u2217 MIT-IBM Watson AI Lab\n\n## Abstract\n\nExisting private synthetic data generation algorithms are agnostic to downstream tasks. However, end users may have specific requirements that the synthetic data must satisfy. Failure to meet these requirements could significantly reduce the utility of the data for downstream use. We introduce a post-processing technique that improves the utility of the synthetic data with respect to measures selected by the end user, while preserving strong privacy guarantees and dataset quality. Our technique involves resampling from the synthetic data to filter out samples that do not meet the selected utility measures, using an efficient stochastic first-order algorithm to find optimal resampling weights. Through comprehensive numerical experiments, we demonstrate that our approach consistently improves the utility of synthetic data across multiple benchmark datasets and state-of-the-art synthetic data generation algorithms.\n\n## 1 Introduction\n\nThe advancement of machine learning (ML) techniques relies on large amounts of training data. However, data collection also poses a significant risk of exposing private information. In recent years, several instances of privacy breaches have surfaced [NS06, Con18], making it urgent to find a reliable way to share data. Today, the de facto standard for privacy protection is differential privacy (DP) [DR14]. DP ensures that useful information of a private dataset can be released while simultaneously preventing adversaries from identifying individuals' personal data. DP has been utilized effectively in a wide variety of settings, by actors including the US Census [Abo18] and various large corporations [App17, Fac20, RE19, HBMAL19, IBM23].\n\nGenerating private synthetic data is a crucial application of DP. It allows data scientists to train their ML models on the synthetic data while preserving a certain level of utility when deploying these models on real test data. The U.S. National Institute of Standards and Technology (NIST) has emphasized its significance by hosting a series of competitions [RTMT21, MMS21]. There is also significant work introducing new DP synthetic data generation mechanisms, including GAN-based [XLW + 18, BJWW + 19, JYVDS19, TWB + 19], marginal-based [ZCP + 17, MSM19, MMS21], and workload-based [VTB + 20, ABK + 21, LVW21, MMSM22, VAA + 22] methods. Existing methods for generating private synthetic data are task-agnostic-they do not take into account the downstream use cases of the synthetic data in the data generation process. However, end users often have specific requirements for synthetic datasets to be successfully analyzed by their existing data science pipelines, which are often well-established, well-understood, heavily vetted, and difficult or expensive to change. Unfortunately, synthetic data generated by existing methods may not always meet these requirements, resulting in reduced utility for their downstream use cases. This raises a fundamental question:\n\nAnswer the following question: List the citations for each of the following in a .csv.\nGAN-based, marginal-based and workload-based DP synthetic data generation mechanisms. Make each item a row and the citation a column."], "reference": ["GAN-based,XLW+18,BJWW+19,JYVDS19,TWB+19,\nMarginal-based,ZCP+17,MSM19,MMS21,,\nWorkload-based,VTB+20,ABK+21,LVW21,MMSM22,VAA+22\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/abstract/csv/qna.yaml", "question_id": "230860748078397119410681161275396330891", "category": "taxonomy", "turns": ["Given the context below:\n## Post-processing Private Synthetic Data for Improving Utility on Selected Measures\n\nHao Wang, Shivchander Sudalairaj, John Henning, Kristjan Greenewald, Akash Srivastava \u2217 MIT-IBM Watson AI Lab\n\n## Abstract\n\nExisting private synthetic data generation algorithms are agnostic to downstream tasks. However, end users may have specific requirements that the synthetic data must satisfy. Failure to meet these requirements could significantly reduce the utility of the data for downstream use. We introduce a post-processing technique that improves the utility of the synthetic data with respect to measures selected by the end user, while preserving strong privacy guarantees and dataset quality. Our technique involves resampling from the synthetic data to filter out samples that do not meet the selected utility measures, using an efficient stochastic first-order algorithm to find optimal resampling weights. Through comprehensive numerical experiments, we demonstrate that our approach consistently improves the utility of synthetic data across multiple benchmark datasets and state-of-the-art synthetic data generation algorithms.\n\n## 1 Introduction\n\nThe advancement of machine learning (ML) techniques relies on large amounts of training data. However, data collection also poses a significant risk of exposing private information. In recent years, several instances of privacy breaches have surfaced [NS06, Con18], making it urgent to find a reliable way to share data. Today, the de facto standard for privacy protection is differential privacy (DP) [DR14]. DP ensures that useful information of a private dataset can be released while simultaneously preventing adversaries from identifying individuals' personal data. DP has been utilized effectively in a wide variety of settings, by actors including the US Census [Abo18] and various large corporations [App17, Fac20, RE19, HBMAL19, IBM23].\n\nGenerating private synthetic data is a crucial application of DP. It allows data scientists to train their ML models on the synthetic data while preserving a certain level of utility when deploying these models on real test data. The U.S. National Institute of Standards and Technology (NIST) has emphasized its significance by hosting a series of competitions [RTMT21, MMS21]. There is also significant work introducing new DP synthetic data generation mechanisms, including GAN-based [XLW + 18, BJWW + 19, JYVDS19, TWB + 19], marginal-based [ZCP + 17, MSM19, MMS21], and workload-based [VTB + 20, ABK + 21, LVW21, MMSM22, VAA + 22] methods. Existing methods for generating private synthetic data are task-agnostic-they do not take into account the downstream use cases of the synthetic data in the data generation process. However, end users often have specific requirements for synthetic datasets to be successfully analyzed by their existing data science pipelines, which are often well-established, well-understood, heavily vetted, and difficult or expensive to change. Unfortunately, synthetic data generated by existing methods may not always meet these requirements, resulting in reduced utility for their downstream use cases. This raises a fundamental question:\n\nAnswer the following question: Create a csv that lists the number of citations given for GAN-based, Marginal-based, and Workload based mechanisms. Where each row is the type and the column is the number of citations given for each.\n"], "reference": ["Type, Number of citations\nGAN-based,4\nMarginal-based,3\nWorkload-based,5\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/tables/bullet_points/qna.yaml", "question_id": "45105405514356068342287134348038050391", "category": "taxonomy", "turns": ["Given the context below:\nTable 1: We compare synthetic data generated without and with our post-processing technique, all under the same privacy budget \u03f5 =2. We demonstrate utility improvement (higher is better, positive numbers imply improvement) and F1 score for downstream models trained on synthetic data and tested on real data. For reference, when training the same downstream model using real data, the F1 scores are: (Adult \u21aa 0 \u00b1 61), (Mushroom \u21aa 0 \u00b1 95), (Shopper \u21aa 0 \u00b1 54), and (Bank \u21aa 0 \u00b1 47). Additionally, we measure the average Jensen-Shannon (JS) distance between the marginal distributions of synthetic and real data (0: identical distribution; 1: totally different distributions) and the average inverse KL-divergence (0: totally different distributions; 1: identical distribution). As shown, our technique consistently improves the utility of the synthetic data across all datasets and all DP mechanisms without degrading the performance of downstream models or statistical metrics.\n\n|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| F1 score\u00a0\u00a0\u00a0\u00a0| F1 score\u00a0\u00a0\u00a0\u00a0\u00a0| JS distance (marginal)\u00a0\u00a0| JS distance (marginal)\u00a0\u00a0| Inverse KL (marginal)\u00a0\u00a0| Inverse KL (marginal)\u00a0\u00a0|\n|----------|--------------|-----------------|----------------|-------------------|--------------------------|--------------------------|-------------------------|-------------------------|\n| Dataset\u00a0| DP Mechanism | Utility Improv. | w/o post-proc. | w/ post-proc.\u00a0\u00a0\u00a0| w/o post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| w/ post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| w/o post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0| w/ post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Adult\u00a0\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.13 \u00b7 0 \u00b1 03\u00a0\u00a0| 0.61\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.61\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.22 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.55\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.56\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.81 \u00b7 0 \u00b1 09\u00a0\u00a0| 0.22\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.33\u00b7 0 \u00b1 02 | 0.07\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.03\u00b7 0 \u00b1 02\u00a0\u00a0\u00a0\u00a0| 0.85\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.91\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.6 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.37\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.5\u00b7 0 \u00b1 03\u00a0| 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.72\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.87\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Mushroom | AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.12 \u00b7 0 \u00b1 0\u00a0\u00a0| 0.93\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.93\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.58 \u00b7 0 \u00b1 0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00b7 0 \u00b1 02 | 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.69 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.47\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.68\u00b7 0 \u00b1 01 | 0.05\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.92\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.83 \u00b7 0 \u00b1 05\u00a0\u00a0| 0.6\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.86\u00b7 0 \u00b1 04 | 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.87\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.95\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Shopper\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.1 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.48\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.48\u00b7 0 \u00b1 02 | 0.02\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.84\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.92\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.5 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.42\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.47\u00b7 0 \u00b1 02 | 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.36 \u00b7 0 \u00b1 05\u00a0\u00a0| 0.27\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.3\u00b7 0 \u00b1 02\u00a0| 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.74\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.85\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.11 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.25\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.31\u00b7 0 \u00b1 05 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.89\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Bank\u00a0\u00a0\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.18 \u00b7 0 \u00b1 01\u00a0\u00a0| 0.45\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.46\u00b7 0 \u00b1 01 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.9\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.32 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.43\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.44\u00b7 0 \u00b1 02 | 0.02\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.98\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.2 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.22\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.24\u00b7 0 \u00b1 07 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0| 0.71\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.88\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.25 \u00b7 0 \u00b1 03\u00a0\u00a0| 0.2\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.23\u00b7 0 \u00b1 05 | 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.9\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 2: Experimental results on the home-credit dataset. We compare synthetic data produced by GEM with \u03f5 \u2208 \u00b6 2 \u21aa 4 \u2666 against the post-processed synthetic data that are generated by applying our post-processing technique with \u03f5$_{post =1 to synthetic data generated from GEM with \u03f5 \u2208 \u00b6 1 \u21aa 3 \u2666. For reference, the downstream model trained and tested on real data has an F1 score of 0 \u00b1 24.\n\n| DP Mechanism\u00a0\u00a0| Utility Improv.\u00a0\u00a0|\u00a0\u00a0F1 score w/o post-proc. | F1 score w/ post-proc.\u00a0\u00a0|\u00a0\u00a0JS distance w/o post-proc. | JS distance w/ post-proc.\u00a0\u00a0|\u00a0\u00a0Inverse KL w/o post-proc. | Inverse KL w/ post-proc.\u00a0\u00a0|\n|----------------|-------------------|---------------------------|--------------------------|------------------------------|-----------------------------|-----------------------------|----------------------------|\n| GEM \u03f5 =2\u00a0\u00a0\u00a0\u00a0| 0.57 \u00b7 0 \u00b1 03\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.19 | 0.19\u00b7 0 \u00b1 02\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.01 | 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.93 | 0.97\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0\u00a0|\n| GEM \u03f5 =4\u00a0\u00a0\u00a0\u00a0| 0.68 \u00b7 0 \u00b1 01\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.21 | 0.21\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.01 | 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.95 | 0.99\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nResult. We present the experimental results on the UCI datasets in Table 1 (and Table 3 in Appendix C with a higher privacy budget). We report utility improvement, F1 score, and statistical measures. As shown, our post-processing technique consistently enhances the utility of synthetic data on selected metrics across all benchmark datasets and all privacy mechanisms. Moreover, the utility improvements are achieved without degrading the performance of downstream models or statistical properties of synthetic data. In other words, our post-processing procedure ensures that the logistic regression classifier trained on synthetic data can achieve comparable or even higher performance on real test data, while simultaneously reducing or maintaining the statistical divergences between synthetic and real data.\n\nWe present the experimental results on the home-credit dataset in Table 2 and illustrate the misalignment of the correlation matrix with and without applying our post-processing procedure in Figure 1. The results demonstrate that our algorithm consistently reduces the overall correlation misalignment. Additionally, our procedure, which includes computing the utility measures from real data, denoising the noisy answers, and computing optimal resampling weights, only takes around 4 mins on 1x NVIDIA GeForce RTX 3090 GPU.\n\nAnswer the following question: List all the datasets used in this paper in bullet format.\n"], "reference": ["Datasets used for this paper:\n- Adult\n- Mushroom\n- Shopper\n- Bank\n- Home-credit\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/tables/bullet_points/qna.yaml", "question_id": "69294882860109184745270748007059173094", "category": "taxonomy", "turns": ["Given the context below:\nTable 1: We compare synthetic data generated without and with our post-processing technique, all under the same privacy budget \u03f5 =2. We demonstrate utility improvement (higher is better, positive numbers imply improvement) and F1 score for downstream models trained on synthetic data and tested on real data. For reference, when training the same downstream model using real data, the F1 scores are: (Adult \u21aa 0 \u00b1 61), (Mushroom \u21aa 0 \u00b1 95), (Shopper \u21aa 0 \u00b1 54), and (Bank \u21aa 0 \u00b1 47). Additionally, we measure the average Jensen-Shannon (JS) distance between the marginal distributions of synthetic and real data (0: identical distribution; 1: totally different distributions) and the average inverse KL-divergence (0: totally different distributions; 1: identical distribution). As shown, our technique consistently improves the utility of the synthetic data across all datasets and all DP mechanisms without degrading the performance of downstream models or statistical metrics.\n\n|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| F1 score\u00a0\u00a0\u00a0\u00a0| F1 score\u00a0\u00a0\u00a0\u00a0\u00a0| JS distance (marginal)\u00a0\u00a0| JS distance (marginal)\u00a0\u00a0| Inverse KL (marginal)\u00a0\u00a0| Inverse KL (marginal)\u00a0\u00a0|\n|----------|--------------|-----------------|----------------|-------------------|--------------------------|--------------------------|-------------------------|-------------------------|\n| Dataset\u00a0| DP Mechanism | Utility Improv. | w/o post-proc. | w/ post-proc.\u00a0\u00a0\u00a0| w/o post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| w/ post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| w/o post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0| w/ post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Adult\u00a0\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.13 \u00b7 0 \u00b1 03\u00a0\u00a0| 0.61\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.61\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.22 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.55\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.56\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.81 \u00b7 0 \u00b1 09\u00a0\u00a0| 0.22\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.33\u00b7 0 \u00b1 02 | 0.07\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.03\u00b7 0 \u00b1 02\u00a0\u00a0\u00a0\u00a0| 0.85\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.91\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.6 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.37\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.5\u00b7 0 \u00b1 03\u00a0| 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.72\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.87\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Mushroom | AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.12 \u00b7 0 \u00b1 0\u00a0\u00a0| 0.93\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.93\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.58 \u00b7 0 \u00b1 0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00b7 0 \u00b1 02 | 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.69 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.47\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.68\u00b7 0 \u00b1 01 | 0.05\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.92\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.83 \u00b7 0 \u00b1 05\u00a0\u00a0| 0.6\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.86\u00b7 0 \u00b1 04 | 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.87\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.95\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Shopper\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.1 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.48\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.48\u00b7 0 \u00b1 02 | 0.02\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.84\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.92\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.5 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.42\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.47\u00b7 0 \u00b1 02 | 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.36 \u00b7 0 \u00b1 05\u00a0\u00a0| 0.27\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.3\u00b7 0 \u00b1 02\u00a0| 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.74\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.85\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.11 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.25\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.31\u00b7 0 \u00b1 05 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.89\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Bank\u00a0\u00a0\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.18 \u00b7 0 \u00b1 01\u00a0\u00a0| 0.45\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.46\u00b7 0 \u00b1 01 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.9\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.32 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.43\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.44\u00b7 0 \u00b1 02 | 0.02\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.98\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.2 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.22\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.24\u00b7 0 \u00b1 07 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0| 0.71\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.88\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.25 \u00b7 0 \u00b1 03\u00a0\u00a0| 0.2\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.23\u00b7 0 \u00b1 05 | 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.9\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 2: Experimental results on the home-credit dataset. We compare synthetic data produced by GEM with \u03f5 \u2208 \u00b6 2 \u21aa 4 \u2666 against the post-processed synthetic data that are generated by applying our post-processing technique with \u03f5$_{post =1 to synthetic data generated from GEM with \u03f5 \u2208 \u00b6 1 \u21aa 3 \u2666. For reference, the downstream model trained and tested on real data has an F1 score of 0 \u00b1 24.\n\n| DP Mechanism\u00a0\u00a0| Utility Improv.\u00a0\u00a0|\u00a0\u00a0F1 score w/o post-proc. | F1 score w/ post-proc.\u00a0\u00a0|\u00a0\u00a0JS distance w/o post-proc. | JS distance w/ post-proc.\u00a0\u00a0|\u00a0\u00a0Inverse KL w/o post-proc. | Inverse KL w/ post-proc.\u00a0\u00a0|\n|----------------|-------------------|---------------------------|--------------------------|------------------------------|-----------------------------|-----------------------------|----------------------------|\n| GEM \u03f5 =2\u00a0\u00a0\u00a0\u00a0| 0.57 \u00b7 0 \u00b1 03\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.19 | 0.19\u00b7 0 \u00b1 02\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.01 | 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.93 | 0.97\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0\u00a0|\n| GEM \u03f5 =4\u00a0\u00a0\u00a0\u00a0| 0.68 \u00b7 0 \u00b1 01\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.21 | 0.21\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.01 | 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.95 | 0.99\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nResult. We present the experimental results on the UCI datasets in Table 1 (and Table 3 in Appendix C with a higher privacy budget). We report utility improvement, F1 score, and statistical measures. As shown, our post-processing technique consistently enhances the utility of synthetic data on selected metrics across all benchmark datasets and all privacy mechanisms. Moreover, the utility improvements are achieved without degrading the performance of downstream models or statistical properties of synthetic data. In other words, our post-processing procedure ensures that the logistic regression classifier trained on synthetic data can achieve comparable or even higher performance on real test data, while simultaneously reducing or maintaining the statistical divergences between synthetic and real data.\n\nWe present the experimental results on the home-credit dataset in Table 2 and illustrate the misalignment of the correlation matrix with and without applying our post-processing procedure in Figure 1. The results demonstrate that our algorithm consistently reduces the overall correlation misalignment. Additionally, our procedure, which includes computing the utility measures from real data, denoising the noisy answers, and computing optimal resampling weights, only takes around 4 mins on 1x NVIDIA GeForce RTX 3090 GPU.\n\nAnswer the following question: What were the DP mechanisms used (in bullets)?"], "reference": ["DP Mechanism:\n- AIM\n- MST\n- DPCTGAN\n- PATECTGAN\n- GEM\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/tables/bullet_points/qna.yaml", "question_id": "112721156388486506495179013623544578065", "category": "taxonomy", "turns": ["Given the context below:\nTable 1: We compare synthetic data generated without and with our post-processing technique, all under the same privacy budget \u03f5 =2. We demonstrate utility improvement (higher is better, positive numbers imply improvement) and F1 score for downstream models trained on synthetic data and tested on real data. For reference, when training the same downstream model using real data, the F1 scores are: (Adult \u21aa 0 \u00b1 61), (Mushroom \u21aa 0 \u00b1 95), (Shopper \u21aa 0 \u00b1 54), and (Bank \u21aa 0 \u00b1 47). Additionally, we measure the average Jensen-Shannon (JS) distance between the marginal distributions of synthetic and real data (0: identical distribution; 1: totally different distributions) and the average inverse KL-divergence (0: totally different distributions; 1: identical distribution). As shown, our technique consistently improves the utility of the synthetic data across all datasets and all DP mechanisms without degrading the performance of downstream models or statistical metrics.\n\n|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| F1 score\u00a0\u00a0\u00a0\u00a0| F1 score\u00a0\u00a0\u00a0\u00a0\u00a0| JS distance (marginal)\u00a0\u00a0| JS distance (marginal)\u00a0\u00a0| Inverse KL (marginal)\u00a0\u00a0| Inverse KL (marginal)\u00a0\u00a0|\n|----------|--------------|-----------------|----------------|-------------------|--------------------------|--------------------------|-------------------------|-------------------------|\n| Dataset\u00a0| DP Mechanism | Utility Improv. | w/o post-proc. | w/ post-proc.\u00a0\u00a0\u00a0| w/o post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| w/ post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| w/o post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0| w/ post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Adult\u00a0\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.13 \u00b7 0 \u00b1 03\u00a0\u00a0| 0.61\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.61\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.22 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.55\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.56\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.81 \u00b7 0 \u00b1 09\u00a0\u00a0| 0.22\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.33\u00b7 0 \u00b1 02 | 0.07\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.03\u00b7 0 \u00b1 02\u00a0\u00a0\u00a0\u00a0| 0.85\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.91\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.6 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.37\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.5\u00b7 0 \u00b1 03\u00a0| 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.72\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.87\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Mushroom | AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.12 \u00b7 0 \u00b1 0\u00a0\u00a0| 0.93\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.93\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.58 \u00b7 0 \u00b1 0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00b7 0 \u00b1 02 | 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.69 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.47\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.68\u00b7 0 \u00b1 01 | 0.05\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.92\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.83 \u00b7 0 \u00b1 05\u00a0\u00a0| 0.6\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.86\u00b7 0 \u00b1 04 | 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.87\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.95\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Shopper\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.1 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.48\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.48\u00b7 0 \u00b1 02 | 0.02\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.84\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.92\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.5 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.42\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.47\u00b7 0 \u00b1 02 | 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.36 \u00b7 0 \u00b1 05\u00a0\u00a0| 0.27\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.3\u00b7 0 \u00b1 02\u00a0| 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.74\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.85\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.11 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.25\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.31\u00b7 0 \u00b1 05 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.89\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Bank\u00a0\u00a0\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.18 \u00b7 0 \u00b1 01\u00a0\u00a0| 0.45\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.46\u00b7 0 \u00b1 01 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.9\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.32 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.43\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.44\u00b7 0 \u00b1 02 | 0.02\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.98\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.2 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.22\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.24\u00b7 0 \u00b1 07 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0| 0.71\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.88\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.25 \u00b7 0 \u00b1 03\u00a0\u00a0| 0.2\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.23\u00b7 0 \u00b1 05 | 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.9\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 2: Experimental results on the home-credit dataset. We compare synthetic data produced by GEM with \u03f5 \u2208 \u00b6 2 \u21aa 4 \u2666 against the post-processed synthetic data that are generated by applying our post-processing technique with \u03f5$_{post =1 to synthetic data generated from GEM with \u03f5 \u2208 \u00b6 1 \u21aa 3 \u2666. For reference, the downstream model trained and tested on real data has an F1 score of 0 \u00b1 24.\n\n| DP Mechanism\u00a0\u00a0| Utility Improv.\u00a0\u00a0|\u00a0\u00a0F1 score w/o post-proc. | F1 score w/ post-proc.\u00a0\u00a0|\u00a0\u00a0JS distance w/o post-proc. | JS distance w/ post-proc.\u00a0\u00a0|\u00a0\u00a0Inverse KL w/o post-proc. | Inverse KL w/ post-proc.\u00a0\u00a0|\n|----------------|-------------------|---------------------------|--------------------------|------------------------------|-----------------------------|-----------------------------|----------------------------|\n| GEM \u03f5 =2\u00a0\u00a0\u00a0\u00a0| 0.57 \u00b7 0 \u00b1 03\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.19 | 0.19\u00b7 0 \u00b1 02\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.01 | 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.93 | 0.97\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0\u00a0|\n| GEM \u03f5 =4\u00a0\u00a0\u00a0\u00a0| 0.68 \u00b7 0 \u00b1 01\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.21 | 0.21\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.01 | 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.95 | 0.99\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nResult. We present the experimental results on the UCI datasets in Table 1 (and Table 3 in Appendix C with a higher privacy budget). We report utility improvement, F1 score, and statistical measures. As shown, our post-processing technique consistently enhances the utility of synthetic data on selected metrics across all benchmark datasets and all privacy mechanisms. Moreover, the utility improvements are achieved without degrading the performance of downstream models or statistical properties of synthetic data. In other words, our post-processing procedure ensures that the logistic regression classifier trained on synthetic data can achieve comparable or even higher performance on real test data, while simultaneously reducing or maintaining the statistical divergences between synthetic and real data.\n\nWe present the experimental results on the home-credit dataset in Table 2 and illustrate the misalignment of the correlation matrix with and without applying our post-processing procedure in Figure 1. The results demonstrate that our algorithm consistently reduces the overall correlation misalignment. Additionally, our procedure, which includes computing the utility measures from real data, denoising the noisy answers, and computing optimal resampling weights, only takes around 4 mins on 1x NVIDIA GeForce RTX 3090 GPU.\n\nAnswer the following question: What types of metrics were calculated in table 1? Give me a list.\n"], "reference": ["Metrics:\n- Utility improvement\n- F1 score (w/o post processing)\n- F1 score (w/ post processing)\n- JS distance (w/o post processing)\n- JS distance (w/ post-processing)\n- Inverse KL (w/o post-processing)\n- Inverse KL (w/ post-processing)\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/tables/plain_text/qna.yaml", "question_id": "23758017158750433548740716559010601950", "category": "taxonomy", "turns": ["Given the context below:\nTable 1: We compare synthetic data generated without and with our post-processing technique, all under the same privacy budget \u03f5 =2. We demonstrate utility improvement (higher is better, positive numbers imply improvement) and F1 score for downstream models trained on synthetic data and tested on real data. For reference, when training the same downstream model using real data, the F1 scores are: (Adult \u21aa 0 \u00b1 61), (Mushroom \u21aa 0 \u00b1 95), (Shopper \u21aa 0 \u00b1 54), and (Bank \u21aa 0 \u00b1 47). Additionally, we measure the average Jensen-Shannon (JS) distance between the marginal distributions of synthetic and real data (0: identical distribution; 1: totally different distributions) and the average inverse KL-divergence (0: totally different distributions; 1: identical distribution). As shown, our technique consistently improves the utility of the synthetic data across all datasets and all DP mechanisms without degrading the performance of downstream models or statistical metrics.\n\n|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| F1 score\u00a0\u00a0\u00a0\u00a0| F1 score\u00a0\u00a0\u00a0\u00a0\u00a0| JS distance (marginal)\u00a0\u00a0| JS distance (marginal)\u00a0\u00a0| Inverse KL (marginal)\u00a0\u00a0| Inverse KL (marginal)\u00a0\u00a0|\n|----------|--------------|-----------------|----------------|-------------------|--------------------------|--------------------------|-------------------------|-------------------------|\n| Dataset\u00a0| DP Mechanism | Utility Improv. | w/o post-proc. | w/ post-proc.\u00a0\u00a0\u00a0| w/o post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| w/ post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| w/o post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0| w/ post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Adult\u00a0\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.13 \u00b7 0 \u00b1 03\u00a0\u00a0| 0.61\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.61\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.22 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.55\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.56\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.81 \u00b7 0 \u00b1 09\u00a0\u00a0| 0.22\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.33\u00b7 0 \u00b1 02 | 0.07\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.03\u00b7 0 \u00b1 02\u00a0\u00a0\u00a0\u00a0| 0.85\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.91\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.6 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.37\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.5\u00b7 0 \u00b1 03\u00a0| 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.72\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.87\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Mushroom | AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.12 \u00b7 0 \u00b1 0\u00a0\u00a0| 0.93\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.93\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.58 \u00b7 0 \u00b1 0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00b7 0 \u00b1 02 | 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.69 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.47\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.68\u00b7 0 \u00b1 01 | 0.05\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.92\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.83 \u00b7 0 \u00b1 05\u00a0\u00a0| 0.6\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.86\u00b7 0 \u00b1 04 | 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.87\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.95\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Shopper\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.1 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.48\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.48\u00b7 0 \u00b1 02 | 0.02\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.84\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.92\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.5 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.42\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.47\u00b7 0 \u00b1 02 | 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.36 \u00b7 0 \u00b1 05\u00a0\u00a0| 0.27\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.3\u00b7 0 \u00b1 02\u00a0| 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.74\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.85\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.11 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.25\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.31\u00b7 0 \u00b1 05 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.89\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Bank\u00a0\u00a0\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.18 \u00b7 0 \u00b1 01\u00a0\u00a0| 0.45\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.46\u00b7 0 \u00b1 01 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.9\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.32 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.43\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.44\u00b7 0 \u00b1 02 | 0.02\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.98\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.2 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.22\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.24\u00b7 0 \u00b1 07 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0| 0.71\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.88\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.25 \u00b7 0 \u00b1 03\u00a0\u00a0| 0.2\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.23\u00b7 0 \u00b1 05 | 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.9\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 2: Experimental results on the home-credit dataset. We compare synthetic data produced by GEM with \u03f5 \u2208 \u00b6 2 \u21aa 4 \u2666 against the post-processed synthetic data that are generated by applying our post-processing technique with \u03f5$_{post =1 to synthetic data generated from GEM with \u03f5 \u2208 \u00b6 1 \u21aa 3 \u2666. For reference, the downstream model trained and tested on real data has an F1 score of 0 \u00b1 24.\n\n| DP Mechanism\u00a0\u00a0| Utility Improv.\u00a0\u00a0|\u00a0\u00a0F1 score w/o post-proc. | F1 score w/ post-proc.\u00a0\u00a0|\u00a0\u00a0JS distance w/o post-proc. | JS distance w/ post-proc.\u00a0\u00a0|\u00a0\u00a0Inverse KL w/o post-proc. | Inverse KL w/ post-proc.\u00a0\u00a0|\n|----------------|-------------------|---------------------------|--------------------------|------------------------------|-----------------------------|-----------------------------|----------------------------|\n| GEM \u03f5 =2\u00a0\u00a0\u00a0\u00a0| 0.57 \u00b7 0 \u00b1 03\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.19 | 0.19\u00b7 0 \u00b1 02\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.01 | 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.93 | 0.97\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0\u00a0|\n| GEM \u03f5 =4\u00a0\u00a0\u00a0\u00a0| 0.68 \u00b7 0 \u00b1 01\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.21 | 0.21\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.01 | 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.95 | 0.99\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nResult. We present the experimental results on the UCI datasets in Table 1 (and Table 3 in Appendix C with a higher privacy budget). We report utility improvement, F1 score, and statistical measures. As shown, our post-processing technique consistently enhances the utility of synthetic data on selected metrics across all benchmark datasets and all privacy mechanisms. Moreover, the utility improvements are achieved without degrading the performance of downstream models or statistical properties of synthetic data. In other words, our post-processing procedure ensures that the logistic regression classifier trained on synthetic data can achieve comparable or even higher performance on real test data, while simultaneously reducing or maintaining the statistical divergences between synthetic and real data.\n\nWe present the experimental results on the home-credit dataset in Table 2 and illustrate the misalignment of the correlation matrix with and without applying our post-processing procedure in Figure 1. The results demonstrate that our algorithm consistently reduces the overall correlation misalignment. Additionally, our procedure, which includes computing the utility measures from real data, denoising the noisy answers, and computing optimal resampling weights, only takes around 4 mins on 1x NVIDIA GeForce RTX 3090 GPU.\n\nAnswer the following question: Did the post-processing technique improve performance based on the f1 score?\n"], "reference": ["Yes, performance was the same or improved across all datasets and DP mechanisms. \n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/tables/plain_text/qna.yaml", "question_id": "8282478500022356825634852654128220007", "category": "taxonomy", "turns": ["Given the context below:\nTable 1: We compare synthetic data generated without and with our post-processing technique, all under the same privacy budget \u03f5 =2. We demonstrate utility improvement (higher is better, positive numbers imply improvement) and F1 score for downstream models trained on synthetic data and tested on real data. For reference, when training the same downstream model using real data, the F1 scores are: (Adult \u21aa 0 \u00b1 61), (Mushroom \u21aa 0 \u00b1 95), (Shopper \u21aa 0 \u00b1 54), and (Bank \u21aa 0 \u00b1 47). Additionally, we measure the average Jensen-Shannon (JS) distance between the marginal distributions of synthetic and real data (0: identical distribution; 1: totally different distributions) and the average inverse KL-divergence (0: totally different distributions; 1: identical distribution). As shown, our technique consistently improves the utility of the synthetic data across all datasets and all DP mechanisms without degrading the performance of downstream models or statistical metrics.\n\n|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| F1 score\u00a0\u00a0\u00a0\u00a0| F1 score\u00a0\u00a0\u00a0\u00a0\u00a0| JS distance (marginal)\u00a0\u00a0| JS distance (marginal)\u00a0\u00a0| Inverse KL (marginal)\u00a0\u00a0| Inverse KL (marginal)\u00a0\u00a0|\n|----------|--------------|-----------------|----------------|-------------------|--------------------------|--------------------------|-------------------------|-------------------------|\n| Dataset\u00a0| DP Mechanism | Utility Improv. | w/o post-proc. | w/ post-proc.\u00a0\u00a0\u00a0| w/o post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| w/ post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| w/o post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0| w/ post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Adult\u00a0\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.13 \u00b7 0 \u00b1 03\u00a0\u00a0| 0.61\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.61\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.22 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.55\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.56\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.81 \u00b7 0 \u00b1 09\u00a0\u00a0| 0.22\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.33\u00b7 0 \u00b1 02 | 0.07\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.03\u00b7 0 \u00b1 02\u00a0\u00a0\u00a0\u00a0| 0.85\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.91\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.6 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.37\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.5\u00b7 0 \u00b1 03\u00a0| 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.72\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.87\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Mushroom | AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.12 \u00b7 0 \u00b1 0\u00a0\u00a0| 0.93\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.93\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.58 \u00b7 0 \u00b1 0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00b7 0 \u00b1 02 | 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.69 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.47\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.68\u00b7 0 \u00b1 01 | 0.05\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.92\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.83 \u00b7 0 \u00b1 05\u00a0\u00a0| 0.6\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.86\u00b7 0 \u00b1 04 | 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.87\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.95\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Shopper\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.1 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.48\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.48\u00b7 0 \u00b1 02 | 0.02\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.84\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.92\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.5 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.42\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.47\u00b7 0 \u00b1 02 | 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.36 \u00b7 0 \u00b1 05\u00a0\u00a0| 0.27\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.3\u00b7 0 \u00b1 02\u00a0| 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.74\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.85\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.11 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.25\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.31\u00b7 0 \u00b1 05 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.89\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Bank\u00a0\u00a0\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.18 \u00b7 0 \u00b1 01\u00a0\u00a0| 0.45\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.46\u00b7 0 \u00b1 01 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.9\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.32 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.43\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.44\u00b7 0 \u00b1 02 | 0.02\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.98\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.2 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.22\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.24\u00b7 0 \u00b1 07 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0| 0.71\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.88\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.25 \u00b7 0 \u00b1 03\u00a0\u00a0| 0.2\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.23\u00b7 0 \u00b1 05 | 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.9\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 2: Experimental results on the home-credit dataset. We compare synthetic data produced by GEM with \u03f5 \u2208 \u00b6 2 \u21aa 4 \u2666 against the post-processed synthetic data that are generated by applying our post-processing technique with \u03f5$_{post =1 to synthetic data generated from GEM with \u03f5 \u2208 \u00b6 1 \u21aa 3 \u2666. For reference, the downstream model trained and tested on real data has an F1 score of 0 \u00b1 24.\n\n| DP Mechanism\u00a0\u00a0| Utility Improv.\u00a0\u00a0|\u00a0\u00a0F1 score w/o post-proc. | F1 score w/ post-proc.\u00a0\u00a0|\u00a0\u00a0JS distance w/o post-proc. | JS distance w/ post-proc.\u00a0\u00a0|\u00a0\u00a0Inverse KL w/o post-proc. | Inverse KL w/ post-proc.\u00a0\u00a0|\n|----------------|-------------------|---------------------------|--------------------------|------------------------------|-----------------------------|-----------------------------|----------------------------|\n| GEM \u03f5 =2\u00a0\u00a0\u00a0\u00a0| 0.57 \u00b7 0 \u00b1 03\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.19 | 0.19\u00b7 0 \u00b1 02\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.01 | 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.93 | 0.97\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0\u00a0|\n| GEM \u03f5 =4\u00a0\u00a0\u00a0\u00a0| 0.68 \u00b7 0 \u00b1 01\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.21 | 0.21\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.01 | 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.95 | 0.99\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nResult. We present the experimental results on the UCI datasets in Table 1 (and Table 3 in Appendix C with a higher privacy budget). We report utility improvement, F1 score, and statistical measures. As shown, our post-processing technique consistently enhances the utility of synthetic data on selected metrics across all benchmark datasets and all privacy mechanisms. Moreover, the utility improvements are achieved without degrading the performance of downstream models or statistical properties of synthetic data. In other words, our post-processing procedure ensures that the logistic regression classifier trained on synthetic data can achieve comparable or even higher performance on real test data, while simultaneously reducing or maintaining the statistical divergences between synthetic and real data.\n\nWe present the experimental results on the home-credit dataset in Table 2 and illustrate the misalignment of the correlation matrix with and without applying our post-processing procedure in Figure 1. The results demonstrate that our algorithm consistently reduces the overall correlation misalignment. Additionally, our procedure, which includes computing the utility measures from real data, denoising the noisy answers, and computing optimal resampling weights, only takes around 4 mins on 1x NVIDIA GeForce RTX 3090 GPU.\n\nAnswer the following question: How did performance compare with real data vs w post processing as measured by the F1 score?"], "reference": ["Performance was approximately the same for the synthetic data vs real data based on the F1 scores.\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/tables/plain_text/qna.yaml", "question_id": "26239601712644162644444465469609811689", "category": "taxonomy", "turns": ["Given the context below:\nTable 1: We compare synthetic data generated without and with our post-processing technique, all under the same privacy budget \u03f5 =2. We demonstrate utility improvement (higher is better, positive numbers imply improvement) and F1 score for downstream models trained on synthetic data and tested on real data. For reference, when training the same downstream model using real data, the F1 scores are: (Adult \u21aa 0 \u00b1 61), (Mushroom \u21aa 0 \u00b1 95), (Shopper \u21aa 0 \u00b1 54), and (Bank \u21aa 0 \u00b1 47). Additionally, we measure the average Jensen-Shannon (JS) distance between the marginal distributions of synthetic and real data (0: identical distribution; 1: totally different distributions) and the average inverse KL-divergence (0: totally different distributions; 1: identical distribution). As shown, our technique consistently improves the utility of the synthetic data across all datasets and all DP mechanisms without degrading the performance of downstream models or statistical metrics.\n\n|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| F1 score\u00a0\u00a0\u00a0\u00a0| F1 score\u00a0\u00a0\u00a0\u00a0\u00a0| JS distance (marginal)\u00a0\u00a0| JS distance (marginal)\u00a0\u00a0| Inverse KL (marginal)\u00a0\u00a0| Inverse KL (marginal)\u00a0\u00a0|\n|----------|--------------|-----------------|----------------|-------------------|--------------------------|--------------------------|-------------------------|-------------------------|\n| Dataset\u00a0| DP Mechanism | Utility Improv. | w/o post-proc. | w/ post-proc.\u00a0\u00a0\u00a0| w/o post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| w/ post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| w/o post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0| w/ post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Adult\u00a0\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.13 \u00b7 0 \u00b1 03\u00a0\u00a0| 0.61\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.61\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.22 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.55\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.56\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.81 \u00b7 0 \u00b1 09\u00a0\u00a0| 0.22\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.33\u00b7 0 \u00b1 02 | 0.07\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.03\u00b7 0 \u00b1 02\u00a0\u00a0\u00a0\u00a0| 0.85\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.91\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.6 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.37\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.5\u00b7 0 \u00b1 03\u00a0| 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.72\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.87\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Mushroom | AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.12 \u00b7 0 \u00b1 0\u00a0\u00a0| 0.93\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.93\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.58 \u00b7 0 \u00b1 0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00b7 0 \u00b1 02 | 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.69 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.47\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.68\u00b7 0 \u00b1 01 | 0.05\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.92\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.83 \u00b7 0 \u00b1 05\u00a0\u00a0| 0.6\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.86\u00b7 0 \u00b1 04 | 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.87\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.95\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Shopper\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.1 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.48\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.48\u00b7 0 \u00b1 02 | 0.02\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.84\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.92\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.5 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.42\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.47\u00b7 0 \u00b1 02 | 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.36 \u00b7 0 \u00b1 05\u00a0\u00a0| 0.27\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.3\u00b7 0 \u00b1 02\u00a0| 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.74\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.85\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.11 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.25\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.31\u00b7 0 \u00b1 05 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.89\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Bank\u00a0\u00a0\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.18 \u00b7 0 \u00b1 01\u00a0\u00a0| 0.45\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.46\u00b7 0 \u00b1 01 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.9\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.32 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.43\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.44\u00b7 0 \u00b1 02 | 0.02\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.98\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.2 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.22\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.24\u00b7 0 \u00b1 07 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0| 0.71\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.88\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.25 \u00b7 0 \u00b1 03\u00a0\u00a0| 0.2\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.23\u00b7 0 \u00b1 05 | 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.9\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 2: Experimental results on the home-credit dataset. We compare synthetic data produced by GEM with \u03f5 \u2208 \u00b6 2 \u21aa 4 \u2666 against the post-processed synthetic data that are generated by applying our post-processing technique with \u03f5$_{post =1 to synthetic data generated from GEM with \u03f5 \u2208 \u00b6 1 \u21aa 3 \u2666. For reference, the downstream model trained and tested on real data has an F1 score of 0 \u00b1 24.\n\n| DP Mechanism\u00a0\u00a0| Utility Improv.\u00a0\u00a0|\u00a0\u00a0F1 score w/o post-proc. | F1 score w/ post-proc.\u00a0\u00a0|\u00a0\u00a0JS distance w/o post-proc. | JS distance w/ post-proc.\u00a0\u00a0|\u00a0\u00a0Inverse KL w/o post-proc. | Inverse KL w/ post-proc.\u00a0\u00a0|\n|----------------|-------------------|---------------------------|--------------------------|------------------------------|-----------------------------|-----------------------------|----------------------------|\n| GEM \u03f5 =2\u00a0\u00a0\u00a0\u00a0| 0.57 \u00b7 0 \u00b1 03\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.19 | 0.19\u00b7 0 \u00b1 02\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.01 | 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.93 | 0.97\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0\u00a0|\n| GEM \u03f5 =4\u00a0\u00a0\u00a0\u00a0| 0.68 \u00b7 0 \u00b1 01\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.21 | 0.21\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.01 | 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.95 | 0.99\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nResult. We present the experimental results on the UCI datasets in Table 1 (and Table 3 in Appendix C with a higher privacy budget). We report utility improvement, F1 score, and statistical measures. As shown, our post-processing technique consistently enhances the utility of synthetic data on selected metrics across all benchmark datasets and all privacy mechanisms. Moreover, the utility improvements are achieved without degrading the performance of downstream models or statistical properties of synthetic data. In other words, our post-processing procedure ensures that the logistic regression classifier trained on synthetic data can achieve comparable or even higher performance on real test data, while simultaneously reducing or maintaining the statistical divergences between synthetic and real data.\n\nWe present the experimental results on the home-credit dataset in Table 2 and illustrate the misalignment of the correlation matrix with and without applying our post-processing procedure in Figure 1. The results demonstrate that our algorithm consistently reduces the overall correlation misalignment. Additionally, our procedure, which includes computing the utility measures from real data, denoising the noisy answers, and computing optimal resampling weights, only takes around 4 mins on 1x NVIDIA GeForce RTX 3090 GPU.\n\nAnswer the following question: For the bank data set, which DP mechanism performed the best according to the utility improvement?\n"], "reference": ["MST was the highest utility score with 0.32.\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/tables/reasoning/qna.yaml", "question_id": "45917352980180410017922418202197457227", "category": "taxonomy", "turns": ["Given the context below:\nTable 1: We compare synthetic data generated without and with our post-processing technique, all under the same privacy budget \u03f5 =2. We demonstrate utility improvement (higher is better, positive numbers imply improvement) and F1 score for downstream models trained on synthetic data and tested on real data. For reference, when training the same downstream model using real data, the F1 scores are: (Adult \u21aa 0 \u00b1 61), (Mushroom \u21aa 0 \u00b1 95), (Shopper \u21aa 0 \u00b1 54), and (Bank \u21aa 0 \u00b1 47). Additionally, we measure the average Jensen-Shannon (JS) distance between the marginal distributions of synthetic and real data (0: identical distribution; 1: totally different distributions) and the average inverse KL-divergence (0: totally different distributions; 1: identical distribution). As shown, our technique consistently improves the utility of the synthetic data across all datasets and all DP mechanisms without degrading the performance of downstream models or statistical metrics.\n\n|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| F1 score\u00a0\u00a0\u00a0\u00a0| F1 score\u00a0\u00a0\u00a0\u00a0\u00a0| JS distance (marginal)\u00a0\u00a0| JS distance (marginal)\u00a0\u00a0| Inverse KL (marginal)\u00a0\u00a0| Inverse KL (marginal)\u00a0\u00a0|\n|----------|--------------|-----------------|----------------|-------------------|--------------------------|--------------------------|-------------------------|-------------------------|\n| Dataset\u00a0| DP Mechanism | Utility Improv. | w/o post-proc. | w/ post-proc.\u00a0\u00a0\u00a0| w/o post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| w/ post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| w/o post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0| w/ post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Adult\u00a0\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.13 \u00b7 0 \u00b1 03\u00a0\u00a0| 0.61\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.61\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.22 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.55\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.56\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.81 \u00b7 0 \u00b1 09\u00a0\u00a0| 0.22\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.33\u00b7 0 \u00b1 02 | 0.07\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.03\u00b7 0 \u00b1 02\u00a0\u00a0\u00a0\u00a0| 0.85\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.91\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.6 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.37\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.5\u00b7 0 \u00b1 03\u00a0| 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.72\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.87\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Mushroom | AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.12 \u00b7 0 \u00b1 0\u00a0\u00a0| 0.93\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.93\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.58 \u00b7 0 \u00b1 0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00b7 0 \u00b1 02 | 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.69 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.47\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.68\u00b7 0 \u00b1 01 | 0.05\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.92\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.83 \u00b7 0 \u00b1 05\u00a0\u00a0| 0.6\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.86\u00b7 0 \u00b1 04 | 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.87\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.95\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Shopper\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.1 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.48\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.48\u00b7 0 \u00b1 02 | 0.02\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.84\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.92\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.5 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.42\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.47\u00b7 0 \u00b1 02 | 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.36 \u00b7 0 \u00b1 05\u00a0\u00a0| 0.27\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.3\u00b7 0 \u00b1 02\u00a0| 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.74\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.85\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.11 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.25\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.31\u00b7 0 \u00b1 05 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.89\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Bank\u00a0\u00a0\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.18 \u00b7 0 \u00b1 01\u00a0\u00a0| 0.45\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.46\u00b7 0 \u00b1 01 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.9\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.32 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.43\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.44\u00b7 0 \u00b1 02 | 0.02\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.98\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.2 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.22\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.24\u00b7 0 \u00b1 07 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0| 0.71\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.88\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.25 \u00b7 0 \u00b1 03\u00a0\u00a0| 0.2\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.23\u00b7 0 \u00b1 05 | 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.9\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 2: Experimental results on the home-credit dataset. We compare synthetic data produced by GEM with \u03f5 \u2208 \u00b6 2 \u21aa 4 \u2666 against the post-processed synthetic data that are generated by applying our post-processing technique with \u03f5$_{post =1 to synthetic data generated from GEM with \u03f5 \u2208 \u00b6 1 \u21aa 3 \u2666. For reference, the downstream model trained and tested on real data has an F1 score of 0 \u00b1 24.\n\n| DP Mechanism\u00a0\u00a0| Utility Improv.\u00a0\u00a0|\u00a0\u00a0F1 score w/o post-proc. | F1 score w/ post-proc.\u00a0\u00a0|\u00a0\u00a0JS distance w/o post-proc. | JS distance w/ post-proc.\u00a0\u00a0|\u00a0\u00a0Inverse KL w/o post-proc. | Inverse KL w/ post-proc.\u00a0\u00a0|\n|----------------|-------------------|---------------------------|--------------------------|------------------------------|-----------------------------|-----------------------------|----------------------------|\n| GEM \u03f5 =2\u00a0\u00a0\u00a0\u00a0| 0.57 \u00b7 0 \u00b1 03\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.19 | 0.19\u00b7 0 \u00b1 02\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.01 | 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.93 | 0.97\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0\u00a0|\n| GEM \u03f5 =4\u00a0\u00a0\u00a0\u00a0| 0.68 \u00b7 0 \u00b1 01\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.21 | 0.21\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.01 | 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.95 | 0.99\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nResult. We present the experimental results on the UCI datasets in Table 1 (and Table 3 in Appendix C with a higher privacy budget). We report utility improvement, F1 score, and statistical measures. As shown, our post-processing technique consistently enhances the utility of synthetic data on selected metrics across all benchmark datasets and all privacy mechanisms. Moreover, the utility improvements are achieved without degrading the performance of downstream models or statistical properties of synthetic data. In other words, our post-processing procedure ensures that the logistic regression classifier trained on synthetic data can achieve comparable or even higher performance on real test data, while simultaneously reducing or maintaining the statistical divergences between synthetic and real data.\n\nWe present the experimental results on the home-credit dataset in Table 2 and illustrate the misalignment of the correlation matrix with and without applying our post-processing procedure in Figure 1. The results demonstrate that our algorithm consistently reduces the overall correlation misalignment. Additionally, our procedure, which includes computing the utility measures from real data, denoising the noisy answers, and computing optimal resampling weights, only takes around 4 mins on 1x NVIDIA GeForce RTX 3090 GPU.\n\nAnswer the following question: Which dataset resulted in the highest performance according to the F1 score (w/post processing)?\n"], "reference": ["The Mushroom dataset resulted in the highest F1 score (w/ post processing). The range of F1 scores for the Mushroom dataset ranged from 0.68 to 0.93. The Adult dataset ranged from 0.33 to 0.61. The shopper dataset ranged from 0.3 to 0.48 and the Bank dataset ranged from 0.23 to 0.46. Additionally, the F1 scores for the real data were the highest for the Mushroom dataset. \n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/tables/reasoning/qna.yaml", "question_id": "168920137566501678050484792868029527576", "category": "taxonomy", "turns": ["Given the context below:\nTable 1: We compare synthetic data generated without and with our post-processing technique, all under the same privacy budget \u03f5 =2. We demonstrate utility improvement (higher is better, positive numbers imply improvement) and F1 score for downstream models trained on synthetic data and tested on real data. For reference, when training the same downstream model using real data, the F1 scores are: (Adult \u21aa 0 \u00b1 61), (Mushroom \u21aa 0 \u00b1 95), (Shopper \u21aa 0 \u00b1 54), and (Bank \u21aa 0 \u00b1 47). Additionally, we measure the average Jensen-Shannon (JS) distance between the marginal distributions of synthetic and real data (0: identical distribution; 1: totally different distributions) and the average inverse KL-divergence (0: totally different distributions; 1: identical distribution). As shown, our technique consistently improves the utility of the synthetic data across all datasets and all DP mechanisms without degrading the performance of downstream models or statistical metrics.\n\n|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| F1 score\u00a0\u00a0\u00a0\u00a0| F1 score\u00a0\u00a0\u00a0\u00a0\u00a0| JS distance (marginal)\u00a0\u00a0| JS distance (marginal)\u00a0\u00a0| Inverse KL (marginal)\u00a0\u00a0| Inverse KL (marginal)\u00a0\u00a0|\n|----------|--------------|-----------------|----------------|-------------------|--------------------------|--------------------------|-------------------------|-------------------------|\n| Dataset\u00a0| DP Mechanism | Utility Improv. | w/o post-proc. | w/ post-proc.\u00a0\u00a0\u00a0| w/o post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| w/ post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| w/o post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0| w/ post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Adult\u00a0\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.13 \u00b7 0 \u00b1 03\u00a0\u00a0| 0.61\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.61\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.22 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.55\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.56\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.81 \u00b7 0 \u00b1 09\u00a0\u00a0| 0.22\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.33\u00b7 0 \u00b1 02 | 0.07\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.03\u00b7 0 \u00b1 02\u00a0\u00a0\u00a0\u00a0| 0.85\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.91\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.6 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.37\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.5\u00b7 0 \u00b1 03\u00a0| 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.72\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.87\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Mushroom | AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.12 \u00b7 0 \u00b1 0\u00a0\u00a0| 0.93\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.93\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.58 \u00b7 0 \u00b1 0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00b7 0 \u00b1 02 | 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.69 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.47\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.68\u00b7 0 \u00b1 01 | 0.05\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.92\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.83 \u00b7 0 \u00b1 05\u00a0\u00a0| 0.6\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.86\u00b7 0 \u00b1 04 | 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.87\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.95\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Shopper\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.1 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.48\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.48\u00b7 0 \u00b1 02 | 0.02\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.84\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.92\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.5 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.42\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.47\u00b7 0 \u00b1 02 | 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.36 \u00b7 0 \u00b1 05\u00a0\u00a0| 0.27\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.3\u00b7 0 \u00b1 02\u00a0| 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.74\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.85\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.11 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.25\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.31\u00b7 0 \u00b1 05 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.89\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Bank\u00a0\u00a0\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.18 \u00b7 0 \u00b1 01\u00a0\u00a0| 0.45\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.46\u00b7 0 \u00b1 01 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.9\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.32 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.43\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.44\u00b7 0 \u00b1 02 | 0.02\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.98\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.2 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.22\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.24\u00b7 0 \u00b1 07 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0| 0.71\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.88\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.25 \u00b7 0 \u00b1 03\u00a0\u00a0| 0.2\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.23\u00b7 0 \u00b1 05 | 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.9\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 2: Experimental results on the home-credit dataset. We compare synthetic data produced by GEM with \u03f5 \u2208 \u00b6 2 \u21aa 4 \u2666 against the post-processed synthetic data that are generated by applying our post-processing technique with \u03f5$_{post =1 to synthetic data generated from GEM with \u03f5 \u2208 \u00b6 1 \u21aa 3 \u2666. For reference, the downstream model trained and tested on real data has an F1 score of 0 \u00b1 24.\n\n| DP Mechanism\u00a0\u00a0| Utility Improv.\u00a0\u00a0|\u00a0\u00a0F1 score w/o post-proc. | F1 score w/ post-proc.\u00a0\u00a0|\u00a0\u00a0JS distance w/o post-proc. | JS distance w/ post-proc.\u00a0\u00a0|\u00a0\u00a0Inverse KL w/o post-proc. | Inverse KL w/ post-proc.\u00a0\u00a0|\n|----------------|-------------------|---------------------------|--------------------------|------------------------------|-----------------------------|-----------------------------|----------------------------|\n| GEM \u03f5 =2\u00a0\u00a0\u00a0\u00a0| 0.57 \u00b7 0 \u00b1 03\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.19 | 0.19\u00b7 0 \u00b1 02\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.01 | 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.93 | 0.97\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0\u00a0|\n| GEM \u03f5 =4\u00a0\u00a0\u00a0\u00a0| 0.68 \u00b7 0 \u00b1 01\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.21 | 0.21\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.01 | 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.95 | 0.99\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nResult. We present the experimental results on the UCI datasets in Table 1 (and Table 3 in Appendix C with a higher privacy budget). We report utility improvement, F1 score, and statistical measures. As shown, our post-processing technique consistently enhances the utility of synthetic data on selected metrics across all benchmark datasets and all privacy mechanisms. Moreover, the utility improvements are achieved without degrading the performance of downstream models or statistical properties of synthetic data. In other words, our post-processing procedure ensures that the logistic regression classifier trained on synthetic data can achieve comparable or even higher performance on real test data, while simultaneously reducing or maintaining the statistical divergences between synthetic and real data.\n\nWe present the experimental results on the home-credit dataset in Table 2 and illustrate the misalignment of the correlation matrix with and without applying our post-processing procedure in Figure 1. The results demonstrate that our algorithm consistently reduces the overall correlation misalignment. Additionally, our procedure, which includes computing the utility measures from real data, denoising the noisy answers, and computing optimal resampling weights, only takes around 4 mins on 1x NVIDIA GeForce RTX 3090 GPU.\n\nAnswer the following question: Which DP Mechanism resulted in the highest average utility improvement across all datasets."], "reference": ["The highest average utility was for the DPCTGAN approach with 0.515 utility improvement. The average utility improvement was 0.1325 for the AIM approach. The average improvement for MST was 0.405. The average for DPCTGAN was 0.515.The PATECTGAN mechanism was 0.4475.\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/tables/reasoning/qna.yaml", "question_id": "269100815912960317053446066237713565838", "category": "taxonomy", "turns": ["Given the context below:\nTable 1: We compare synthetic data generated without and with our post-processing technique, all under the same privacy budget \u03f5 =2. We demonstrate utility improvement (higher is better, positive numbers imply improvement) and F1 score for downstream models trained on synthetic data and tested on real data. For reference, when training the same downstream model using real data, the F1 scores are: (Adult \u21aa 0 \u00b1 61), (Mushroom \u21aa 0 \u00b1 95), (Shopper \u21aa 0 \u00b1 54), and (Bank \u21aa 0 \u00b1 47). Additionally, we measure the average Jensen-Shannon (JS) distance between the marginal distributions of synthetic and real data (0: identical distribution; 1: totally different distributions) and the average inverse KL-divergence (0: totally different distributions; 1: identical distribution). As shown, our technique consistently improves the utility of the synthetic data across all datasets and all DP mechanisms without degrading the performance of downstream models or statistical metrics.\n\n|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| F1 score\u00a0\u00a0\u00a0\u00a0| F1 score\u00a0\u00a0\u00a0\u00a0\u00a0| JS distance (marginal)\u00a0\u00a0| JS distance (marginal)\u00a0\u00a0| Inverse KL (marginal)\u00a0\u00a0| Inverse KL (marginal)\u00a0\u00a0|\n|----------|--------------|-----------------|----------------|-------------------|--------------------------|--------------------------|-------------------------|-------------------------|\n| Dataset\u00a0| DP Mechanism | Utility Improv. | w/o post-proc. | w/ post-proc.\u00a0\u00a0\u00a0| w/o post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| w/ post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| w/o post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0| w/ post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Adult\u00a0\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.13 \u00b7 0 \u00b1 03\u00a0\u00a0| 0.61\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.61\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.22 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.55\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.56\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.81 \u00b7 0 \u00b1 09\u00a0\u00a0| 0.22\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.33\u00b7 0 \u00b1 02 | 0.07\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.03\u00b7 0 \u00b1 02\u00a0\u00a0\u00a0\u00a0| 0.85\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.91\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.6 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.37\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.5\u00b7 0 \u00b1 03\u00a0| 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.72\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.87\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Mushroom | AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.12 \u00b7 0 \u00b1 0\u00a0\u00a0| 0.93\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.93\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.58 \u00b7 0 \u00b1 0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00b7 0 \u00b1 02 | 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.69 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.47\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.68\u00b7 0 \u00b1 01 | 0.05\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.92\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.83 \u00b7 0 \u00b1 05\u00a0\u00a0| 0.6\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.86\u00b7 0 \u00b1 04 | 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.87\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.95\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Shopper\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.1 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.48\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.48\u00b7 0 \u00b1 02 | 0.02\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.84\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.92\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.5 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.42\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.47\u00b7 0 \u00b1 02 | 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.36 \u00b7 0 \u00b1 05\u00a0\u00a0| 0.27\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.3\u00b7 0 \u00b1 02\u00a0| 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.74\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.85\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.11 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.25\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.31\u00b7 0 \u00b1 05 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.89\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Bank\u00a0\u00a0\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.18 \u00b7 0 \u00b1 01\u00a0\u00a0| 0.45\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.46\u00b7 0 \u00b1 01 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.9\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.32 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.43\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.44\u00b7 0 \u00b1 02 | 0.02\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.98\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.2 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.22\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.24\u00b7 0 \u00b1 07 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0| 0.71\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.88\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.25 \u00b7 0 \u00b1 03\u00a0\u00a0| 0.2\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.23\u00b7 0 \u00b1 05 | 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.9\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 2: Experimental results on the home-credit dataset. We compare synthetic data produced by GEM with \u03f5 \u2208 \u00b6 2 \u21aa 4 \u2666 against the post-processed synthetic data that are generated by applying our post-processing technique with \u03f5$_{post =1 to synthetic data generated from GEM with \u03f5 \u2208 \u00b6 1 \u21aa 3 \u2666. For reference, the downstream model trained and tested on real data has an F1 score of 0 \u00b1 24.\n\n| DP Mechanism\u00a0\u00a0| Utility Improv.\u00a0\u00a0|\u00a0\u00a0F1 score w/o post-proc. | F1 score w/ post-proc.\u00a0\u00a0|\u00a0\u00a0JS distance w/o post-proc. | JS distance w/ post-proc.\u00a0\u00a0|\u00a0\u00a0Inverse KL w/o post-proc. | Inverse KL w/ post-proc.\u00a0\u00a0|\n|----------------|-------------------|---------------------------|--------------------------|------------------------------|-----------------------------|-----------------------------|----------------------------|\n| GEM \u03f5 =2\u00a0\u00a0\u00a0\u00a0| 0.57 \u00b7 0 \u00b1 03\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.19 | 0.19\u00b7 0 \u00b1 02\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.01 | 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.93 | 0.97\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0\u00a0|\n| GEM \u03f5 =4\u00a0\u00a0\u00a0\u00a0| 0.68 \u00b7 0 \u00b1 01\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.21 | 0.21\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.01 | 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.95 | 0.99\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nResult. We present the experimental results on the UCI datasets in Table 1 (and Table 3 in Appendix C with a higher privacy budget). We report utility improvement, F1 score, and statistical measures. As shown, our post-processing technique consistently enhances the utility of synthetic data on selected metrics across all benchmark datasets and all privacy mechanisms. Moreover, the utility improvements are achieved without degrading the performance of downstream models or statistical properties of synthetic data. In other words, our post-processing procedure ensures that the logistic regression classifier trained on synthetic data can achieve comparable or even higher performance on real test data, while simultaneously reducing or maintaining the statistical divergences between synthetic and real data.\n\nWe present the experimental results on the home-credit dataset in Table 2 and illustrate the misalignment of the correlation matrix with and without applying our post-processing procedure in Figure 1. The results demonstrate that our algorithm consistently reduces the overall correlation misalignment. Additionally, our procedure, which includes computing the utility measures from real data, denoising the noisy answers, and computing optimal resampling weights, only takes around 4 mins on 1x NVIDIA GeForce RTX 3090 GPU.\n\nAnswer the following question: Is the utility improvement or the difference between no and post-processing for the f1 score higher?\n"], "reference": ["The Utility improvement is higher. We first took the difference between F1 scores subtracting the F1 for w/post processing minus F1 score with post processing. This difference ranged from 0 to 0.26 with an average difference of 0.058125. The difference in utility improvement ragned from 0.1 to 0.81 with an average of 0.375.\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/tables/csv/qna.yaml", "question_id": "206469191930851162031349026753499177208", "category": "taxonomy", "turns": ["Given the context below:\nTable 1: We compare synthetic data generated without and with our post-processing technique, all under the same privacy budget \u03f5 =2. We demonstrate utility improvement (higher is better, positive numbers imply improvement) and F1 score for downstream models trained on synthetic data and tested on real data. For reference, when training the same downstream model using real data, the F1 scores are: (Adult \u21aa 0 \u00b1 61), (Mushroom \u21aa 0 \u00b1 95), (Shopper \u21aa 0 \u00b1 54), and (Bank \u21aa 0 \u00b1 47). Additionally, we measure the average Jensen-Shannon (JS) distance between the marginal distributions of synthetic and real data (0: identical distribution; 1: totally different distributions) and the average inverse KL-divergence (0: totally different distributions; 1: identical distribution). As shown, our technique consistently improves the utility of the synthetic data across all datasets and all DP mechanisms without degrading the performance of downstream models or statistical metrics.\n\n|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| F1 score\u00a0\u00a0\u00a0\u00a0| F1 score\u00a0\u00a0\u00a0\u00a0\u00a0| JS distance (marginal)\u00a0\u00a0| JS distance (marginal)\u00a0\u00a0| Inverse KL (marginal)\u00a0\u00a0| Inverse KL (marginal)\u00a0\u00a0|\n|----------|--------------|-----------------|----------------|-------------------|--------------------------|--------------------------|-------------------------|-------------------------|\n| Dataset\u00a0| DP Mechanism | Utility Improv. | w/o post-proc. | w/ post-proc.\u00a0\u00a0\u00a0| w/o post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| w/ post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| w/o post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0| w/ post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Adult\u00a0\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.13 \u00b7 0 \u00b1 03\u00a0\u00a0| 0.61\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.61\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.22 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.55\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.56\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.81 \u00b7 0 \u00b1 09\u00a0\u00a0| 0.22\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.33\u00b7 0 \u00b1 02 | 0.07\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.03\u00b7 0 \u00b1 02\u00a0\u00a0\u00a0\u00a0| 0.85\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.91\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.6 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.37\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.5\u00b7 0 \u00b1 03\u00a0| 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.72\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.87\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Mushroom | AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.12 \u00b7 0 \u00b1 0\u00a0\u00a0| 0.93\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.93\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.58 \u00b7 0 \u00b1 0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00b7 0 \u00b1 02 | 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.69 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.47\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.68\u00b7 0 \u00b1 01 | 0.05\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.92\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.83 \u00b7 0 \u00b1 05\u00a0\u00a0| 0.6\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.86\u00b7 0 \u00b1 04 | 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.87\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.95\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Shopper\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.1 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.48\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.48\u00b7 0 \u00b1 02 | 0.02\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.84\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.92\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.5 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.42\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.47\u00b7 0 \u00b1 02 | 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.36 \u00b7 0 \u00b1 05\u00a0\u00a0| 0.27\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.3\u00b7 0 \u00b1 02\u00a0| 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.74\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.85\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.11 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.25\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.31\u00b7 0 \u00b1 05 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.89\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Bank\u00a0\u00a0\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.18 \u00b7 0 \u00b1 01\u00a0\u00a0| 0.45\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.46\u00b7 0 \u00b1 01 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.9\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.32 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.43\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.44\u00b7 0 \u00b1 02 | 0.02\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.98\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.2 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.22\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.24\u00b7 0 \u00b1 07 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0| 0.71\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.88\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.25 \u00b7 0 \u00b1 03\u00a0\u00a0| 0.2\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.23\u00b7 0 \u00b1 05 | 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.9\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 2: Experimental results on the home-credit dataset. We compare synthetic data produced by GEM with \u03f5 \u2208 \u00b6 2 \u21aa 4 \u2666 against the post-processed synthetic data that are generated by applying our post-processing technique with \u03f5$_{post =1 to synthetic data generated from GEM with \u03f5 \u2208 \u00b6 1 \u21aa 3 \u2666. For reference, the downstream model trained and tested on real data has an F1 score of 0 \u00b1 24.\n\n| DP Mechanism\u00a0\u00a0| Utility Improv.\u00a0\u00a0|\u00a0\u00a0F1 score w/o post-proc. | F1 score w/ post-proc.\u00a0\u00a0|\u00a0\u00a0JS distance w/o post-proc. | JS distance w/ post-proc.\u00a0\u00a0|\u00a0\u00a0Inverse KL w/o post-proc. | Inverse KL w/ post-proc.\u00a0\u00a0|\n|----------------|-------------------|---------------------------|--------------------------|------------------------------|-----------------------------|-----------------------------|----------------------------|\n| GEM \u03f5 =2\u00a0\u00a0\u00a0\u00a0| 0.57 \u00b7 0 \u00b1 03\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.19 | 0.19\u00b7 0 \u00b1 02\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.01 | 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.93 | 0.97\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0\u00a0|\n| GEM \u03f5 =4\u00a0\u00a0\u00a0\u00a0| 0.68 \u00b7 0 \u00b1 01\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.21 | 0.21\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.01 | 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.95 | 0.99\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nResult. We present the experimental results on the UCI datasets in Table 1 (and Table 3 in Appendix C with a higher privacy budget). We report utility improvement, F1 score, and statistical measures. As shown, our post-processing technique consistently enhances the utility of synthetic data on selected metrics across all benchmark datasets and all privacy mechanisms. Moreover, the utility improvements are achieved without degrading the performance of downstream models or statistical properties of synthetic data. In other words, our post-processing procedure ensures that the logistic regression classifier trained on synthetic data can achieve comparable or even higher performance on real test data, while simultaneously reducing or maintaining the statistical divergences between synthetic and real data.\n\nWe present the experimental results on the home-credit dataset in Table 2 and illustrate the misalignment of the correlation matrix with and without applying our post-processing procedure in Figure 1. The results demonstrate that our algorithm consistently reduces the overall correlation misalignment. Additionally, our procedure, which includes computing the utility measures from real data, denoising the noisy answers, and computing optimal resampling weights, only takes around 4 mins on 1x NVIDIA GeForce RTX 3090 GPU.\n\nAnswer the following question: Create a .csv table of the f1 scores for training the same downstream model using real data. \n"], "reference": [",Adult,Mushroom,Shopper,Bank\nF1,0.61,0.95,0.54,0.47\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/tables/csv/qna.yaml", "question_id": "289674108930322995857811321290867913592", "category": "taxonomy", "turns": ["Given the context below:\nTable 1: We compare synthetic data generated without and with our post-processing technique, all under the same privacy budget \u03f5 =2. We demonstrate utility improvement (higher is better, positive numbers imply improvement) and F1 score for downstream models trained on synthetic data and tested on real data. For reference, when training the same downstream model using real data, the F1 scores are: (Adult \u21aa 0 \u00b1 61), (Mushroom \u21aa 0 \u00b1 95), (Shopper \u21aa 0 \u00b1 54), and (Bank \u21aa 0 \u00b1 47). Additionally, we measure the average Jensen-Shannon (JS) distance between the marginal distributions of synthetic and real data (0: identical distribution; 1: totally different distributions) and the average inverse KL-divergence (0: totally different distributions; 1: identical distribution). As shown, our technique consistently improves the utility of the synthetic data across all datasets and all DP mechanisms without degrading the performance of downstream models or statistical metrics.\n\n|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| F1 score\u00a0\u00a0\u00a0\u00a0| F1 score\u00a0\u00a0\u00a0\u00a0\u00a0| JS distance (marginal)\u00a0\u00a0| JS distance (marginal)\u00a0\u00a0| Inverse KL (marginal)\u00a0\u00a0| Inverse KL (marginal)\u00a0\u00a0|\n|----------|--------------|-----------------|----------------|-------------------|--------------------------|--------------------------|-------------------------|-------------------------|\n| Dataset\u00a0| DP Mechanism | Utility Improv. | w/o post-proc. | w/ post-proc.\u00a0\u00a0\u00a0| w/o post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| w/ post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| w/o post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0| w/ post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Adult\u00a0\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.13 \u00b7 0 \u00b1 03\u00a0\u00a0| 0.61\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.61\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.22 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.55\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.56\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.81 \u00b7 0 \u00b1 09\u00a0\u00a0| 0.22\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.33\u00b7 0 \u00b1 02 | 0.07\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.03\u00b7 0 \u00b1 02\u00a0\u00a0\u00a0\u00a0| 0.85\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.91\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.6 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.37\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.5\u00b7 0 \u00b1 03\u00a0| 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.72\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.87\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Mushroom | AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.12 \u00b7 0 \u00b1 0\u00a0\u00a0| 0.93\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.93\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.58 \u00b7 0 \u00b1 0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00b7 0 \u00b1 02 | 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.69 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.47\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.68\u00b7 0 \u00b1 01 | 0.05\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.92\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.83 \u00b7 0 \u00b1 05\u00a0\u00a0| 0.6\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.86\u00b7 0 \u00b1 04 | 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.87\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.95\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Shopper\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.1 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.48\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.48\u00b7 0 \u00b1 02 | 0.02\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.84\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.92\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.5 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.42\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.47\u00b7 0 \u00b1 02 | 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.36 \u00b7 0 \u00b1 05\u00a0\u00a0| 0.27\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.3\u00b7 0 \u00b1 02\u00a0| 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.74\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.85\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.11 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.25\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.31\u00b7 0 \u00b1 05 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.89\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Bank\u00a0\u00a0\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.18 \u00b7 0 \u00b1 01\u00a0\u00a0| 0.45\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.46\u00b7 0 \u00b1 01 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.9\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.32 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.43\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.44\u00b7 0 \u00b1 02 | 0.02\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.98\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.2 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.22\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.24\u00b7 0 \u00b1 07 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0| 0.71\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.88\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.25 \u00b7 0 \u00b1 03\u00a0\u00a0| 0.2\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.23\u00b7 0 \u00b1 05 | 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.9\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 2: Experimental results on the home-credit dataset. We compare synthetic data produced by GEM with \u03f5 \u2208 \u00b6 2 \u21aa 4 \u2666 against the post-processed synthetic data that are generated by applying our post-processing technique with \u03f5$_{post =1 to synthetic data generated from GEM with \u03f5 \u2208 \u00b6 1 \u21aa 3 \u2666. For reference, the downstream model trained and tested on real data has an F1 score of 0 \u00b1 24.\n\n| DP Mechanism\u00a0\u00a0| Utility Improv.\u00a0\u00a0|\u00a0\u00a0F1 score w/o post-proc. | F1 score w/ post-proc.\u00a0\u00a0|\u00a0\u00a0JS distance w/o post-proc. | JS distance w/ post-proc.\u00a0\u00a0|\u00a0\u00a0Inverse KL w/o post-proc. | Inverse KL w/ post-proc.\u00a0\u00a0|\n|----------------|-------------------|---------------------------|--------------------------|------------------------------|-----------------------------|-----------------------------|----------------------------|\n| GEM \u03f5 =2\u00a0\u00a0\u00a0\u00a0| 0.57 \u00b7 0 \u00b1 03\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.19 | 0.19\u00b7 0 \u00b1 02\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.01 | 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.93 | 0.97\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0\u00a0|\n| GEM \u03f5 =4\u00a0\u00a0\u00a0\u00a0| 0.68 \u00b7 0 \u00b1 01\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.21 | 0.21\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.01 | 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.95 | 0.99\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nResult. We present the experimental results on the UCI datasets in Table 1 (and Table 3 in Appendix C with a higher privacy budget). We report utility improvement, F1 score, and statistical measures. As shown, our post-processing technique consistently enhances the utility of synthetic data on selected metrics across all benchmark datasets and all privacy mechanisms. Moreover, the utility improvements are achieved without degrading the performance of downstream models or statistical properties of synthetic data. In other words, our post-processing procedure ensures that the logistic regression classifier trained on synthetic data can achieve comparable or even higher performance on real test data, while simultaneously reducing or maintaining the statistical divergences between synthetic and real data.\n\nWe present the experimental results on the home-credit dataset in Table 2 and illustrate the misalignment of the correlation matrix with and without applying our post-processing procedure in Figure 1. The results demonstrate that our algorithm consistently reduces the overall correlation misalignment. Additionally, our procedure, which includes computing the utility measures from real data, denoising the noisy answers, and computing optimal resampling weights, only takes around 4 mins on 1x NVIDIA GeForce RTX 3090 GPU.\n\nAnswer the following question: Create a new table in csv format with just the data set with the AIM DP mechanism and the f1 scores with post processing."], "reference": ["Dataset (DP mechanism),F1 score (w/post-proc)\nAdult (AIM),0.61 \u00b10.0\nMushroom (AIM),0.93 \u00b10.0\nShopper (AIM),0.48 \u00b10.02\nBank (AIM),0.46 \u00b10.01\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/tables/csv/qna.yaml", "question_id": "239729390783402182517747229067733741861", "category": "taxonomy", "turns": ["Given the context below:\nTable 1: We compare synthetic data generated without and with our post-processing technique, all under the same privacy budget \u03f5 =2. We demonstrate utility improvement (higher is better, positive numbers imply improvement) and F1 score for downstream models trained on synthetic data and tested on real data. For reference, when training the same downstream model using real data, the F1 scores are: (Adult \u21aa 0 \u00b1 61), (Mushroom \u21aa 0 \u00b1 95), (Shopper \u21aa 0 \u00b1 54), and (Bank \u21aa 0 \u00b1 47). Additionally, we measure the average Jensen-Shannon (JS) distance between the marginal distributions of synthetic and real data (0: identical distribution; 1: totally different distributions) and the average inverse KL-divergence (0: totally different distributions; 1: identical distribution). As shown, our technique consistently improves the utility of the synthetic data across all datasets and all DP mechanisms without degrading the performance of downstream models or statistical metrics.\n\n|\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| F1 score\u00a0\u00a0\u00a0\u00a0| F1 score\u00a0\u00a0\u00a0\u00a0\u00a0| JS distance (marginal)\u00a0\u00a0| JS distance (marginal)\u00a0\u00a0| Inverse KL (marginal)\u00a0\u00a0| Inverse KL (marginal)\u00a0\u00a0|\n|----------|--------------|-----------------|----------------|-------------------|--------------------------|--------------------------|-------------------------|-------------------------|\n| Dataset\u00a0| DP Mechanism | Utility Improv. | w/o post-proc. | w/ post-proc.\u00a0\u00a0\u00a0| w/o post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| w/ post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| w/o post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0| w/ post-proc.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Adult\u00a0\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.13 \u00b7 0 \u00b1 03\u00a0\u00a0| 0.61\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.61\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.22 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.55\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.56\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.81 \u00b7 0 \u00b1 09\u00a0\u00a0| 0.22\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.33\u00b7 0 \u00b1 02 | 0.07\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.03\u00b7 0 \u00b1 02\u00a0\u00a0\u00a0\u00a0| 0.85\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.91\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.6 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.37\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.5\u00b7 0 \u00b1 03\u00a0| 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.72\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.87\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Mushroom | AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.12 \u00b7 0 \u00b1 0\u00a0\u00a0| 0.93\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.93\u00b7 0 \u00b1 0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.58 \u00b7 0 \u00b1 0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00b7 0 \u00b1 02 | 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.69 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.47\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.68\u00b7 0 \u00b1 01 | 0.05\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.92\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.83 \u00b7 0 \u00b1 05\u00a0\u00a0| 0.6\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.86\u00b7 0 \u00b1 04 | 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.87\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.95\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Shopper\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.1 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.48\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.48\u00b7 0 \u00b1 02 | 0.02\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.84\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.92\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.5 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.42\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.47\u00b7 0 \u00b1 02 | 0.01\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.36 \u00b7 0 \u00b1 05\u00a0\u00a0| 0.27\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.3\u00b7 0 \u00b1 02\u00a0| 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.74\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.85\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.11 \u00b7 0 \u00b1 04\u00a0\u00a0| 0.25\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.31\u00b7 0 \u00b1 05 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.8\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.89\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n| Bank\u00a0\u00a0\u00a0| AIM\u00a0\u00a0\u00a0\u00a0\u00a0| 0.18 \u00b7 0 \u00b1 01\u00a0\u00a0| 0.45\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.46\u00b7 0 \u00b1 01 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.9\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| MST\u00a0\u00a0\u00a0\u00a0\u00a0| 0.32 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.43\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.44\u00b7 0 \u00b1 02 | 0.02\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.98\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 1\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| DPCTGAN\u00a0\u00a0\u00a0| 0.2 \u00b7 0 \u00b1 02\u00a0\u00a0| 0.22\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.24\u00b7 0 \u00b1 07 | 0.04\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.02\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0| 0.71\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.88\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0|\n|\u00a0\u00a0\u00a0\u00a0\u00a0| PATECTGAN\u00a0\u00a0| 0.25 \u00b7 0 \u00b1 03\u00a0\u00a0| 0.2\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.23\u00b7 0 \u00b1 05 | 0.03\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 0.9\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nTable 2: Experimental results on the home-credit dataset. We compare synthetic data produced by GEM with \u03f5 \u2208 \u00b6 2 \u21aa 4 \u2666 against the post-processed synthetic data that are generated by applying our post-processing technique with \u03f5$_{post =1 to synthetic data generated from GEM with \u03f5 \u2208 \u00b6 1 \u21aa 3 \u2666. For reference, the downstream model trained and tested on real data has an F1 score of 0 \u00b1 24.\n\n| DP Mechanism\u00a0\u00a0| Utility Improv.\u00a0\u00a0|\u00a0\u00a0F1 score w/o post-proc. | F1 score w/ post-proc.\u00a0\u00a0|\u00a0\u00a0JS distance w/o post-proc. | JS distance w/ post-proc.\u00a0\u00a0|\u00a0\u00a0Inverse KL w/o post-proc. | Inverse KL w/ post-proc.\u00a0\u00a0|\n|----------------|-------------------|---------------------------|--------------------------|------------------------------|-----------------------------|-----------------------------|----------------------------|\n| GEM \u03f5 =2\u00a0\u00a0\u00a0\u00a0| 0.57 \u00b7 0 \u00b1 03\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.19 | 0.19\u00b7 0 \u00b1 02\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.01 | 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.93 | 0.97\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0\u00a0|\n| GEM \u03f5 =4\u00a0\u00a0\u00a0\u00a0| 0.68 \u00b7 0 \u00b1 01\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.21 | 0.21\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.01 | 0.01\u00b7 0 \u00b1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.95 | 0.99\u00b7 0 \u00b1 01\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nResult. We present the experimental results on the UCI datasets in Table 1 (and Table 3 in Appendix C with a higher privacy budget). We report utility improvement, F1 score, and statistical measures. As shown, our post-processing technique consistently enhances the utility of synthetic data on selected metrics across all benchmark datasets and all privacy mechanisms. Moreover, the utility improvements are achieved without degrading the performance of downstream models or statistical properties of synthetic data. In other words, our post-processing procedure ensures that the logistic regression classifier trained on synthetic data can achieve comparable or even higher performance on real test data, while simultaneously reducing or maintaining the statistical divergences between synthetic and real data.\n\nWe present the experimental results on the home-credit dataset in Table 2 and illustrate the misalignment of the correlation matrix with and without applying our post-processing procedure in Figure 1. The results demonstrate that our algorithm consistently reduces the overall correlation misalignment. Additionally, our procedure, which includes computing the utility measures from real data, denoising the noisy answers, and computing optimal resampling weights, only takes around 4 mins on 1x NVIDIA GeForce RTX 3090 GPU.\n\nAnswer the following question: Create a table that takes the adult dataset and sorts it by largest to smallest utility improvement. Don't include results other than utility improvement.\n"], "reference": ["DP Mechanism,Utility Improvement\nDPCTGAN,0.81 \u00b10.09\nPATECTGAN,0.6 \u00b10.04\nMST,0.22 \u00b10.02\nAIM,0.13 \u00b10.03\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/bullet_points/qna.yaml", "question_id": "148750204803643997458491252562059189094", "category": "taxonomy", "turns": ["Given the context below:\n## 1.1 Related Work\n\nDP synthetic data generation mechanisms. Generating synthetic data using DP mechanisms is an active area of research [see e.g., HLM12, BLR13, GAH + 14, CXZX15, BSG17, AZK + 19, UV20, GMHI20, TMH + 21, VAA + 22, BSV22]. Along this line of work, the closest ones to ours are workload-aware methods [VTB + 20, ABK + 21, MMSM22, LVW21], which aim to ensure that the synthetic data perform well on a collection of queries. Our work differs in three key features. First, all existing approaches focus on generating private synthetic data from scratch, while we aim to post-process synthetic data to downweight samples that do not accurately represent the real data under the specified utility measures. As a result, our approach is more efficient as it does not need to fit a graphical model [MMSM22] or a neural network [LVW21] to represent the data distribution, and it is more extendable, allowing a single synthetic dataset to be quickly post-processed multiple times for different sets of utility measures as needed by downstream users. Second, some existing work may not scale well to large datasets as they either require solving an integer program multiple times [VTB + 20] or need to solve a large-scale optimization problem [ABK + 21]. In contrast, our approach is highly scalable, as it only requires solving a convex program whose number of variables is equal to the number of specified utility measures. Third, existing work often evaluates the quality of the synthetic data by how well it preserves key statistics (e.g., 3-way marginals) of the real data. In contrast, in our experiments, we evaluate our approach on the more stringent and realistic test of training various downstream ML models on the synthetic data and measuring their performance on real test data. The experimental results demonstrate that our approach can enhance the utility of synthetic data on selected measures without compromising their downstream quality.\n\nPublic data assisted methods/Post-processing methods. Our work introduces a post-processing procedure for improving the utility of a given synthetic dataset based on selected measures. As a special case, our approach can be applied to post-process publicly available datasets. In this regard, this work is related to public-data-assisted methods [see e.g., LVS + 21, LVW21], which leverage public data for saving privacy budgets. We extend Algorithm 1 in [LVS + 21] by formalizing the preservation of (noisy) utility measures as a constrained optimization problem, rather than minimizing the corresponding Lagrangian function. We further establish strong duality and propose a stochastic first-order method for solving this constrained optimization efficiently. We extend Algorithm 4 in [LVW21] by allowing any non-negative violation tolerance (i.e., any \u03b3 \u2265 0 in (3b)) compared with \u03b3 = 0 in [LVW21]. This extension offers increased flexibility, as users can now select various values of \u03b3 to navigate the trade-off between minimizing the distortion of the synthetic data and enhancing their utility on selected measures. Moreover, our experiments show that setting \u03b3 to be a small positive number (e.g., \u03b3 = 1 e-5) consistently outperforms when \u03b3 = 0. Finally, our work is related with [NWD20], which proposes to post-process outputs from differentially private GAN-based models for improving the quality of the generated synthetic data. However, their method is tailored to GAN-based privacy mechanisms while our approach is model-agnostic. This versatility is crucial, given that marginal-based and workload-based mechanisms often yield higher quality synthetic tabular data, as evidenced by benchmark experiments in [TMH + 21]. Our experiments indicate that our method consistently improves the utility of synthetic data produced by all kinds of privacy mechanisms, even when the initial synthetic data are of high quality.\n\n## 2 Preliminaries and Problem Formulation\n\nIn this section, we review differential privacy and provide an overview of our problem setup.\n\n## 2.1 Differential Privacy\n\nWe first recall the definition of differential privacy (DP) [DR14].\n\nDefinition 1. $^{A randomized mechanism}$M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-differential privacy, if for any $^{adjacent datasets}$D $^{and}$D ', which only differ in one individual's record, and all possible outputs $^{from the mechanism}$O \u2286 R, we have\n\nDP has two important properties: post-processing immunity and composition rule. Specifically, if M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-DP and g : R \u2192 R ' is any randomized (or deterministic) function, then\n\nAnswer the following question: In what ways does the DP synthetic data generation technique described in this paper differ from previous work? Return a bulleted list.\n"], "reference": ["- Leverages a more efficient approach to generating private synthetic data than existing methods that do so by scratch;\n- Is highly scalable and only requires solving a convex program whose number of variables is equal to the number of specified utility measures;\n- Is evaluated on more stringent and realistic test of training various downstream ML models on the synthetic data and measuring their performance on real test data.\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/bullet_points/qna.yaml", "question_id": "180135244477843915615135967686238026414", "category": "taxonomy", "turns": ["Given the context below:\n## 1.1 Related Work\n\nDP synthetic data generation mechanisms. Generating synthetic data using DP mechanisms is an active area of research [see e.g., HLM12, BLR13, GAH + 14, CXZX15, BSG17, AZK + 19, UV20, GMHI20, TMH + 21, VAA + 22, BSV22]. Along this line of work, the closest ones to ours are workload-aware methods [VTB + 20, ABK + 21, MMSM22, LVW21], which aim to ensure that the synthetic data perform well on a collection of queries. Our work differs in three key features. First, all existing approaches focus on generating private synthetic data from scratch, while we aim to post-process synthetic data to downweight samples that do not accurately represent the real data under the specified utility measures. As a result, our approach is more efficient as it does not need to fit a graphical model [MMSM22] or a neural network [LVW21] to represent the data distribution, and it is more extendable, allowing a single synthetic dataset to be quickly post-processed multiple times for different sets of utility measures as needed by downstream users. Second, some existing work may not scale well to large datasets as they either require solving an integer program multiple times [VTB + 20] or need to solve a large-scale optimization problem [ABK + 21]. In contrast, our approach is highly scalable, as it only requires solving a convex program whose number of variables is equal to the number of specified utility measures. Third, existing work often evaluates the quality of the synthetic data by how well it preserves key statistics (e.g., 3-way marginals) of the real data. In contrast, in our experiments, we evaluate our approach on the more stringent and realistic test of training various downstream ML models on the synthetic data and measuring their performance on real test data. The experimental results demonstrate that our approach can enhance the utility of synthetic data on selected measures without compromising their downstream quality.\n\nPublic data assisted methods/Post-processing methods. Our work introduces a post-processing procedure for improving the utility of a given synthetic dataset based on selected measures. As a special case, our approach can be applied to post-process publicly available datasets. In this regard, this work is related to public-data-assisted methods [see e.g., LVS + 21, LVW21], which leverage public data for saving privacy budgets. We extend Algorithm 1 in [LVS + 21] by formalizing the preservation of (noisy) utility measures as a constrained optimization problem, rather than minimizing the corresponding Lagrangian function. We further establish strong duality and propose a stochastic first-order method for solving this constrained optimization efficiently. We extend Algorithm 4 in [LVW21] by allowing any non-negative violation tolerance (i.e., any \u03b3 \u2265 0 in (3b)) compared with \u03b3 = 0 in [LVW21]. This extension offers increased flexibility, as users can now select various values of \u03b3 to navigate the trade-off between minimizing the distortion of the synthetic data and enhancing their utility on selected measures. Moreover, our experiments show that setting \u03b3 to be a small positive number (e.g., \u03b3 = 1 e-5) consistently outperforms when \u03b3 = 0. Finally, our work is related with [NWD20], which proposes to post-process outputs from differentially private GAN-based models for improving the quality of the generated synthetic data. However, their method is tailored to GAN-based privacy mechanisms while our approach is model-agnostic. This versatility is crucial, given that marginal-based and workload-based mechanisms often yield higher quality synthetic tabular data, as evidenced by benchmark experiments in [TMH + 21]. Our experiments indicate that our method consistently improves the utility of synthetic data produced by all kinds of privacy mechanisms, even when the initial synthetic data are of high quality.\n\n## 2 Preliminaries and Problem Formulation\n\nIn this section, we review differential privacy and provide an overview of our problem setup.\n\n## 2.1 Differential Privacy\n\nWe first recall the definition of differential privacy (DP) [DR14].\n\nDefinition 1. $^{A randomized mechanism}$M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-differential privacy, if for any $^{adjacent datasets}$D $^{and}$D ', which only differ in one individual's record, and all possible outputs $^{from the mechanism}$O \u2286 R, we have\n\nDP has two important properties: post-processing immunity and composition rule. Specifically, if M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-DP and g : R \u2192 R ' is any randomized (or deterministic) function, then\n\nAnswer the following question: Provide a list of the core contributions of this technique as it relates to existing work in the field."], "reference": ["- Extended Algorithm 1 in [LVS + 21] by formalizing the preservation of (noisy) utility measures as a constrained optimization problem, rather than minimizing the corresponding Lagrangian function.\n- Extended Algorithm 4 in [LVW21] by allowing any non-negative violation tolerance (i.e., any \u03b3 \u2265 0 in (3b)) compared with \u03b3 = 0 in [LVW21]\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/bullet_points/qna.yaml", "question_id": "140838421922418209889607826686014093612", "category": "taxonomy", "turns": ["Given the context below:\n## 1.1 Related Work\n\nDP synthetic data generation mechanisms. Generating synthetic data using DP mechanisms is an active area of research [see e.g., HLM12, BLR13, GAH + 14, CXZX15, BSG17, AZK + 19, UV20, GMHI20, TMH + 21, VAA + 22, BSV22]. Along this line of work, the closest ones to ours are workload-aware methods [VTB + 20, ABK + 21, MMSM22, LVW21], which aim to ensure that the synthetic data perform well on a collection of queries. Our work differs in three key features. First, all existing approaches focus on generating private synthetic data from scratch, while we aim to post-process synthetic data to downweight samples that do not accurately represent the real data under the specified utility measures. As a result, our approach is more efficient as it does not need to fit a graphical model [MMSM22] or a neural network [LVW21] to represent the data distribution, and it is more extendable, allowing a single synthetic dataset to be quickly post-processed multiple times for different sets of utility measures as needed by downstream users. Second, some existing work may not scale well to large datasets as they either require solving an integer program multiple times [VTB + 20] or need to solve a large-scale optimization problem [ABK + 21]. In contrast, our approach is highly scalable, as it only requires solving a convex program whose number of variables is equal to the number of specified utility measures. Third, existing work often evaluates the quality of the synthetic data by how well it preserves key statistics (e.g., 3-way marginals) of the real data. In contrast, in our experiments, we evaluate our approach on the more stringent and realistic test of training various downstream ML models on the synthetic data and measuring their performance on real test data. The experimental results demonstrate that our approach can enhance the utility of synthetic data on selected measures without compromising their downstream quality.\n\nPublic data assisted methods/Post-processing methods. Our work introduces a post-processing procedure for improving the utility of a given synthetic dataset based on selected measures. As a special case, our approach can be applied to post-process publicly available datasets. In this regard, this work is related to public-data-assisted methods [see e.g., LVS + 21, LVW21], which leverage public data for saving privacy budgets. We extend Algorithm 1 in [LVS + 21] by formalizing the preservation of (noisy) utility measures as a constrained optimization problem, rather than minimizing the corresponding Lagrangian function. We further establish strong duality and propose a stochastic first-order method for solving this constrained optimization efficiently. We extend Algorithm 4 in [LVW21] by allowing any non-negative violation tolerance (i.e., any \u03b3 \u2265 0 in (3b)) compared with \u03b3 = 0 in [LVW21]. This extension offers increased flexibility, as users can now select various values of \u03b3 to navigate the trade-off between minimizing the distortion of the synthetic data and enhancing their utility on selected measures. Moreover, our experiments show that setting \u03b3 to be a small positive number (e.g., \u03b3 = 1 e-5) consistently outperforms when \u03b3 = 0. Finally, our work is related with [NWD20], which proposes to post-process outputs from differentially private GAN-based models for improving the quality of the generated synthetic data. However, their method is tailored to GAN-based privacy mechanisms while our approach is model-agnostic. This versatility is crucial, given that marginal-based and workload-based mechanisms often yield higher quality synthetic tabular data, as evidenced by benchmark experiments in [TMH + 21]. Our experiments indicate that our method consistently improves the utility of synthetic data produced by all kinds of privacy mechanisms, even when the initial synthetic data are of high quality.\n\n## 2 Preliminaries and Problem Formulation\n\nIn this section, we review differential privacy and provide an overview of our problem setup.\n\n## 2.1 Differential Privacy\n\nWe first recall the definition of differential privacy (DP) [DR14].\n\nDefinition 1. $^{A randomized mechanism}$M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-differential privacy, if for any $^{adjacent datasets}$D $^{and}$D ', which only differ in one individual's record, and all possible outputs $^{from the mechanism}$O \u2286 R, we have\n\nDP has two important properties: post-processing immunity and composition rule. Specifically, if M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-DP and g : R \u2192 R ' is any randomized (or deterministic) function, then\n\nAnswer the following question: Return a list of all references in this paper.\n"], "reference": ["- HLM12\n- BLR13\n- GAH + 14\n- CXZX15\n- BSG17\n- AZK + 19\n- UV20\n- GMHI20\n- TMH + 21\n- VAA + 22\n- BSV22\n- VTB + 20\n- ABK + 21\n- MMSM22\n- LVW21\n- LVS + 21\n- NWD20\n- TMH + 21\n- DR14\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/bullet_points/qna.yaml", "question_id": "186374013887446797753042798545559700316", "category": "taxonomy", "turns": ["Given the context below:\ndata across multiple datasets and state-of-the-art privacy mechanisms. Additionally, it achieves this without degrading the performance of downstream models or statistical metrics. We provide additional experimental results and implementation details in Appendix C.\n\nBenchmark. We evaluate our algorithm on four benchmark datasets: Adult, Bank, Mushroom, and Shopping, which are from the UCI machine learning repository [DG17]. All the datasets have a target variable that can be used for a downstream prediction task. We use four existing DP mechanisms-AIM [MMSM22], MST [MMS21], DPCTGAN [RLP + 20], and PATECTGAN [RLP + 20]to generate private synthetic data. Among these DP mechanisms, AIM is a workload-based method; MST is a marginal-based method; DPCTGAN and PATECTGAN are GAN-based methods. All of their implementations are from the OpenDP library [Sma23]. To demonstrate the scalability of our approach, we conduct an additional experiment on the home-credit dataset [MOK18]. This dataset has 307,511 data points and 104 features. We apply GEM [LVW21] to this high-dimensional dataset for generating synthetic data. We pre-process each dataset to convert categorical columns into numerical features and standardize all features to the range [0 \u21aa 1].\n\nWe clarify that our objective is not to compare the quality of synthetic data generated by these DP mechanisms (for a benchmark evaluation, please refer to [TMH + 21]), but to demonstrate how our post-processing method can consistently improve the utility of synthetic data generated from these different DP mechanisms.\n\nSetup. We compare synthetic data produced by different privacy mechanisms with \u03f5 \u2208 \u00b6 2 \u21aa 4 \u2666 against the post-processed synthetic data that are generated by applying our post-processing technique with \u03f5$_{post}$ =1 to synthetic data generated from privacy mechanisms with \u03f5 \u2208 \u00b6 1 \u21aa 3 \u2666. For UCI datasets, we select 5 features, including the target variable, from the synthetic data that have the highest absolute correlation with the target variable. These features are chosen as they have a higher influence on downstream prediction tasks. The set of these features is denoted by S \u2286 [d]. Next, we define the utility measures as the first-order and second-order moment queries among the features in S : Q = \u00b6 q$_{i}$ (x) = x$_{i}$ \u2666 i \u2208 S \u222a \u00b6 q$_{i\u21aaj}$ (x) = x$_{i}$x$_{j}$ \u2666 i\u21aaj \u2208 S. For home-credit dataset, we follow the same setup but choosing 10 features.\n\nWe apply the Gaussian mechanism with (\u03f5$_{post}$ =1 \u21aa \u03b4$_{post}$ =1 \u25c1n $^{2}$) to estimate utility measures from the real data, where n denotes the number of real data points. Finally, we apply Algorithm 1 with \u03b3 = 1 e-5, a batch size of 256 for UCI datasets and 4096 for home-credit, and 200 epochs to compute the optimal resampling weights, which are then used to resample from the synthetic data with the same sample size. We repeat our experiment 5 times to obtain an error bar for each evaluation measure.\n\nEvaluation measure. We provide evaluation metrics for assessing the quality of synthetic data after post-processing. Our first metric, utility improvement, is defined as:\n\nThe range of this metric is (-\u221e \u21aa 1], where a value of 1 indicates a perfect alignment between the correlation matrix of the post-processed data and the real data.\n\nTo assess the downstream quality of synthetic data, we train predictive models using the synthetic data and then test their performance on real data. Specifically, we split the real data, using 80% for generating synthetic data and setting aside 20% to evaluate the performance of predictive models. We use the synthetic data to train a logistic regression classifier to predict the target variable based on other features. Then we apply this classifier to real data and calculate its F1 score. We use the function BinaryLogisticRegression from SDMetrics library [Dat23] to implement this process.\n\nWe compute two statistical measures to quantify the similarity between synthetic and real data distributions. The first measure is the average Jensen-Shannon distance between the marginal distributions of synthetic and real data, which ranges from 0 (identical distribution) to 1 (totally different distributions). The second measure is the average inverse KL-divergence between the marginal distributions of synthetic and real data, which ranges from 0 (totally different distributions) to 1 (identical distribution). These statistical measures are implemented by using synthcity library [QCvdS23].\n\nAnswer the following question: List the synthetic dataset generation methods referenced in the paper and the corresponding citation in a bulleted list. \n\n"], "reference": ["- **Synthetic Dataset Generation Methods:**\n - AIM [MMSM22]\n - MST [MMS21]\n - DPCTGAN. [RLP + 20]\n - PATECTGAN [RLP + 20]\n - GEM [LVW21]\n\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/bullet_points/qna.yaml", "question_id": "61032609450982071849422395957616920470", "category": "taxonomy", "turns": ["Given the context below:\ndata across multiple datasets and state-of-the-art privacy mechanisms. Additionally, it achieves this without degrading the performance of downstream models or statistical metrics. We provide additional experimental results and implementation details in Appendix C.\n\nBenchmark. We evaluate our algorithm on four benchmark datasets: Adult, Bank, Mushroom, and Shopping, which are from the UCI machine learning repository [DG17]. All the datasets have a target variable that can be used for a downstream prediction task. We use four existing DP mechanisms-AIM [MMSM22], MST [MMS21], DPCTGAN [RLP + 20], and PATECTGAN [RLP + 20]to generate private synthetic data. Among these DP mechanisms, AIM is a workload-based method; MST is a marginal-based method; DPCTGAN and PATECTGAN are GAN-based methods. All of their implementations are from the OpenDP library [Sma23]. To demonstrate the scalability of our approach, we conduct an additional experiment on the home-credit dataset [MOK18]. This dataset has 307,511 data points and 104 features. We apply GEM [LVW21] to this high-dimensional dataset for generating synthetic data. We pre-process each dataset to convert categorical columns into numerical features and standardize all features to the range [0 \u21aa 1].\n\nWe clarify that our objective is not to compare the quality of synthetic data generated by these DP mechanisms (for a benchmark evaluation, please refer to [TMH + 21]), but to demonstrate how our post-processing method can consistently improve the utility of synthetic data generated from these different DP mechanisms.\n\nSetup. We compare synthetic data produced by different privacy mechanisms with \u03f5 \u2208 \u00b6 2 \u21aa 4 \u2666 against the post-processed synthetic data that are generated by applying our post-processing technique with \u03f5$_{post}$ =1 to synthetic data generated from privacy mechanisms with \u03f5 \u2208 \u00b6 1 \u21aa 3 \u2666. For UCI datasets, we select 5 features, including the target variable, from the synthetic data that have the highest absolute correlation with the target variable. These features are chosen as they have a higher influence on downstream prediction tasks. The set of these features is denoted by S \u2286 [d]. Next, we define the utility measures as the first-order and second-order moment queries among the features in S : Q = \u00b6 q$_{i}$ (x) = x$_{i}$ \u2666 i \u2208 S \u222a \u00b6 q$_{i\u21aaj}$ (x) = x$_{i}$x$_{j}$ \u2666 i\u21aaj \u2208 S. For home-credit dataset, we follow the same setup but choosing 10 features.\n\nWe apply the Gaussian mechanism with (\u03f5$_{post}$ =1 \u21aa \u03b4$_{post}$ =1 \u25c1n $^{2}$) to estimate utility measures from the real data, where n denotes the number of real data points. Finally, we apply Algorithm 1 with \u03b3 = 1 e-5, a batch size of 256 for UCI datasets and 4096 for home-credit, and 200 epochs to compute the optimal resampling weights, which are then used to resample from the synthetic data with the same sample size. We repeat our experiment 5 times to obtain an error bar for each evaluation measure.\n\nEvaluation measure. We provide evaluation metrics for assessing the quality of synthetic data after post-processing. Our first metric, utility improvement, is defined as:\n\nThe range of this metric is (-\u221e \u21aa 1], where a value of 1 indicates a perfect alignment between the correlation matrix of the post-processed data and the real data.\n\nTo assess the downstream quality of synthetic data, we train predictive models using the synthetic data and then test their performance on real data. Specifically, we split the real data, using 80% for generating synthetic data and setting aside 20% to evaluate the performance of predictive models. We use the synthetic data to train a logistic regression classifier to predict the target variable based on other features. Then we apply this classifier to real data and calculate its F1 score. We use the function BinaryLogisticRegression from SDMetrics library [Dat23] to implement this process.\n\nWe compute two statistical measures to quantify the similarity between synthetic and real data distributions. The first measure is the average Jensen-Shannon distance between the marginal distributions of synthetic and real data, which ranges from 0 (identical distribution) to 1 (totally different distributions). The second measure is the average inverse KL-divergence between the marginal distributions of synthetic and real data, which ranges from 0 (totally different distributions) to 1 (identical distribution). These statistical measures are implemented by using synthcity library [QCvdS23].\n\nAnswer the following question: List the experimental parameters and their values used for running Algorithm 1."], "reference": ["- \u03b3,1e-5\n- batch\\_size (for UCI),256\n- batch\\_size (for home-credit),4096\n- epochs,200\n- experiment runs, 5\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/bullet_points/qna.yaml", "question_id": "11697594796626755231696765585470243870", "category": "taxonomy", "turns": ["Given the context below:\ndata across multiple datasets and state-of-the-art privacy mechanisms. Additionally, it achieves this without degrading the performance of downstream models or statistical metrics. We provide additional experimental results and implementation details in Appendix C.\n\nBenchmark. We evaluate our algorithm on four benchmark datasets: Adult, Bank, Mushroom, and Shopping, which are from the UCI machine learning repository [DG17]. All the datasets have a target variable that can be used for a downstream prediction task. We use four existing DP mechanisms-AIM [MMSM22], MST [MMS21], DPCTGAN [RLP + 20], and PATECTGAN [RLP + 20]to generate private synthetic data. Among these DP mechanisms, AIM is a workload-based method; MST is a marginal-based method; DPCTGAN and PATECTGAN are GAN-based methods. All of their implementations are from the OpenDP library [Sma23]. To demonstrate the scalability of our approach, we conduct an additional experiment on the home-credit dataset [MOK18]. This dataset has 307,511 data points and 104 features. We apply GEM [LVW21] to this high-dimensional dataset for generating synthetic data. We pre-process each dataset to convert categorical columns into numerical features and standardize all features to the range [0 \u21aa 1].\n\nWe clarify that our objective is not to compare the quality of synthetic data generated by these DP mechanisms (for a benchmark evaluation, please refer to [TMH + 21]), but to demonstrate how our post-processing method can consistently improve the utility of synthetic data generated from these different DP mechanisms.\n\nSetup. We compare synthetic data produced by different privacy mechanisms with \u03f5 \u2208 \u00b6 2 \u21aa 4 \u2666 against the post-processed synthetic data that are generated by applying our post-processing technique with \u03f5$_{post}$ =1 to synthetic data generated from privacy mechanisms with \u03f5 \u2208 \u00b6 1 \u21aa 3 \u2666. For UCI datasets, we select 5 features, including the target variable, from the synthetic data that have the highest absolute correlation with the target variable. These features are chosen as they have a higher influence on downstream prediction tasks. The set of these features is denoted by S \u2286 [d]. Next, we define the utility measures as the first-order and second-order moment queries among the features in S : Q = \u00b6 q$_{i}$ (x) = x$_{i}$ \u2666 i \u2208 S \u222a \u00b6 q$_{i\u21aaj}$ (x) = x$_{i}$x$_{j}$ \u2666 i\u21aaj \u2208 S. For home-credit dataset, we follow the same setup but choosing 10 features.\n\nWe apply the Gaussian mechanism with (\u03f5$_{post}$ =1 \u21aa \u03b4$_{post}$ =1 \u25c1n $^{2}$) to estimate utility measures from the real data, where n denotes the number of real data points. Finally, we apply Algorithm 1 with \u03b3 = 1 e-5, a batch size of 256 for UCI datasets and 4096 for home-credit, and 200 epochs to compute the optimal resampling weights, which are then used to resample from the synthetic data with the same sample size. We repeat our experiment 5 times to obtain an error bar for each evaluation measure.\n\nEvaluation measure. We provide evaluation metrics for assessing the quality of synthetic data after post-processing. Our first metric, utility improvement, is defined as:\n\nThe range of this metric is (-\u221e \u21aa 1], where a value of 1 indicates a perfect alignment between the correlation matrix of the post-processed data and the real data.\n\nTo assess the downstream quality of synthetic data, we train predictive models using the synthetic data and then test their performance on real data. Specifically, we split the real data, using 80% for generating synthetic data and setting aside 20% to evaluate the performance of predictive models. We use the synthetic data to train a logistic regression classifier to predict the target variable based on other features. Then we apply this classifier to real data and calculate its F1 score. We use the function BinaryLogisticRegression from SDMetrics library [Dat23] to implement this process.\n\nWe compute two statistical measures to quantify the similarity between synthetic and real data distributions. The first measure is the average Jensen-Shannon distance between the marginal distributions of synthetic and real data, which ranges from 0 (identical distribution) to 1 (totally different distributions). The second measure is the average inverse KL-divergence between the marginal distributions of synthetic and real data, which ranges from 0 (totally different distributions) to 1 (identical distribution). These statistical measures are implemented by using synthcity library [QCvdS23].\n\nAnswer the following question: List the metrics used to measure the distance between synthetic data and real data. For each metric, list the name, description and range of value. Display the result in bulleted list format.\n"], "reference": ["- **Average Jensen-Shannon Distance:**\u00a0Measures the average Jensen-Shannon Distance between marginal distributions, ranging from 0 (identical) to 1 (totally different).\n- **Average Inverse KL-Divergence:**\u00a0Measures the average Inverse KL-Divergence between marginal distributions, ranging from 0 (totally different) to 1 (identical).\n\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/plain_text/qna.yaml", "question_id": "245310128874741708696509667177113955227", "category": "taxonomy", "turns": ["Given the context below:\n## 1.1 Related Work\n\nDP synthetic data generation mechanisms. Generating synthetic data using DP mechanisms is an active area of research [see e.g., HLM12, BLR13, GAH + 14, CXZX15, BSG17, AZK + 19, UV20, GMHI20, TMH + 21, VAA + 22, BSV22]. Along this line of work, the closest ones to ours are workload-aware methods [VTB + 20, ABK + 21, MMSM22, LVW21], which aim to ensure that the synthetic data perform well on a collection of queries. Our work differs in three key features. First, all existing approaches focus on generating private synthetic data from scratch, while we aim to post-process synthetic data to downweight samples that do not accurately represent the real data under the specified utility measures. As a result, our approach is more efficient as it does not need to fit a graphical model [MMSM22] or a neural network [LVW21] to represent the data distribution, and it is more extendable, allowing a single synthetic dataset to be quickly post-processed multiple times for different sets of utility measures as needed by downstream users. Second, some existing work may not scale well to large datasets as they either require solving an integer program multiple times [VTB + 20] or need to solve a large-scale optimization problem [ABK + 21]. In contrast, our approach is highly scalable, as it only requires solving a convex program whose number of variables is equal to the number of specified utility measures. Third, existing work often evaluates the quality of the synthetic data by how well it preserves key statistics (e.g., 3-way marginals) of the real data. In contrast, in our experiments, we evaluate our approach on the more stringent and realistic test of training various downstream ML models on the synthetic data and measuring their performance on real test data. The experimental results demonstrate that our approach can enhance the utility of synthetic data on selected measures without compromising their downstream quality.\n\nPublic data assisted methods/Post-processing methods. Our work introduces a post-processing procedure for improving the utility of a given synthetic dataset based on selected measures. As a special case, our approach can be applied to post-process publicly available datasets. In this regard, this work is related to public-data-assisted methods [see e.g., LVS + 21, LVW21], which leverage public data for saving privacy budgets. We extend Algorithm 1 in [LVS + 21] by formalizing the preservation of (noisy) utility measures as a constrained optimization problem, rather than minimizing the corresponding Lagrangian function. We further establish strong duality and propose a stochastic first-order method for solving this constrained optimization efficiently. We extend Algorithm 4 in [LVW21] by allowing any non-negative violation tolerance (i.e., any \u03b3 \u2265 0 in (3b)) compared with \u03b3 = 0 in [LVW21]. This extension offers increased flexibility, as users can now select various values of \u03b3 to navigate the trade-off between minimizing the distortion of the synthetic data and enhancing their utility on selected measures. Moreover, our experiments show that setting \u03b3 to be a small positive number (e.g., \u03b3 = 1 e-5) consistently outperforms when \u03b3 = 0. Finally, our work is related with [NWD20], which proposes to post-process outputs from differentially private GAN-based models for improving the quality of the generated synthetic data. However, their method is tailored to GAN-based privacy mechanisms while our approach is model-agnostic. This versatility is crucial, given that marginal-based and workload-based mechanisms often yield higher quality synthetic tabular data, as evidenced by benchmark experiments in [TMH + 21]. Our experiments indicate that our method consistently improves the utility of synthetic data produced by all kinds of privacy mechanisms, even when the initial synthetic data are of high quality.\n\n## 2 Preliminaries and Problem Formulation\n\nIn this section, we review differential privacy and provide an overview of our problem setup.\n\n## 2.1 Differential Privacy\n\nWe first recall the definition of differential privacy (DP) [DR14].\n\nDefinition 1. $^{A randomized mechanism}$M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-differential privacy, if for any $^{adjacent datasets}$D $^{and}$D ', which only differ in one individual's record, and all possible outputs $^{from the mechanism}$O \u2286 R, we have\n\nDP has two important properties: post-processing immunity and composition rule. Specifically, if M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-DP and g : R \u2192 R ' is any randomized (or deterministic) function, then\n\nAnswer the following question: What are the two properties of differential privacy?\n"], "reference": ["post-processing immunity and composition rule\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/plain_text/qna.yaml", "question_id": "250220594764218393275708766547192249213", "category": "taxonomy", "turns": ["Given the context below:\n## 1.1 Related Work\n\nDP synthetic data generation mechanisms. Generating synthetic data using DP mechanisms is an active area of research [see e.g., HLM12, BLR13, GAH + 14, CXZX15, BSG17, AZK + 19, UV20, GMHI20, TMH + 21, VAA + 22, BSV22]. Along this line of work, the closest ones to ours are workload-aware methods [VTB + 20, ABK + 21, MMSM22, LVW21], which aim to ensure that the synthetic data perform well on a collection of queries. Our work differs in three key features. First, all existing approaches focus on generating private synthetic data from scratch, while we aim to post-process synthetic data to downweight samples that do not accurately represent the real data under the specified utility measures. As a result, our approach is more efficient as it does not need to fit a graphical model [MMSM22] or a neural network [LVW21] to represent the data distribution, and it is more extendable, allowing a single synthetic dataset to be quickly post-processed multiple times for different sets of utility measures as needed by downstream users. Second, some existing work may not scale well to large datasets as they either require solving an integer program multiple times [VTB + 20] or need to solve a large-scale optimization problem [ABK + 21]. In contrast, our approach is highly scalable, as it only requires solving a convex program whose number of variables is equal to the number of specified utility measures. Third, existing work often evaluates the quality of the synthetic data by how well it preserves key statistics (e.g., 3-way marginals) of the real data. In contrast, in our experiments, we evaluate our approach on the more stringent and realistic test of training various downstream ML models on the synthetic data and measuring their performance on real test data. The experimental results demonstrate that our approach can enhance the utility of synthetic data on selected measures without compromising their downstream quality.\n\nPublic data assisted methods/Post-processing methods. Our work introduces a post-processing procedure for improving the utility of a given synthetic dataset based on selected measures. As a special case, our approach can be applied to post-process publicly available datasets. In this regard, this work is related to public-data-assisted methods [see e.g., LVS + 21, LVW21], which leverage public data for saving privacy budgets. We extend Algorithm 1 in [LVS + 21] by formalizing the preservation of (noisy) utility measures as a constrained optimization problem, rather than minimizing the corresponding Lagrangian function. We further establish strong duality and propose a stochastic first-order method for solving this constrained optimization efficiently. We extend Algorithm 4 in [LVW21] by allowing any non-negative violation tolerance (i.e., any \u03b3 \u2265 0 in (3b)) compared with \u03b3 = 0 in [LVW21]. This extension offers increased flexibility, as users can now select various values of \u03b3 to navigate the trade-off between minimizing the distortion of the synthetic data and enhancing their utility on selected measures. Moreover, our experiments show that setting \u03b3 to be a small positive number (e.g., \u03b3 = 1 e-5) consistently outperforms when \u03b3 = 0. Finally, our work is related with [NWD20], which proposes to post-process outputs from differentially private GAN-based models for improving the quality of the generated synthetic data. However, their method is tailored to GAN-based privacy mechanisms while our approach is model-agnostic. This versatility is crucial, given that marginal-based and workload-based mechanisms often yield higher quality synthetic tabular data, as evidenced by benchmark experiments in [TMH + 21]. Our experiments indicate that our method consistently improves the utility of synthetic data produced by all kinds of privacy mechanisms, even when the initial synthetic data are of high quality.\n\n## 2 Preliminaries and Problem Formulation\n\nIn this section, we review differential privacy and provide an overview of our problem setup.\n\n## 2.1 Differential Privacy\n\nWe first recall the definition of differential privacy (DP) [DR14].\n\nDefinition 1. $^{A randomized mechanism}$M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-differential privacy, if for any $^{adjacent datasets}$D $^{and}$D ', which only differ in one individual's record, and all possible outputs $^{from the mechanism}$O \u2286 R, we have\n\nDP has two important properties: post-processing immunity and composition rule. Specifically, if M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-DP and g : R \u2192 R ' is any randomized (or deterministic) function, then\n\nAnswer the following question: Which references relate to active research in generation differentially private synthetic data?"], "reference": ["HLM12, BLR13, GAH + 14, CXZX15, BSG17, AZK + 19, UV20, GMHI20, TMH + 21, VAA + 22, and BSV22.\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/plain_text/qna.yaml", "question_id": "269112424790249293436830484762147517591", "category": "taxonomy", "turns": ["Given the context below:\n## 1.1 Related Work\n\nDP synthetic data generation mechanisms. Generating synthetic data using DP mechanisms is an active area of research [see e.g., HLM12, BLR13, GAH + 14, CXZX15, BSG17, AZK + 19, UV20, GMHI20, TMH + 21, VAA + 22, BSV22]. Along this line of work, the closest ones to ours are workload-aware methods [VTB + 20, ABK + 21, MMSM22, LVW21], which aim to ensure that the synthetic data perform well on a collection of queries. Our work differs in three key features. First, all existing approaches focus on generating private synthetic data from scratch, while we aim to post-process synthetic data to downweight samples that do not accurately represent the real data under the specified utility measures. As a result, our approach is more efficient as it does not need to fit a graphical model [MMSM22] or a neural network [LVW21] to represent the data distribution, and it is more extendable, allowing a single synthetic dataset to be quickly post-processed multiple times for different sets of utility measures as needed by downstream users. Second, some existing work may not scale well to large datasets as they either require solving an integer program multiple times [VTB + 20] or need to solve a large-scale optimization problem [ABK + 21]. In contrast, our approach is highly scalable, as it only requires solving a convex program whose number of variables is equal to the number of specified utility measures. Third, existing work often evaluates the quality of the synthetic data by how well it preserves key statistics (e.g., 3-way marginals) of the real data. In contrast, in our experiments, we evaluate our approach on the more stringent and realistic test of training various downstream ML models on the synthetic data and measuring their performance on real test data. The experimental results demonstrate that our approach can enhance the utility of synthetic data on selected measures without compromising their downstream quality.\n\nPublic data assisted methods/Post-processing methods. Our work introduces a post-processing procedure for improving the utility of a given synthetic dataset based on selected measures. As a special case, our approach can be applied to post-process publicly available datasets. In this regard, this work is related to public-data-assisted methods [see e.g., LVS + 21, LVW21], which leverage public data for saving privacy budgets. We extend Algorithm 1 in [LVS + 21] by formalizing the preservation of (noisy) utility measures as a constrained optimization problem, rather than minimizing the corresponding Lagrangian function. We further establish strong duality and propose a stochastic first-order method for solving this constrained optimization efficiently. We extend Algorithm 4 in [LVW21] by allowing any non-negative violation tolerance (i.e., any \u03b3 \u2265 0 in (3b)) compared with \u03b3 = 0 in [LVW21]. This extension offers increased flexibility, as users can now select various values of \u03b3 to navigate the trade-off between minimizing the distortion of the synthetic data and enhancing their utility on selected measures. Moreover, our experiments show that setting \u03b3 to be a small positive number (e.g., \u03b3 = 1 e-5) consistently outperforms when \u03b3 = 0. Finally, our work is related with [NWD20], which proposes to post-process outputs from differentially private GAN-based models for improving the quality of the generated synthetic data. However, their method is tailored to GAN-based privacy mechanisms while our approach is model-agnostic. This versatility is crucial, given that marginal-based and workload-based mechanisms often yield higher quality synthetic tabular data, as evidenced by benchmark experiments in [TMH + 21]. Our experiments indicate that our method consistently improves the utility of synthetic data produced by all kinds of privacy mechanisms, even when the initial synthetic data are of high quality.\n\n## 2 Preliminaries and Problem Formulation\n\nIn this section, we review differential privacy and provide an overview of our problem setup.\n\n## 2.1 Differential Privacy\n\nWe first recall the definition of differential privacy (DP) [DR14].\n\nDefinition 1. $^{A randomized mechanism}$M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-differential privacy, if for any $^{adjacent datasets}$D $^{and}$D ', which only differ in one individual's record, and all possible outputs $^{from the mechanism}$O \u2286 R, we have\n\nDP has two important properties: post-processing immunity and composition rule. Specifically, if M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-DP and g : R \u2192 R ' is any randomized (or deterministic) function, then\n\nAnswer the following question: Why is some existing work in this field not scalable?\n"], "reference": ["Some existing work may not scale well to large datasets as they either require solving an integer program multiple times [VTB + 20] or need to solve a large-scale optimization problem [ABK + 21]\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/plain_text/qna.yaml", "question_id": "293053408778727710441795581746432326752", "category": "taxonomy", "turns": ["Given the context below:\ndata across multiple datasets and state-of-the-art privacy mechanisms. Additionally, it achieves this without degrading the performance of downstream models or statistical metrics. We provide additional experimental results and implementation details in Appendix C.\n\nBenchmark. We evaluate our algorithm on four benchmark datasets: Adult, Bank, Mushroom, and Shopping, which are from the UCI machine learning repository [DG17]. All the datasets have a target variable that can be used for a downstream prediction task. We use four existing DP mechanisms-AIM [MMSM22], MST [MMS21], DPCTGAN [RLP + 20], and PATECTGAN [RLP + 20]to generate private synthetic data. Among these DP mechanisms, AIM is a workload-based method; MST is a marginal-based method; DPCTGAN and PATECTGAN are GAN-based methods. All of their implementations are from the OpenDP library [Sma23]. To demonstrate the scalability of our approach, we conduct an additional experiment on the home-credit dataset [MOK18]. This dataset has 307,511 data points and 104 features. We apply GEM [LVW21] to this high-dimensional dataset for generating synthetic data. We pre-process each dataset to convert categorical columns into numerical features and standardize all features to the range [0 \u21aa 1].\n\nWe clarify that our objective is not to compare the quality of synthetic data generated by these DP mechanisms (for a benchmark evaluation, please refer to [TMH + 21]), but to demonstrate how our post-processing method can consistently improve the utility of synthetic data generated from these different DP mechanisms.\n\nSetup. We compare synthetic data produced by different privacy mechanisms with \u03f5 \u2208 \u00b6 2 \u21aa 4 \u2666 against the post-processed synthetic data that are generated by applying our post-processing technique with \u03f5$_{post}$ =1 to synthetic data generated from privacy mechanisms with \u03f5 \u2208 \u00b6 1 \u21aa 3 \u2666. For UCI datasets, we select 5 features, including the target variable, from the synthetic data that have the highest absolute correlation with the target variable. These features are chosen as they have a higher influence on downstream prediction tasks. The set of these features is denoted by S \u2286 [d]. Next, we define the utility measures as the first-order and second-order moment queries among the features in S : Q = \u00b6 q$_{i}$ (x) = x$_{i}$ \u2666 i \u2208 S \u222a \u00b6 q$_{i\u21aaj}$ (x) = x$_{i}$x$_{j}$ \u2666 i\u21aaj \u2208 S. For home-credit dataset, we follow the same setup but choosing 10 features.\n\nWe apply the Gaussian mechanism with (\u03f5$_{post}$ =1 \u21aa \u03b4$_{post}$ =1 \u25c1n $^{2}$) to estimate utility measures from the real data, where n denotes the number of real data points. Finally, we apply Algorithm 1 with \u03b3 = 1 e-5, a batch size of 256 for UCI datasets and 4096 for home-credit, and 200 epochs to compute the optimal resampling weights, which are then used to resample from the synthetic data with the same sample size. We repeat our experiment 5 times to obtain an error bar for each evaluation measure.\n\nEvaluation measure. We provide evaluation metrics for assessing the quality of synthetic data after post-processing. Our first metric, utility improvement, is defined as:\n\nThe range of this metric is (-\u221e \u21aa 1], where a value of 1 indicates a perfect alignment between the correlation matrix of the post-processed data and the real data.\n\nTo assess the downstream quality of synthetic data, we train predictive models using the synthetic data and then test their performance on real data. Specifically, we split the real data, using 80% for generating synthetic data and setting aside 20% to evaluate the performance of predictive models. We use the synthetic data to train a logistic regression classifier to predict the target variable based on other features. Then we apply this classifier to real data and calculate its F1 score. We use the function BinaryLogisticRegression from SDMetrics library [Dat23] to implement this process.\n\nWe compute two statistical measures to quantify the similarity between synthetic and real data distributions. The first measure is the average Jensen-Shannon distance between the marginal distributions of synthetic and real data, which ranges from 0 (identical distribution) to 1 (totally different distributions). The second measure is the average inverse KL-divergence between the marginal distributions of synthetic and real data, which ranges from 0 (totally different distributions) to 1 (identical distribution). These statistical measures are implemented by using synthcity library [QCvdS23].\n\nAnswer the following question: What are the three types of evaluation metrics used in the paper? \n"], "reference": ["Utility Improvement, Predictive Performance on real data, Distance Metrics.\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/plain_text/qna.yaml", "question_id": "34497557727086378523096627629463694542", "category": "taxonomy", "turns": ["Given the context below:\ndata across multiple datasets and state-of-the-art privacy mechanisms. Additionally, it achieves this without degrading the performance of downstream models or statistical metrics. We provide additional experimental results and implementation details in Appendix C.\n\nBenchmark. We evaluate our algorithm on four benchmark datasets: Adult, Bank, Mushroom, and Shopping, which are from the UCI machine learning repository [DG17]. All the datasets have a target variable that can be used for a downstream prediction task. We use four existing DP mechanisms-AIM [MMSM22], MST [MMS21], DPCTGAN [RLP + 20], and PATECTGAN [RLP + 20]to generate private synthetic data. Among these DP mechanisms, AIM is a workload-based method; MST is a marginal-based method; DPCTGAN and PATECTGAN are GAN-based methods. All of their implementations are from the OpenDP library [Sma23]. To demonstrate the scalability of our approach, we conduct an additional experiment on the home-credit dataset [MOK18]. This dataset has 307,511 data points and 104 features. We apply GEM [LVW21] to this high-dimensional dataset for generating synthetic data. We pre-process each dataset to convert categorical columns into numerical features and standardize all features to the range [0 \u21aa 1].\n\nWe clarify that our objective is not to compare the quality of synthetic data generated by these DP mechanisms (for a benchmark evaluation, please refer to [TMH + 21]), but to demonstrate how our post-processing method can consistently improve the utility of synthetic data generated from these different DP mechanisms.\n\nSetup. We compare synthetic data produced by different privacy mechanisms with \u03f5 \u2208 \u00b6 2 \u21aa 4 \u2666 against the post-processed synthetic data that are generated by applying our post-processing technique with \u03f5$_{post}$ =1 to synthetic data generated from privacy mechanisms with \u03f5 \u2208 \u00b6 1 \u21aa 3 \u2666. For UCI datasets, we select 5 features, including the target variable, from the synthetic data that have the highest absolute correlation with the target variable. These features are chosen as they have a higher influence on downstream prediction tasks. The set of these features is denoted by S \u2286 [d]. Next, we define the utility measures as the first-order and second-order moment queries among the features in S : Q = \u00b6 q$_{i}$ (x) = x$_{i}$ \u2666 i \u2208 S \u222a \u00b6 q$_{i\u21aaj}$ (x) = x$_{i}$x$_{j}$ \u2666 i\u21aaj \u2208 S. For home-credit dataset, we follow the same setup but choosing 10 features.\n\nWe apply the Gaussian mechanism with (\u03f5$_{post}$ =1 \u21aa \u03b4$_{post}$ =1 \u25c1n $^{2}$) to estimate utility measures from the real data, where n denotes the number of real data points. Finally, we apply Algorithm 1 with \u03b3 = 1 e-5, a batch size of 256 for UCI datasets and 4096 for home-credit, and 200 epochs to compute the optimal resampling weights, which are then used to resample from the synthetic data with the same sample size. We repeat our experiment 5 times to obtain an error bar for each evaluation measure.\n\nEvaluation measure. We provide evaluation metrics for assessing the quality of synthetic data after post-processing. Our first metric, utility improvement, is defined as:\n\nThe range of this metric is (-\u221e \u21aa 1], where a value of 1 indicates a perfect alignment between the correlation matrix of the post-processed data and the real data.\n\nTo assess the downstream quality of synthetic data, we train predictive models using the synthetic data and then test their performance on real data. Specifically, we split the real data, using 80% for generating synthetic data and setting aside 20% to evaluate the performance of predictive models. We use the synthetic data to train a logistic regression classifier to predict the target variable based on other features. Then we apply this classifier to real data and calculate its F1 score. We use the function BinaryLogisticRegression from SDMetrics library [Dat23] to implement this process.\n\nWe compute two statistical measures to quantify the similarity between synthetic and real data distributions. The first measure is the average Jensen-Shannon distance between the marginal distributions of synthetic and real data, which ranges from 0 (identical distribution) to 1 (totally different distributions). The second measure is the average inverse KL-divergence between the marginal distributions of synthetic and real data, which ranges from 0 (totally different distributions) to 1 (identical distribution). These statistical measures are implemented by using synthcity library [QCvdS23].\n\nAnswer the following question: What are the metrics used to measure the distributional distance between real data and synthetic data? "], "reference": ["There are two metrics: Average Jensen-Shannon Distance and Average Inverse KL-Divergence. \n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/plain_text/qna.yaml", "question_id": "61768416089125380434259677824528298857", "category": "taxonomy", "turns": ["Given the context below:\ndata across multiple datasets and state-of-the-art privacy mechanisms. Additionally, it achieves this without degrading the performance of downstream models or statistical metrics. We provide additional experimental results and implementation details in Appendix C.\n\nBenchmark. We evaluate our algorithm on four benchmark datasets: Adult, Bank, Mushroom, and Shopping, which are from the UCI machine learning repository [DG17]. All the datasets have a target variable that can be used for a downstream prediction task. We use four existing DP mechanisms-AIM [MMSM22], MST [MMS21], DPCTGAN [RLP + 20], and PATECTGAN [RLP + 20]to generate private synthetic data. Among these DP mechanisms, AIM is a workload-based method; MST is a marginal-based method; DPCTGAN and PATECTGAN are GAN-based methods. All of their implementations are from the OpenDP library [Sma23]. To demonstrate the scalability of our approach, we conduct an additional experiment on the home-credit dataset [MOK18]. This dataset has 307,511 data points and 104 features. We apply GEM [LVW21] to this high-dimensional dataset for generating synthetic data. We pre-process each dataset to convert categorical columns into numerical features and standardize all features to the range [0 \u21aa 1].\n\nWe clarify that our objective is not to compare the quality of synthetic data generated by these DP mechanisms (for a benchmark evaluation, please refer to [TMH + 21]), but to demonstrate how our post-processing method can consistently improve the utility of synthetic data generated from these different DP mechanisms.\n\nSetup. We compare synthetic data produced by different privacy mechanisms with \u03f5 \u2208 \u00b6 2 \u21aa 4 \u2666 against the post-processed synthetic data that are generated by applying our post-processing technique with \u03f5$_{post}$ =1 to synthetic data generated from privacy mechanisms with \u03f5 \u2208 \u00b6 1 \u21aa 3 \u2666. For UCI datasets, we select 5 features, including the target variable, from the synthetic data that have the highest absolute correlation with the target variable. These features are chosen as they have a higher influence on downstream prediction tasks. The set of these features is denoted by S \u2286 [d]. Next, we define the utility measures as the first-order and second-order moment queries among the features in S : Q = \u00b6 q$_{i}$ (x) = x$_{i}$ \u2666 i \u2208 S \u222a \u00b6 q$_{i\u21aaj}$ (x) = x$_{i}$x$_{j}$ \u2666 i\u21aaj \u2208 S. For home-credit dataset, we follow the same setup but choosing 10 features.\n\nWe apply the Gaussian mechanism with (\u03f5$_{post}$ =1 \u21aa \u03b4$_{post}$ =1 \u25c1n $^{2}$) to estimate utility measures from the real data, where n denotes the number of real data points. Finally, we apply Algorithm 1 with \u03b3 = 1 e-5, a batch size of 256 for UCI datasets and 4096 for home-credit, and 200 epochs to compute the optimal resampling weights, which are then used to resample from the synthetic data with the same sample size. We repeat our experiment 5 times to obtain an error bar for each evaluation measure.\n\nEvaluation measure. We provide evaluation metrics for assessing the quality of synthetic data after post-processing. Our first metric, utility improvement, is defined as:\n\nThe range of this metric is (-\u221e \u21aa 1], where a value of 1 indicates a perfect alignment between the correlation matrix of the post-processed data and the real data.\n\nTo assess the downstream quality of synthetic data, we train predictive models using the synthetic data and then test their performance on real data. Specifically, we split the real data, using 80% for generating synthetic data and setting aside 20% to evaluate the performance of predictive models. We use the synthetic data to train a logistic regression classifier to predict the target variable based on other features. Then we apply this classifier to real data and calculate its F1 score. We use the function BinaryLogisticRegression from SDMetrics library [Dat23] to implement this process.\n\nWe compute two statistical measures to quantify the similarity between synthetic and real data distributions. The first measure is the average Jensen-Shannon distance between the marginal distributions of synthetic and real data, which ranges from 0 (identical distribution) to 1 (totally different distributions). The second measure is the average inverse KL-divergence between the marginal distributions of synthetic and real data, which ranges from 0 (totally different distributions) to 1 (identical distribution). These statistical measures are implemented by using synthcity library [QCvdS23].\n\nAnswer the following question: What is the citation for MST approach? \n"], "reference": ["[MMS21].\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/markdown/qna.yaml", "question_id": "29411256457558837535538901011368774083", "category": "taxonomy", "turns": ["Given the context below:\n## 1.1 Related Work\n\nDP synthetic data generation mechanisms. Generating synthetic data using DP mechanisms is an active area of research [see e.g., HLM12, BLR13, GAH + 14, CXZX15, BSG17, AZK + 19, UV20, GMHI20, TMH + 21, VAA + 22, BSV22]. Along this line of work, the closest ones to ours are workload-aware methods [VTB + 20, ABK + 21, MMSM22, LVW21], which aim to ensure that the synthetic data perform well on a collection of queries. Our work differs in three key features. First, all existing approaches focus on generating private synthetic data from scratch, while we aim to post-process synthetic data to downweight samples that do not accurately represent the real data under the specified utility measures. As a result, our approach is more efficient as it does not need to fit a graphical model [MMSM22] or a neural network [LVW21] to represent the data distribution, and it is more extendable, allowing a single synthetic dataset to be quickly post-processed multiple times for different sets of utility measures as needed by downstream users. Second, some existing work may not scale well to large datasets as they either require solving an integer program multiple times [VTB + 20] or need to solve a large-scale optimization problem [ABK + 21]. In contrast, our approach is highly scalable, as it only requires solving a convex program whose number of variables is equal to the number of specified utility measures. Third, existing work often evaluates the quality of the synthetic data by how well it preserves key statistics (e.g., 3-way marginals) of the real data. In contrast, in our experiments, we evaluate our approach on the more stringent and realistic test of training various downstream ML models on the synthetic data and measuring their performance on real test data. The experimental results demonstrate that our approach can enhance the utility of synthetic data on selected measures without compromising their downstream quality.\n\nPublic data assisted methods/Post-processing methods. Our work introduces a post-processing procedure for improving the utility of a given synthetic dataset based on selected measures. As a special case, our approach can be applied to post-process publicly available datasets. In this regard, this work is related to public-data-assisted methods [see e.g., LVS + 21, LVW21], which leverage public data for saving privacy budgets. We extend Algorithm 1 in [LVS + 21] by formalizing the preservation of (noisy) utility measures as a constrained optimization problem, rather than minimizing the corresponding Lagrangian function. We further establish strong duality and propose a stochastic first-order method for solving this constrained optimization efficiently. We extend Algorithm 4 in [LVW21] by allowing any non-negative violation tolerance (i.e., any \u03b3 \u2265 0 in (3b)) compared with \u03b3 = 0 in [LVW21]. This extension offers increased flexibility, as users can now select various values of \u03b3 to navigate the trade-off between minimizing the distortion of the synthetic data and enhancing their utility on selected measures. Moreover, our experiments show that setting \u03b3 to be a small positive number (e.g., \u03b3 = 1 e-5) consistently outperforms when \u03b3 = 0. Finally, our work is related with [NWD20], which proposes to post-process outputs from differentially private GAN-based models for improving the quality of the generated synthetic data. However, their method is tailored to GAN-based privacy mechanisms while our approach is model-agnostic. This versatility is crucial, given that marginal-based and workload-based mechanisms often yield higher quality synthetic tabular data, as evidenced by benchmark experiments in [TMH + 21]. Our experiments indicate that our method consistently improves the utility of synthetic data produced by all kinds of privacy mechanisms, even when the initial synthetic data are of high quality.\n\n## 2 Preliminaries and Problem Formulation\n\nIn this section, we review differential privacy and provide an overview of our problem setup.\n\n## 2.1 Differential Privacy\n\nWe first recall the definition of differential privacy (DP) [DR14].\n\nDefinition 1. $^{A randomized mechanism}$M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-differential privacy, if for any $^{adjacent datasets}$D $^{and}$D ', which only differ in one individual's record, and all possible outputs $^{from the mechanism}$O \u2286 R, we have\n\nDP has two important properties: post-processing immunity and composition rule. Specifically, if M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-DP and g : R \u2192 R ' is any randomized (or deterministic) function, then\n\nAnswer the following question: Update this markdown document by changing headers from level 2 to level 3.\n"], "reference": ["\\### 1.1 Related Work\n\nDP synthetic data generation mechanisms. Generating synthetic data using DP mechanisms is an active area of research [see e.g., HLM12, BLR13, GAH + 14, CXZX15, BSG17, AZK + 19, UV20, GMHI20, TMH + 21, VAA + 22, BSV22]. Along this line of work, the closest ones to ours are workload-aware methods [VTB + 20, ABK + 21, MMSM22, LVW21], which aim to ensure that the synthetic data perform well on a collection of queries. Our work differs in three key features. First, all existing approaches focus on generating private synthetic data from scratch, while we aim to post-process synthetic data to downweight samples that do not accurately represent the real data under the specified utility measures. As a result, our approach is more efficient as it does not need to fit a graphical model [MMSM22] or a neural network [LVW21] to represent the data distribution, and it is more extendable, allowing a single synthetic dataset to be quickly post-processed multiple times for different sets of utility measures as needed by downstream users. Second, some existing work may not scale well to large datasets as they either require solving an integer program multiple times [VTB + 20] or need to solve a large-scale optimization problem [ABK + 21]. In contrast, our approach is highly scalable, as it only requires solving a convex program whose number of variables is equal to the number of specified utility measures. Third, existing work often evaluates the quality of the synthetic data by how well it preserves key statistics (e.g., 3-way marginals) of the real data. In contrast, in our experiments, we evaluate our approach on the more stringent and realistic test of training various downstream ML models on the synthetic data and measuring their performance on real test data. The experimental results demonstrate that our approach can enhance the utility of synthetic data on selected measures without compromising their downstream quality.\n\nPublic data assisted methods/Post-processing methods. Our work introduces a post-processing procedure for improving the utility of a given synthetic dataset based on selected measures. As a special case, our approach can be applied to post-process publicly available datasets. In this regard, this work is related to public-data-assisted methods [see e.g., LVS + 21, LVW21], which leverage public data for saving privacy budgets. We extend Algorithm 1 in [LVS + 21] by formalizing the preservation of (noisy) utility measures as a constrained optimization problem, rather than minimizing the corresponding Lagrangian function. We further establish strong duality and propose a stochastic first-order method for solving this constrained optimization efficiently. We extend Algorithm 4 in [LVW21] by allowing any non-negative violation tolerance (i.e., any \u03b3 \u2265 0 in (3b)) compared with \u03b3 = 0 in [LVW21]. This extension offers increased flexibility, as users can now select various values of \u03b3 to navigate the trade-off between minimizing the distortion of the synthetic data and enhancing their utility on selected measures. Moreover, our experiments show that setting \u03b3 to be a small positive number (e.g., \u03b3 = 1 e-5) consistently outperforms when \u03b3 = 0. Finally, our work is related with [NWD20], which proposes to post-process outputs from differentially private GAN-based models for improving the quality of the generated synthetic data. However, their method is tailored to GAN-based privacy mechanisms while our approach is model-agnostic. This versatility is crucial, given that marginal-based and workload-based mechanisms often yield higher quality synthetic tabular data, as evidenced by benchmark experiments in [TMH + 21]. Our experiments indicate that our method consistently improves the utility of synthetic data produced by all kinds of privacy mechanisms, even when the initial synthetic data are of high quality.\n\n\\### 2 Preliminaries and Problem Formulation\n\nIn this section, we review differential privacy and provide an overview of our problem setup.\n\n\\### 2.1 Differential Privacy\n\nWe first recall the definition of differential privacy (DP) [DR14].\n\nDefinition 1. $^{A randomized mechanism}$M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-differential privacy, if for any $^{adjacent datasets}$D $^{and}$D ', which only differ in one individual's record, and all possible outputs $^{from the mechanism}$O \u2286 R, we have\n\nDP has two important properties: post-processing immunity and composition rule. Specifically, if M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-DP and g : R \u2192 R ' is any randomized (or deterministic) function, then\n\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/markdown/qna.yaml", "question_id": "4085903994725864634496471302763152512", "category": "taxonomy", "turns": ["Given the context below:\n## 1.1 Related Work\n\nDP synthetic data generation mechanisms. Generating synthetic data using DP mechanisms is an active area of research [see e.g., HLM12, BLR13, GAH + 14, CXZX15, BSG17, AZK + 19, UV20, GMHI20, TMH + 21, VAA + 22, BSV22]. Along this line of work, the closest ones to ours are workload-aware methods [VTB + 20, ABK + 21, MMSM22, LVW21], which aim to ensure that the synthetic data perform well on a collection of queries. Our work differs in three key features. First, all existing approaches focus on generating private synthetic data from scratch, while we aim to post-process synthetic data to downweight samples that do not accurately represent the real data under the specified utility measures. As a result, our approach is more efficient as it does not need to fit a graphical model [MMSM22] or a neural network [LVW21] to represent the data distribution, and it is more extendable, allowing a single synthetic dataset to be quickly post-processed multiple times for different sets of utility measures as needed by downstream users. Second, some existing work may not scale well to large datasets as they either require solving an integer program multiple times [VTB + 20] or need to solve a large-scale optimization problem [ABK + 21]. In contrast, our approach is highly scalable, as it only requires solving a convex program whose number of variables is equal to the number of specified utility measures. Third, existing work often evaluates the quality of the synthetic data by how well it preserves key statistics (e.g., 3-way marginals) of the real data. In contrast, in our experiments, we evaluate our approach on the more stringent and realistic test of training various downstream ML models on the synthetic data and measuring their performance on real test data. The experimental results demonstrate that our approach can enhance the utility of synthetic data on selected measures without compromising their downstream quality.\n\nPublic data assisted methods/Post-processing methods. Our work introduces a post-processing procedure for improving the utility of a given synthetic dataset based on selected measures. As a special case, our approach can be applied to post-process publicly available datasets. In this regard, this work is related to public-data-assisted methods [see e.g., LVS + 21, LVW21], which leverage public data for saving privacy budgets. We extend Algorithm 1 in [LVS + 21] by formalizing the preservation of (noisy) utility measures as a constrained optimization problem, rather than minimizing the corresponding Lagrangian function. We further establish strong duality and propose a stochastic first-order method for solving this constrained optimization efficiently. We extend Algorithm 4 in [LVW21] by allowing any non-negative violation tolerance (i.e., any \u03b3 \u2265 0 in (3b)) compared with \u03b3 = 0 in [LVW21]. This extension offers increased flexibility, as users can now select various values of \u03b3 to navigate the trade-off between minimizing the distortion of the synthetic data and enhancing their utility on selected measures. Moreover, our experiments show that setting \u03b3 to be a small positive number (e.g., \u03b3 = 1 e-5) consistently outperforms when \u03b3 = 0. Finally, our work is related with [NWD20], which proposes to post-process outputs from differentially private GAN-based models for improving the quality of the generated synthetic data. However, their method is tailored to GAN-based privacy mechanisms while our approach is model-agnostic. This versatility is crucial, given that marginal-based and workload-based mechanisms often yield higher quality synthetic tabular data, as evidenced by benchmark experiments in [TMH + 21]. Our experiments indicate that our method consistently improves the utility of synthetic data produced by all kinds of privacy mechanisms, even when the initial synthetic data are of high quality.\n\n## 2 Preliminaries and Problem Formulation\n\nIn this section, we review differential privacy and provide an overview of our problem setup.\n\n## 2.1 Differential Privacy\n\nWe first recall the definition of differential privacy (DP) [DR14].\n\nDefinition 1. $^{A randomized mechanism}$M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-differential privacy, if for any $^{adjacent datasets}$D $^{and}$D ', which only differ in one individual's record, and all possible outputs $^{from the mechanism}$O \u2286 R, we have\n\nDP has two important properties: post-processing immunity and composition rule. Specifically, if M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-DP and g : R \u2192 R ' is any randomized (or deterministic) function, then\n\nAnswer the following question: Extract all references from the document (such as HLM12, ABK + 21, LVW21, etc) and provide the count of each reference in a markdown table format."], "reference": ["| Reference | Count |\n| --------- | ----- |\n| HLM12\u00a0\u00a0| 1\u00a0\u00a0|\n| BLR13\u00a0\u00a0| 1\u00a0\u00a0|\n| GAH + 14\u00a0| 1\u00a0\u00a0|\n| CXZX15\u00a0\u00a0| 1\u00a0\u00a0|\n| BSG17\u00a0\u00a0| 1\u00a0\u00a0|\n| AZK + 19\u00a0| 1\u00a0\u00a0|\n| UV20\u00a0\u00a0\u00a0| 1\u00a0\u00a0|\n| GMHI20\u00a0\u00a0| 1\u00a0\u00a0|\n| TMH + 21\u00a0| 1\u00a0\u00a0|\n| VAA + 22\u00a0| 1\u00a0\u00a0|\n| BSV22\u00a0\u00a0| 1\u00a0\u00a0|\n| VTB + 20\u00a0| 2\u00a0\u00a0|\n| ABK + 21\u00a0| 2\u00a0\u00a0|\n| MMSM22\u00a0\u00a0| 2\u00a0\u00a0|\n| LVW21\u00a0\u00a0| 5\u00a0\u00a0|\n| LVS + 21\u00a0| 2\u00a0\u00a0|\n| NWD20\u00a0\u00a0| 1\u00a0\u00a0|\n| TMH + 21\u00a0| 1\u00a0\u00a0|\n| DR14\u00a0\u00a0\u00a0| 1\u00a0\u00a0|\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/markdown/qna.yaml", "question_id": "161705532503286099326719790973297265449", "category": "taxonomy", "turns": ["Given the context below:\n## 1.1 Related Work\n\nDP synthetic data generation mechanisms. Generating synthetic data using DP mechanisms is an active area of research [see e.g., HLM12, BLR13, GAH + 14, CXZX15, BSG17, AZK + 19, UV20, GMHI20, TMH + 21, VAA + 22, BSV22]. Along this line of work, the closest ones to ours are workload-aware methods [VTB + 20, ABK + 21, MMSM22, LVW21], which aim to ensure that the synthetic data perform well on a collection of queries. Our work differs in three key features. First, all existing approaches focus on generating private synthetic data from scratch, while we aim to post-process synthetic data to downweight samples that do not accurately represent the real data under the specified utility measures. As a result, our approach is more efficient as it does not need to fit a graphical model [MMSM22] or a neural network [LVW21] to represent the data distribution, and it is more extendable, allowing a single synthetic dataset to be quickly post-processed multiple times for different sets of utility measures as needed by downstream users. Second, some existing work may not scale well to large datasets as they either require solving an integer program multiple times [VTB + 20] or need to solve a large-scale optimization problem [ABK + 21]. In contrast, our approach is highly scalable, as it only requires solving a convex program whose number of variables is equal to the number of specified utility measures. Third, existing work often evaluates the quality of the synthetic data by how well it preserves key statistics (e.g., 3-way marginals) of the real data. In contrast, in our experiments, we evaluate our approach on the more stringent and realistic test of training various downstream ML models on the synthetic data and measuring their performance on real test data. The experimental results demonstrate that our approach can enhance the utility of synthetic data on selected measures without compromising their downstream quality.\n\nPublic data assisted methods/Post-processing methods. Our work introduces a post-processing procedure for improving the utility of a given synthetic dataset based on selected measures. As a special case, our approach can be applied to post-process publicly available datasets. In this regard, this work is related to public-data-assisted methods [see e.g., LVS + 21, LVW21], which leverage public data for saving privacy budgets. We extend Algorithm 1 in [LVS + 21] by formalizing the preservation of (noisy) utility measures as a constrained optimization problem, rather than minimizing the corresponding Lagrangian function. We further establish strong duality and propose a stochastic first-order method for solving this constrained optimization efficiently. We extend Algorithm 4 in [LVW21] by allowing any non-negative violation tolerance (i.e., any \u03b3 \u2265 0 in (3b)) compared with \u03b3 = 0 in [LVW21]. This extension offers increased flexibility, as users can now select various values of \u03b3 to navigate the trade-off between minimizing the distortion of the synthetic data and enhancing their utility on selected measures. Moreover, our experiments show that setting \u03b3 to be a small positive number (e.g., \u03b3 = 1 e-5) consistently outperforms when \u03b3 = 0. Finally, our work is related with [NWD20], which proposes to post-process outputs from differentially private GAN-based models for improving the quality of the generated synthetic data. However, their method is tailored to GAN-based privacy mechanisms while our approach is model-agnostic. This versatility is crucial, given that marginal-based and workload-based mechanisms often yield higher quality synthetic tabular data, as evidenced by benchmark experiments in [TMH + 21]. Our experiments indicate that our method consistently improves the utility of synthetic data produced by all kinds of privacy mechanisms, even when the initial synthetic data are of high quality.\n\n## 2 Preliminaries and Problem Formulation\n\nIn this section, we review differential privacy and provide an overview of our problem setup.\n\n## 2.1 Differential Privacy\n\nWe first recall the definition of differential privacy (DP) [DR14].\n\nDefinition 1. $^{A randomized mechanism}$M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-differential privacy, if for any $^{adjacent datasets}$D $^{and}$D ', which only differ in one individual's record, and all possible outputs $^{from the mechanism}$O \u2286 R, we have\n\nDP has two important properties: post-processing immunity and composition rule. Specifically, if M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-DP and g : R \u2192 R ' is any randomized (or deterministic) function, then\n\nAnswer the following question: Create a table of contents for this document. Table should be in markdown format.\n"], "reference": ["```\n| Section | Title |\n| ------- | ------------------------------------- |\n| 1.1 | Related Work |\n| 2 | Preliminaries and Problem Formulation |\n| 2.1 | Differential Privacy |\n```\n\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/markdown/qna.yaml", "question_id": "120068030579810821617095273348887198444", "category": "taxonomy", "turns": ["Given the context below:\ndata across multiple datasets and state-of-the-art privacy mechanisms. Additionally, it achieves this without degrading the performance of downstream models or statistical metrics. We provide additional experimental results and implementation details in Appendix C.\n\nBenchmark. We evaluate our algorithm on four benchmark datasets: Adult, Bank, Mushroom, and Shopping, which are from the UCI machine learning repository [DG17]. All the datasets have a target variable that can be used for a downstream prediction task. We use four existing DP mechanisms-AIM [MMSM22], MST [MMS21], DPCTGAN [RLP + 20], and PATECTGAN [RLP + 20]to generate private synthetic data. Among these DP mechanisms, AIM is a workload-based method; MST is a marginal-based method; DPCTGAN and PATECTGAN are GAN-based methods. All of their implementations are from the OpenDP library [Sma23]. To demonstrate the scalability of our approach, we conduct an additional experiment on the home-credit dataset [MOK18]. This dataset has 307,511 data points and 104 features. We apply GEM [LVW21] to this high-dimensional dataset for generating synthetic data. We pre-process each dataset to convert categorical columns into numerical features and standardize all features to the range [0 \u21aa 1].\n\nWe clarify that our objective is not to compare the quality of synthetic data generated by these DP mechanisms (for a benchmark evaluation, please refer to [TMH + 21]), but to demonstrate how our post-processing method can consistently improve the utility of synthetic data generated from these different DP mechanisms.\n\nSetup. We compare synthetic data produced by different privacy mechanisms with \u03f5 \u2208 \u00b6 2 \u21aa 4 \u2666 against the post-processed synthetic data that are generated by applying our post-processing technique with \u03f5$_{post}$ =1 to synthetic data generated from privacy mechanisms with \u03f5 \u2208 \u00b6 1 \u21aa 3 \u2666. For UCI datasets, we select 5 features, including the target variable, from the synthetic data that have the highest absolute correlation with the target variable. These features are chosen as they have a higher influence on downstream prediction tasks. The set of these features is denoted by S \u2286 [d]. Next, we define the utility measures as the first-order and second-order moment queries among the features in S : Q = \u00b6 q$_{i}$ (x) = x$_{i}$ \u2666 i \u2208 S \u222a \u00b6 q$_{i\u21aaj}$ (x) = x$_{i}$x$_{j}$ \u2666 i\u21aaj \u2208 S. For home-credit dataset, we follow the same setup but choosing 10 features.\n\nWe apply the Gaussian mechanism with (\u03f5$_{post}$ =1 \u21aa \u03b4$_{post}$ =1 \u25c1n $^{2}$) to estimate utility measures from the real data, where n denotes the number of real data points. Finally, we apply Algorithm 1 with \u03b3 = 1 e-5, a batch size of 256 for UCI datasets and 4096 for home-credit, and 200 epochs to compute the optimal resampling weights, which are then used to resample from the synthetic data with the same sample size. We repeat our experiment 5 times to obtain an error bar for each evaluation measure.\n\nEvaluation measure. We provide evaluation metrics for assessing the quality of synthetic data after post-processing. Our first metric, utility improvement, is defined as:\n\nThe range of this metric is (-\u221e \u21aa 1], where a value of 1 indicates a perfect alignment between the correlation matrix of the post-processed data and the real data.\n\nTo assess the downstream quality of synthetic data, we train predictive models using the synthetic data and then test their performance on real data. Specifically, we split the real data, using 80% for generating synthetic data and setting aside 20% to evaluate the performance of predictive models. We use the synthetic data to train a logistic regression classifier to predict the target variable based on other features. Then we apply this classifier to real data and calculate its F1 score. We use the function BinaryLogisticRegression from SDMetrics library [Dat23] to implement this process.\n\nWe compute two statistical measures to quantify the similarity between synthetic and real data distributions. The first measure is the average Jensen-Shannon distance between the marginal distributions of synthetic and real data, which ranges from 0 (identical distribution) to 1 (totally different distributions). The second measure is the average inverse KL-divergence between the marginal distributions of synthetic and real data, which ranges from 0 (totally different distributions) to 1 (identical distribution). These statistical measures are implemented by using synthcity library [QCvdS23].\n\nAnswer the following question: List the synthetic dataset generation methods referenced in the paper and the corresponding citation (if exists in the text). Please use Markdown format. \n"], "reference": ["| Method | Citation |\n|---|---|\n| AIM | [MMSM22] |\n| MST | [MMS21] |\n| DPCTGAN | [RLP + 20] |\n| PATECTGAN | [RLP + 20] |\n| GEM | [LVW21] |\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/markdown/qna.yaml", "question_id": "204124677646185513364602230207442542631", "category": "taxonomy", "turns": ["Given the context below:\ndata across multiple datasets and state-of-the-art privacy mechanisms. Additionally, it achieves this without degrading the performance of downstream models or statistical metrics. We provide additional experimental results and implementation details in Appendix C.\n\nBenchmark. We evaluate our algorithm on four benchmark datasets: Adult, Bank, Mushroom, and Shopping, which are from the UCI machine learning repository [DG17]. All the datasets have a target variable that can be used for a downstream prediction task. We use four existing DP mechanisms-AIM [MMSM22], MST [MMS21], DPCTGAN [RLP + 20], and PATECTGAN [RLP + 20]to generate private synthetic data. Among these DP mechanisms, AIM is a workload-based method; MST is a marginal-based method; DPCTGAN and PATECTGAN are GAN-based methods. All of their implementations are from the OpenDP library [Sma23]. To demonstrate the scalability of our approach, we conduct an additional experiment on the home-credit dataset [MOK18]. This dataset has 307,511 data points and 104 features. We apply GEM [LVW21] to this high-dimensional dataset for generating synthetic data. We pre-process each dataset to convert categorical columns into numerical features and standardize all features to the range [0 \u21aa 1].\n\nWe clarify that our objective is not to compare the quality of synthetic data generated by these DP mechanisms (for a benchmark evaluation, please refer to [TMH + 21]), but to demonstrate how our post-processing method can consistently improve the utility of synthetic data generated from these different DP mechanisms.\n\nSetup. We compare synthetic data produced by different privacy mechanisms with \u03f5 \u2208 \u00b6 2 \u21aa 4 \u2666 against the post-processed synthetic data that are generated by applying our post-processing technique with \u03f5$_{post}$ =1 to synthetic data generated from privacy mechanisms with \u03f5 \u2208 \u00b6 1 \u21aa 3 \u2666. For UCI datasets, we select 5 features, including the target variable, from the synthetic data that have the highest absolute correlation with the target variable. These features are chosen as they have a higher influence on downstream prediction tasks. The set of these features is denoted by S \u2286 [d]. Next, we define the utility measures as the first-order and second-order moment queries among the features in S : Q = \u00b6 q$_{i}$ (x) = x$_{i}$ \u2666 i \u2208 S \u222a \u00b6 q$_{i\u21aaj}$ (x) = x$_{i}$x$_{j}$ \u2666 i\u21aaj \u2208 S. For home-credit dataset, we follow the same setup but choosing 10 features.\n\nWe apply the Gaussian mechanism with (\u03f5$_{post}$ =1 \u21aa \u03b4$_{post}$ =1 \u25c1n $^{2}$) to estimate utility measures from the real data, where n denotes the number of real data points. Finally, we apply Algorithm 1 with \u03b3 = 1 e-5, a batch size of 256 for UCI datasets and 4096 for home-credit, and 200 epochs to compute the optimal resampling weights, which are then used to resample from the synthetic data with the same sample size. We repeat our experiment 5 times to obtain an error bar for each evaluation measure.\n\nEvaluation measure. We provide evaluation metrics for assessing the quality of synthetic data after post-processing. Our first metric, utility improvement, is defined as:\n\nThe range of this metric is (-\u221e \u21aa 1], where a value of 1 indicates a perfect alignment between the correlation matrix of the post-processed data and the real data.\n\nTo assess the downstream quality of synthetic data, we train predictive models using the synthetic data and then test their performance on real data. Specifically, we split the real data, using 80% for generating synthetic data and setting aside 20% to evaluate the performance of predictive models. We use the synthetic data to train a logistic regression classifier to predict the target variable based on other features. Then we apply this classifier to real data and calculate its F1 score. We use the function BinaryLogisticRegression from SDMetrics library [Dat23] to implement this process.\n\nWe compute two statistical measures to quantify the similarity between synthetic and real data distributions. The first measure is the average Jensen-Shannon distance between the marginal distributions of synthetic and real data, which ranges from 0 (identical distribution) to 1 (totally different distributions). The second measure is the average inverse KL-divergence between the marginal distributions of synthetic and real data, which ranges from 0 (totally different distributions) to 1 (identical distribution). These statistical measures are implemented by using synthcity library [QCvdS23].\n\nAnswer the following question: List the experimental parameter settings for running Algorithm 1. Display the result in Markdown format."], "reference": ["| Parameter | Value |\n|---|---|\n| \u03b3 | 1e-5 |\n| Batch size (UCI) | 256 |\n| Batch size (home-credit) | 4096 |\n| Epochs | 200 |\n| Experiment repetitions | 5 |\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/markdown/qna.yaml", "question_id": "246449486130822311667047153258363829407", "category": "taxonomy", "turns": ["Given the context below:\ndata across multiple datasets and state-of-the-art privacy mechanisms. Additionally, it achieves this without degrading the performance of downstream models or statistical metrics. We provide additional experimental results and implementation details in Appendix C.\n\nBenchmark. We evaluate our algorithm on four benchmark datasets: Adult, Bank, Mushroom, and Shopping, which are from the UCI machine learning repository [DG17]. All the datasets have a target variable that can be used for a downstream prediction task. We use four existing DP mechanisms-AIM [MMSM22], MST [MMS21], DPCTGAN [RLP + 20], and PATECTGAN [RLP + 20]to generate private synthetic data. Among these DP mechanisms, AIM is a workload-based method; MST is a marginal-based method; DPCTGAN and PATECTGAN are GAN-based methods. All of their implementations are from the OpenDP library [Sma23]. To demonstrate the scalability of our approach, we conduct an additional experiment on the home-credit dataset [MOK18]. This dataset has 307,511 data points and 104 features. We apply GEM [LVW21] to this high-dimensional dataset for generating synthetic data. We pre-process each dataset to convert categorical columns into numerical features and standardize all features to the range [0 \u21aa 1].\n\nWe clarify that our objective is not to compare the quality of synthetic data generated by these DP mechanisms (for a benchmark evaluation, please refer to [TMH + 21]), but to demonstrate how our post-processing method can consistently improve the utility of synthetic data generated from these different DP mechanisms.\n\nSetup. We compare synthetic data produced by different privacy mechanisms with \u03f5 \u2208 \u00b6 2 \u21aa 4 \u2666 against the post-processed synthetic data that are generated by applying our post-processing technique with \u03f5$_{post}$ =1 to synthetic data generated from privacy mechanisms with \u03f5 \u2208 \u00b6 1 \u21aa 3 \u2666. For UCI datasets, we select 5 features, including the target variable, from the synthetic data that have the highest absolute correlation with the target variable. These features are chosen as they have a higher influence on downstream prediction tasks. The set of these features is denoted by S \u2286 [d]. Next, we define the utility measures as the first-order and second-order moment queries among the features in S : Q = \u00b6 q$_{i}$ (x) = x$_{i}$ \u2666 i \u2208 S \u222a \u00b6 q$_{i\u21aaj}$ (x) = x$_{i}$x$_{j}$ \u2666 i\u21aaj \u2208 S. For home-credit dataset, we follow the same setup but choosing 10 features.\n\nWe apply the Gaussian mechanism with (\u03f5$_{post}$ =1 \u21aa \u03b4$_{post}$ =1 \u25c1n $^{2}$) to estimate utility measures from the real data, where n denotes the number of real data points. Finally, we apply Algorithm 1 with \u03b3 = 1 e-5, a batch size of 256 for UCI datasets and 4096 for home-credit, and 200 epochs to compute the optimal resampling weights, which are then used to resample from the synthetic data with the same sample size. We repeat our experiment 5 times to obtain an error bar for each evaluation measure.\n\nEvaluation measure. We provide evaluation metrics for assessing the quality of synthetic data after post-processing. Our first metric, utility improvement, is defined as:\n\nThe range of this metric is (-\u221e \u21aa 1], where a value of 1 indicates a perfect alignment between the correlation matrix of the post-processed data and the real data.\n\nTo assess the downstream quality of synthetic data, we train predictive models using the synthetic data and then test their performance on real data. Specifically, we split the real data, using 80% for generating synthetic data and setting aside 20% to evaluate the performance of predictive models. We use the synthetic data to train a logistic regression classifier to predict the target variable based on other features. Then we apply this classifier to real data and calculate its F1 score. We use the function BinaryLogisticRegression from SDMetrics library [Dat23] to implement this process.\n\nWe compute two statistical measures to quantify the similarity between synthetic and real data distributions. The first measure is the average Jensen-Shannon distance between the marginal distributions of synthetic and real data, which ranges from 0 (identical distribution) to 1 (totally different distributions). The second measure is the average inverse KL-divergence between the marginal distributions of synthetic and real data, which ranges from 0 (totally different distributions) to 1 (identical distribution). These statistical measures are implemented by using synthcity library [QCvdS23].\n\nAnswer the following question: List metrics used to measure the distance between synthetic data and real data. For each metric, list the name, description and range of value. Display the result in Markdown format.\n"], "reference": ["| Metric | Description | Range |\n|---|---|---|\n| Average Jensen-Shannon Distance | Measures similarity between marginal distributions | 0 (identical) to 1 (totally different) |\n| Average Inverse KL-Divergence | Measures similarity between marginal distributions | 0 (totally different) to 1 (identical) |\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/reasoning/qna.yaml", "question_id": "313615675720989086988003939047226315611", "category": "taxonomy", "turns": ["Given the context below:\n## 1.1 Related Work\n\nDP synthetic data generation mechanisms. Generating synthetic data using DP mechanisms is an active area of research [see e.g., HLM12, BLR13, GAH + 14, CXZX15, BSG17, AZK + 19, UV20, GMHI20, TMH + 21, VAA + 22, BSV22]. Along this line of work, the closest ones to ours are workload-aware methods [VTB + 20, ABK + 21, MMSM22, LVW21], which aim to ensure that the synthetic data perform well on a collection of queries. Our work differs in three key features. First, all existing approaches focus on generating private synthetic data from scratch, while we aim to post-process synthetic data to downweight samples that do not accurately represent the real data under the specified utility measures. As a result, our approach is more efficient as it does not need to fit a graphical model [MMSM22] or a neural network [LVW21] to represent the data distribution, and it is more extendable, allowing a single synthetic dataset to be quickly post-processed multiple times for different sets of utility measures as needed by downstream users. Second, some existing work may not scale well to large datasets as they either require solving an integer program multiple times [VTB + 20] or need to solve a large-scale optimization problem [ABK + 21]. In contrast, our approach is highly scalable, as it only requires solving a convex program whose number of variables is equal to the number of specified utility measures. Third, existing work often evaluates the quality of the synthetic data by how well it preserves key statistics (e.g., 3-way marginals) of the real data. In contrast, in our experiments, we evaluate our approach on the more stringent and realistic test of training various downstream ML models on the synthetic data and measuring their performance on real test data. The experimental results demonstrate that our approach can enhance the utility of synthetic data on selected measures without compromising their downstream quality.\n\nPublic data assisted methods/Post-processing methods. Our work introduces a post-processing procedure for improving the utility of a given synthetic dataset based on selected measures. As a special case, our approach can be applied to post-process publicly available datasets. In this regard, this work is related to public-data-assisted methods [see e.g., LVS + 21, LVW21], which leverage public data for saving privacy budgets. We extend Algorithm 1 in [LVS + 21] by formalizing the preservation of (noisy) utility measures as a constrained optimization problem, rather than minimizing the corresponding Lagrangian function. We further establish strong duality and propose a stochastic first-order method for solving this constrained optimization efficiently. We extend Algorithm 4 in [LVW21] by allowing any non-negative violation tolerance (i.e., any \u03b3 \u2265 0 in (3b)) compared with \u03b3 = 0 in [LVW21]. This extension offers increased flexibility, as users can now select various values of \u03b3 to navigate the trade-off between minimizing the distortion of the synthetic data and enhancing their utility on selected measures. Moreover, our experiments show that setting \u03b3 to be a small positive number (e.g., \u03b3 = 1 e-5) consistently outperforms when \u03b3 = 0. Finally, our work is related with [NWD20], which proposes to post-process outputs from differentially private GAN-based models for improving the quality of the generated synthetic data. However, their method is tailored to GAN-based privacy mechanisms while our approach is model-agnostic. This versatility is crucial, given that marginal-based and workload-based mechanisms often yield higher quality synthetic tabular data, as evidenced by benchmark experiments in [TMH + 21]. Our experiments indicate that our method consistently improves the utility of synthetic data produced by all kinds of privacy mechanisms, even when the initial synthetic data are of high quality.\n\n## 2 Preliminaries and Problem Formulation\n\nIn this section, we review differential privacy and provide an overview of our problem setup.\n\n## 2.1 Differential Privacy\n\nWe first recall the definition of differential privacy (DP) [DR14].\n\nDefinition 1. $^{A randomized mechanism}$M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-differential privacy, if for any $^{adjacent datasets}$D $^{and}$D ', which only differ in one individual's record, and all possible outputs $^{from the mechanism}$O \u2286 R, we have\n\nDP has two important properties: post-processing immunity and composition rule. Specifically, if M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-DP and g : R \u2192 R ' is any randomized (or deterministic) function, then\n\nAnswer the following question: What are the main advantages to the differentially private synthetic generation approach described in the paper?\n"], "reference": ["The approach described in the paper has three key advantages compared to related work in this space. First, the underlying generation mechanism is efficient and extensible as it leverages a single synthetic dataset that can be quickly post-processed for multiple different needs. Second, it is highly scalable and can work well on large datasets. Third, the quality of the generated synthetic data is evaluated on more stringent and realistic evaluation data than existing methods.\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/reasoning/qna.yaml", "question_id": "150743702925997915544717351663717742589", "category": "taxonomy", "turns": ["Given the context below:\n## 1.1 Related Work\n\nDP synthetic data generation mechanisms. Generating synthetic data using DP mechanisms is an active area of research [see e.g., HLM12, BLR13, GAH + 14, CXZX15, BSG17, AZK + 19, UV20, GMHI20, TMH + 21, VAA + 22, BSV22]. Along this line of work, the closest ones to ours are workload-aware methods [VTB + 20, ABK + 21, MMSM22, LVW21], which aim to ensure that the synthetic data perform well on a collection of queries. Our work differs in three key features. First, all existing approaches focus on generating private synthetic data from scratch, while we aim to post-process synthetic data to downweight samples that do not accurately represent the real data under the specified utility measures. As a result, our approach is more efficient as it does not need to fit a graphical model [MMSM22] or a neural network [LVW21] to represent the data distribution, and it is more extendable, allowing a single synthetic dataset to be quickly post-processed multiple times for different sets of utility measures as needed by downstream users. Second, some existing work may not scale well to large datasets as they either require solving an integer program multiple times [VTB + 20] or need to solve a large-scale optimization problem [ABK + 21]. In contrast, our approach is highly scalable, as it only requires solving a convex program whose number of variables is equal to the number of specified utility measures. Third, existing work often evaluates the quality of the synthetic data by how well it preserves key statistics (e.g., 3-way marginals) of the real data. In contrast, in our experiments, we evaluate our approach on the more stringent and realistic test of training various downstream ML models on the synthetic data and measuring their performance on real test data. The experimental results demonstrate that our approach can enhance the utility of synthetic data on selected measures without compromising their downstream quality.\n\nPublic data assisted methods/Post-processing methods. Our work introduces a post-processing procedure for improving the utility of a given synthetic dataset based on selected measures. As a special case, our approach can be applied to post-process publicly available datasets. In this regard, this work is related to public-data-assisted methods [see e.g., LVS + 21, LVW21], which leverage public data for saving privacy budgets. We extend Algorithm 1 in [LVS + 21] by formalizing the preservation of (noisy) utility measures as a constrained optimization problem, rather than minimizing the corresponding Lagrangian function. We further establish strong duality and propose a stochastic first-order method for solving this constrained optimization efficiently. We extend Algorithm 4 in [LVW21] by allowing any non-negative violation tolerance (i.e., any \u03b3 \u2265 0 in (3b)) compared with \u03b3 = 0 in [LVW21]. This extension offers increased flexibility, as users can now select various values of \u03b3 to navigate the trade-off between minimizing the distortion of the synthetic data and enhancing their utility on selected measures. Moreover, our experiments show that setting \u03b3 to be a small positive number (e.g., \u03b3 = 1 e-5) consistently outperforms when \u03b3 = 0. Finally, our work is related with [NWD20], which proposes to post-process outputs from differentially private GAN-based models for improving the quality of the generated synthetic data. However, their method is tailored to GAN-based privacy mechanisms while our approach is model-agnostic. This versatility is crucial, given that marginal-based and workload-based mechanisms often yield higher quality synthetic tabular data, as evidenced by benchmark experiments in [TMH + 21]. Our experiments indicate that our method consistently improves the utility of synthetic data produced by all kinds of privacy mechanisms, even when the initial synthetic data are of high quality.\n\n## 2 Preliminaries and Problem Formulation\n\nIn this section, we review differential privacy and provide an overview of our problem setup.\n\n## 2.1 Differential Privacy\n\nWe first recall the definition of differential privacy (DP) [DR14].\n\nDefinition 1. $^{A randomized mechanism}$M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-differential privacy, if for any $^{adjacent datasets}$D $^{and}$D ', which only differ in one individual's record, and all possible outputs $^{from the mechanism}$O \u2286 R, we have\n\nDP has two important properties: post-processing immunity and composition rule. Specifically, if M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-DP and g : R \u2192 R ' is any randomized (or deterministic) function, then\n\nAnswer the following question: Describe the the novelty of the technique in one sentence."], "reference": ["The approach described in this paper allows existing synthetic datasets to be optimized for different downtream use cases based on provided user measures.\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/reasoning/qna.yaml", "question_id": "15618686968943051841018945770687905617", "category": "taxonomy", "turns": ["Given the context below:\n## 1.1 Related Work\n\nDP synthetic data generation mechanisms. Generating synthetic data using DP mechanisms is an active area of research [see e.g., HLM12, BLR13, GAH + 14, CXZX15, BSG17, AZK + 19, UV20, GMHI20, TMH + 21, VAA + 22, BSV22]. Along this line of work, the closest ones to ours are workload-aware methods [VTB + 20, ABK + 21, MMSM22, LVW21], which aim to ensure that the synthetic data perform well on a collection of queries. Our work differs in three key features. First, all existing approaches focus on generating private synthetic data from scratch, while we aim to post-process synthetic data to downweight samples that do not accurately represent the real data under the specified utility measures. As a result, our approach is more efficient as it does not need to fit a graphical model [MMSM22] or a neural network [LVW21] to represent the data distribution, and it is more extendable, allowing a single synthetic dataset to be quickly post-processed multiple times for different sets of utility measures as needed by downstream users. Second, some existing work may not scale well to large datasets as they either require solving an integer program multiple times [VTB + 20] or need to solve a large-scale optimization problem [ABK + 21]. In contrast, our approach is highly scalable, as it only requires solving a convex program whose number of variables is equal to the number of specified utility measures. Third, existing work often evaluates the quality of the synthetic data by how well it preserves key statistics (e.g., 3-way marginals) of the real data. In contrast, in our experiments, we evaluate our approach on the more stringent and realistic test of training various downstream ML models on the synthetic data and measuring their performance on real test data. The experimental results demonstrate that our approach can enhance the utility of synthetic data on selected measures without compromising their downstream quality.\n\nPublic data assisted methods/Post-processing methods. Our work introduces a post-processing procedure for improving the utility of a given synthetic dataset based on selected measures. As a special case, our approach can be applied to post-process publicly available datasets. In this regard, this work is related to public-data-assisted methods [see e.g., LVS + 21, LVW21], which leverage public data for saving privacy budgets. We extend Algorithm 1 in [LVS + 21] by formalizing the preservation of (noisy) utility measures as a constrained optimization problem, rather than minimizing the corresponding Lagrangian function. We further establish strong duality and propose a stochastic first-order method for solving this constrained optimization efficiently. We extend Algorithm 4 in [LVW21] by allowing any non-negative violation tolerance (i.e., any \u03b3 \u2265 0 in (3b)) compared with \u03b3 = 0 in [LVW21]. This extension offers increased flexibility, as users can now select various values of \u03b3 to navigate the trade-off between minimizing the distortion of the synthetic data and enhancing their utility on selected measures. Moreover, our experiments show that setting \u03b3 to be a small positive number (e.g., \u03b3 = 1 e-5) consistently outperforms when \u03b3 = 0. Finally, our work is related with [NWD20], which proposes to post-process outputs from differentially private GAN-based models for improving the quality of the generated synthetic data. However, their method is tailored to GAN-based privacy mechanisms while our approach is model-agnostic. This versatility is crucial, given that marginal-based and workload-based mechanisms often yield higher quality synthetic tabular data, as evidenced by benchmark experiments in [TMH + 21]. Our experiments indicate that our method consistently improves the utility of synthetic data produced by all kinds of privacy mechanisms, even when the initial synthetic data are of high quality.\n\n## 2 Preliminaries and Problem Formulation\n\nIn this section, we review differential privacy and provide an overview of our problem setup.\n\n## 2.1 Differential Privacy\n\nWe first recall the definition of differential privacy (DP) [DR14].\n\nDefinition 1. $^{A randomized mechanism}$M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-differential privacy, if for any $^{adjacent datasets}$D $^{and}$D ', which only differ in one individual's record, and all possible outputs $^{from the mechanism}$O \u2286 R, we have\n\nDP has two important properties: post-processing immunity and composition rule. Specifically, if M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-DP and g : R \u2192 R ' is any randomized (or deterministic) function, then\n\nAnswer the following question: Describe the paper's approach in simple terms.\n"], "reference": ["This paper describes a technique to create a lot of data to support different use cases while making sure to preserve the anonymity of the individual represented in each data point.\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/csv/qna.yaml", "question_id": "154526838047430053121819025349513685537", "category": "taxonomy", "turns": ["Given the context below:\n## 1.1 Related Work\n\nDP synthetic data generation mechanisms. Generating synthetic data using DP mechanisms is an active area of research [see e.g., HLM12, BLR13, GAH + 14, CXZX15, BSG17, AZK + 19, UV20, GMHI20, TMH + 21, VAA + 22, BSV22]. Along this line of work, the closest ones to ours are workload-aware methods [VTB + 20, ABK + 21, MMSM22, LVW21], which aim to ensure that the synthetic data perform well on a collection of queries. Our work differs in three key features. First, all existing approaches focus on generating private synthetic data from scratch, while we aim to post-process synthetic data to downweight samples that do not accurately represent the real data under the specified utility measures. As a result, our approach is more efficient as it does not need to fit a graphical model [MMSM22] or a neural network [LVW21] to represent the data distribution, and it is more extendable, allowing a single synthetic dataset to be quickly post-processed multiple times for different sets of utility measures as needed by downstream users. Second, some existing work may not scale well to large datasets as they either require solving an integer program multiple times [VTB + 20] or need to solve a large-scale optimization problem [ABK + 21]. In contrast, our approach is highly scalable, as it only requires solving a convex program whose number of variables is equal to the number of specified utility measures. Third, existing work often evaluates the quality of the synthetic data by how well it preserves key statistics (e.g., 3-way marginals) of the real data. In contrast, in our experiments, we evaluate our approach on the more stringent and realistic test of training various downstream ML models on the synthetic data and measuring their performance on real test data. The experimental results demonstrate that our approach can enhance the utility of synthetic data on selected measures without compromising their downstream quality.\n\nPublic data assisted methods/Post-processing methods. Our work introduces a post-processing procedure for improving the utility of a given synthetic dataset based on selected measures. As a special case, our approach can be applied to post-process publicly available datasets. In this regard, this work is related to public-data-assisted methods [see e.g., LVS + 21, LVW21], which leverage public data for saving privacy budgets. We extend Algorithm 1 in [LVS + 21] by formalizing the preservation of (noisy) utility measures as a constrained optimization problem, rather than minimizing the corresponding Lagrangian function. We further establish strong duality and propose a stochastic first-order method for solving this constrained optimization efficiently. We extend Algorithm 4 in [LVW21] by allowing any non-negative violation tolerance (i.e., any \u03b3 \u2265 0 in (3b)) compared with \u03b3 = 0 in [LVW21]. This extension offers increased flexibility, as users can now select various values of \u03b3 to navigate the trade-off between minimizing the distortion of the synthetic data and enhancing their utility on selected measures. Moreover, our experiments show that setting \u03b3 to be a small positive number (e.g., \u03b3 = 1 e-5) consistently outperforms when \u03b3 = 0. Finally, our work is related with [NWD20], which proposes to post-process outputs from differentially private GAN-based models for improving the quality of the generated synthetic data. However, their method is tailored to GAN-based privacy mechanisms while our approach is model-agnostic. This versatility is crucial, given that marginal-based and workload-based mechanisms often yield higher quality synthetic tabular data, as evidenced by benchmark experiments in [TMH + 21]. Our experiments indicate that our method consistently improves the utility of synthetic data produced by all kinds of privacy mechanisms, even when the initial synthetic data are of high quality.\n\n## 2 Preliminaries and Problem Formulation\n\nIn this section, we review differential privacy and provide an overview of our problem setup.\n\n## 2.1 Differential Privacy\n\nWe first recall the definition of differential privacy (DP) [DR14].\n\nDefinition 1. $^{A randomized mechanism}$M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-differential privacy, if for any $^{adjacent datasets}$D $^{and}$D ', which only differ in one individual's record, and all possible outputs $^{from the mechanism}$O \u2286 R, we have\n\nDP has two important properties: post-processing immunity and composition rule. Specifically, if M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-DP and g : R \u2192 R ' is any randomized (or deterministic) function, then\n\nAnswer the following question: Extract the content of section 1.1 into a csv format. The paragraph titles should be the header names and the paragraph content should be a row entry.\n"], "reference": ["DP synthetic data generation mechanisms,Public data assisted methods/Post-processing methods\n\"Generating synthetic data using DP mechanisms is an active area of research [see e.g., HLM12, BLR13, GAH + 14, CXZX15, BSG17, AZK + 19, UV20, GMHI20, TMH + 21, VAA + 22, BSV22]. Along this line of work, the closest ones to ours are workload-aware methods [VTB + 20, ABK + 21, MMSM22, LVW21], which aim to ensure that the synthetic data perform well on a collection of queries. Our work differs in three key features. First, all existing approaches focus on generating private synthetic data from scratch, while we aim to post-process synthetic data to downweight samples that do not accurately represent the real data under the specified utility measures. As a result, our approach is more efficient as it does not need to fit a graphical model [MMSM22] or a neural network [LVW21] to represent the data distribution, and it is more extendable, allowing a single synthetic dataset to be quickly post-processed multiple times for different sets of utility measures as needed by downstream users. Second, some existing work may not scale well to large datasets as they either require solving an integer program multiple times [VTB + 20] or need to solve a large-scale optimization problem [ABK + 21]. In contrast, our approach is highly scalable, as it only requires solving a convex program whose number of variables is equal to the number of specified utility measures. Third, existing work often evaluates the quality of the synthetic data by how well it preserves key statistics (e.g., 3-way marginals) of the real data. In contrast, in our experiments, we evaluate our approach on the more stringent and realistic test of training various downstream ML models on the synthetic data and measuring their performance on real test data. The experimental results demonstrate that our approach can enhance the utility of synthetic data on selected measures without compromising their downstream quality.\",\"Our work introduces a post-processing procedure for improving the utility of a given synthetic dataset based on selected measures. As a special case, our approach can be applied to post-process publicly available datasets. In this regard, this work is related to public-data-assisted methods [see e.g., LVS + 21, LVW21], which leverage public data for saving privacy budgets. We extend Algorithm 1 in [LVS + 21] by formalizing the preservation of (noisy) utility measures as a constrained optimization problem, rather than minimizing the corresponding Lagrangian function. We further establish strong duality and propose a stochastic first-order method for solving this constrained optimization efficiently. We extend Algorithm 4 in [LVW21] by allowing any non-negative violation tolerance (i.e., any \u03b3 \u2265 0 in (3b)) compared with \u03b3 = 0 in [LVW21]. This extension offers increased flexibility, as users can now select various values of \u03b3 to navigate the trade-off between minimizing the distortion of the synthetic data and enhancing their utility on selected measures. Moreover, our experiments show that setting \u03b3 to be a small positive number (e.g., \u03b3 = 1 e-5) consistently outperforms when \u03b3 = 0. Finally, our work is related with [NWD20], which proposes to post-process outputs from differentially private GAN-based models for improving the quality of the generated synthetic data. However, their method is tailored to GAN-based privacy mechanisms while our approach is model-agnostic. This versatility is crucial, given that marginal-based and workload-based mechanisms often yield higher quality synthetic tabular data, as evidenced by benchmark experiments in [TMH + 21]. Our experiments indicate that our method consistently improves the utility of synthetic data produced by all kinds of privacy mechanisms, even when the initial synthetic data are of high quality.\"\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/csv/qna.yaml", "question_id": "166479164740018125591837301081795643678", "category": "taxonomy", "turns": ["Given the context below:\n## 1.1 Related Work\n\nDP synthetic data generation mechanisms. Generating synthetic data using DP mechanisms is an active area of research [see e.g., HLM12, BLR13, GAH + 14, CXZX15, BSG17, AZK + 19, UV20, GMHI20, TMH + 21, VAA + 22, BSV22]. Along this line of work, the closest ones to ours are workload-aware methods [VTB + 20, ABK + 21, MMSM22, LVW21], which aim to ensure that the synthetic data perform well on a collection of queries. Our work differs in three key features. First, all existing approaches focus on generating private synthetic data from scratch, while we aim to post-process synthetic data to downweight samples that do not accurately represent the real data under the specified utility measures. As a result, our approach is more efficient as it does not need to fit a graphical model [MMSM22] or a neural network [LVW21] to represent the data distribution, and it is more extendable, allowing a single synthetic dataset to be quickly post-processed multiple times for different sets of utility measures as needed by downstream users. Second, some existing work may not scale well to large datasets as they either require solving an integer program multiple times [VTB + 20] or need to solve a large-scale optimization problem [ABK + 21]. In contrast, our approach is highly scalable, as it only requires solving a convex program whose number of variables is equal to the number of specified utility measures. Third, existing work often evaluates the quality of the synthetic data by how well it preserves key statistics (e.g., 3-way marginals) of the real data. In contrast, in our experiments, we evaluate our approach on the more stringent and realistic test of training various downstream ML models on the synthetic data and measuring their performance on real test data. The experimental results demonstrate that our approach can enhance the utility of synthetic data on selected measures without compromising their downstream quality.\n\nPublic data assisted methods/Post-processing methods. Our work introduces a post-processing procedure for improving the utility of a given synthetic dataset based on selected measures. As a special case, our approach can be applied to post-process publicly available datasets. In this regard, this work is related to public-data-assisted methods [see e.g., LVS + 21, LVW21], which leverage public data for saving privacy budgets. We extend Algorithm 1 in [LVS + 21] by formalizing the preservation of (noisy) utility measures as a constrained optimization problem, rather than minimizing the corresponding Lagrangian function. We further establish strong duality and propose a stochastic first-order method for solving this constrained optimization efficiently. We extend Algorithm 4 in [LVW21] by allowing any non-negative violation tolerance (i.e., any \u03b3 \u2265 0 in (3b)) compared with \u03b3 = 0 in [LVW21]. This extension offers increased flexibility, as users can now select various values of \u03b3 to navigate the trade-off between minimizing the distortion of the synthetic data and enhancing their utility on selected measures. Moreover, our experiments show that setting \u03b3 to be a small positive number (e.g., \u03b3 = 1 e-5) consistently outperforms when \u03b3 = 0. Finally, our work is related with [NWD20], which proposes to post-process outputs from differentially private GAN-based models for improving the quality of the generated synthetic data. However, their method is tailored to GAN-based privacy mechanisms while our approach is model-agnostic. This versatility is crucial, given that marginal-based and workload-based mechanisms often yield higher quality synthetic tabular data, as evidenced by benchmark experiments in [TMH + 21]. Our experiments indicate that our method consistently improves the utility of synthetic data produced by all kinds of privacy mechanisms, even when the initial synthetic data are of high quality.\n\n## 2 Preliminaries and Problem Formulation\n\nIn this section, we review differential privacy and provide an overview of our problem setup.\n\n## 2.1 Differential Privacy\n\nWe first recall the definition of differential privacy (DP) [DR14].\n\nDefinition 1. $^{A randomized mechanism}$M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-differential privacy, if for any $^{adjacent datasets}$D $^{and}$D ', which only differ in one individual's record, and all possible outputs $^{from the mechanism}$O \u2286 R, we have\n\nDP has two important properties: post-processing immunity and composition rule. Specifically, if M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-DP and g : R \u2192 R ' is any randomized (or deterministic) function, then\n\nAnswer the following question: Extract all references from the document (such as HLM12, ABK + 21, LVW21, etc) and provide the count of each reference in a csv table format."], "reference": ["Reference,Count\nHLM12,1\nBLR13,1\nGAH + 14,1\nCXZX15,1\nBSG17,1\nAZK + 19,1\nUV20,1\nGMHI20,1\nTMH + 21,1\nVAA + 22,1\nBSV22,1\nVTB + 20,2\nABK + 21,2\nMMSM22,2\nLVW21,5\nLVS + 21,2\nNWD20,1\nTMH + 21,1\nDR14,1\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/csv/qna.yaml", "question_id": "98084228788201546249513621616736812041", "category": "taxonomy", "turns": ["Given the context below:\n## 1.1 Related Work\n\nDP synthetic data generation mechanisms. Generating synthetic data using DP mechanisms is an active area of research [see e.g., HLM12, BLR13, GAH + 14, CXZX15, BSG17, AZK + 19, UV20, GMHI20, TMH + 21, VAA + 22, BSV22]. Along this line of work, the closest ones to ours are workload-aware methods [VTB + 20, ABK + 21, MMSM22, LVW21], which aim to ensure that the synthetic data perform well on a collection of queries. Our work differs in three key features. First, all existing approaches focus on generating private synthetic data from scratch, while we aim to post-process synthetic data to downweight samples that do not accurately represent the real data under the specified utility measures. As a result, our approach is more efficient as it does not need to fit a graphical model [MMSM22] or a neural network [LVW21] to represent the data distribution, and it is more extendable, allowing a single synthetic dataset to be quickly post-processed multiple times for different sets of utility measures as needed by downstream users. Second, some existing work may not scale well to large datasets as they either require solving an integer program multiple times [VTB + 20] or need to solve a large-scale optimization problem [ABK + 21]. In contrast, our approach is highly scalable, as it only requires solving a convex program whose number of variables is equal to the number of specified utility measures. Third, existing work often evaluates the quality of the synthetic data by how well it preserves key statistics (e.g., 3-way marginals) of the real data. In contrast, in our experiments, we evaluate our approach on the more stringent and realistic test of training various downstream ML models on the synthetic data and measuring their performance on real test data. The experimental results demonstrate that our approach can enhance the utility of synthetic data on selected measures without compromising their downstream quality.\n\nPublic data assisted methods/Post-processing methods. Our work introduces a post-processing procedure for improving the utility of a given synthetic dataset based on selected measures. As a special case, our approach can be applied to post-process publicly available datasets. In this regard, this work is related to public-data-assisted methods [see e.g., LVS + 21, LVW21], which leverage public data for saving privacy budgets. We extend Algorithm 1 in [LVS + 21] by formalizing the preservation of (noisy) utility measures as a constrained optimization problem, rather than minimizing the corresponding Lagrangian function. We further establish strong duality and propose a stochastic first-order method for solving this constrained optimization efficiently. We extend Algorithm 4 in [LVW21] by allowing any non-negative violation tolerance (i.e., any \u03b3 \u2265 0 in (3b)) compared with \u03b3 = 0 in [LVW21]. This extension offers increased flexibility, as users can now select various values of \u03b3 to navigate the trade-off between minimizing the distortion of the synthetic data and enhancing their utility on selected measures. Moreover, our experiments show that setting \u03b3 to be a small positive number (e.g., \u03b3 = 1 e-5) consistently outperforms when \u03b3 = 0. Finally, our work is related with [NWD20], which proposes to post-process outputs from differentially private GAN-based models for improving the quality of the generated synthetic data. However, their method is tailored to GAN-based privacy mechanisms while our approach is model-agnostic. This versatility is crucial, given that marginal-based and workload-based mechanisms often yield higher quality synthetic tabular data, as evidenced by benchmark experiments in [TMH + 21]. Our experiments indicate that our method consistently improves the utility of synthetic data produced by all kinds of privacy mechanisms, even when the initial synthetic data are of high quality.\n\n## 2 Preliminaries and Problem Formulation\n\nIn this section, we review differential privacy and provide an overview of our problem setup.\n\n## 2.1 Differential Privacy\n\nWe first recall the definition of differential privacy (DP) [DR14].\n\nDefinition 1. $^{A randomized mechanism}$M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-differential privacy, if for any $^{adjacent datasets}$D $^{and}$D ', which only differ in one individual's record, and all possible outputs $^{from the mechanism}$O \u2286 R, we have\n\nDP has two important properties: post-processing immunity and composition rule. Specifically, if M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-DP and g : R \u2192 R ' is any randomized (or deterministic) function, then\n\nAnswer the following question: Extract all headers in a csv format.\n"], "reference": ["Header,\n1.1 Related Work,\n2 Preliminaries and Problem Formulation,\n2.1 Differential Privacy,\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/csv/qna.yaml", "question_id": "243305619819519148576364159422067625882", "category": "taxonomy", "turns": ["Given the context below:\ndata across multiple datasets and state-of-the-art privacy mechanisms. Additionally, it achieves this without degrading the performance of downstream models or statistical metrics. We provide additional experimental results and implementation details in Appendix C.\n\nBenchmark. We evaluate our algorithm on four benchmark datasets: Adult, Bank, Mushroom, and Shopping, which are from the UCI machine learning repository [DG17]. All the datasets have a target variable that can be used for a downstream prediction task. We use four existing DP mechanisms-AIM [MMSM22], MST [MMS21], DPCTGAN [RLP + 20], and PATECTGAN [RLP + 20]to generate private synthetic data. Among these DP mechanisms, AIM is a workload-based method; MST is a marginal-based method; DPCTGAN and PATECTGAN are GAN-based methods. All of their implementations are from the OpenDP library [Sma23]. To demonstrate the scalability of our approach, we conduct an additional experiment on the home-credit dataset [MOK18]. This dataset has 307,511 data points and 104 features. We apply GEM [LVW21] to this high-dimensional dataset for generating synthetic data. We pre-process each dataset to convert categorical columns into numerical features and standardize all features to the range [0 \u21aa 1].\n\nWe clarify that our objective is not to compare the quality of synthetic data generated by these DP mechanisms (for a benchmark evaluation, please refer to [TMH + 21]), but to demonstrate how our post-processing method can consistently improve the utility of synthetic data generated from these different DP mechanisms.\n\nSetup. We compare synthetic data produced by different privacy mechanisms with \u03f5 \u2208 \u00b6 2 \u21aa 4 \u2666 against the post-processed synthetic data that are generated by applying our post-processing technique with \u03f5$_{post}$ =1 to synthetic data generated from privacy mechanisms with \u03f5 \u2208 \u00b6 1 \u21aa 3 \u2666. For UCI datasets, we select 5 features, including the target variable, from the synthetic data that have the highest absolute correlation with the target variable. These features are chosen as they have a higher influence on downstream prediction tasks. The set of these features is denoted by S \u2286 [d]. Next, we define the utility measures as the first-order and second-order moment queries among the features in S : Q = \u00b6 q$_{i}$ (x) = x$_{i}$ \u2666 i \u2208 S \u222a \u00b6 q$_{i\u21aaj}$ (x) = x$_{i}$x$_{j}$ \u2666 i\u21aaj \u2208 S. For home-credit dataset, we follow the same setup but choosing 10 features.\n\nWe apply the Gaussian mechanism with (\u03f5$_{post}$ =1 \u21aa \u03b4$_{post}$ =1 \u25c1n $^{2}$) to estimate utility measures from the real data, where n denotes the number of real data points. Finally, we apply Algorithm 1 with \u03b3 = 1 e-5, a batch size of 256 for UCI datasets and 4096 for home-credit, and 200 epochs to compute the optimal resampling weights, which are then used to resample from the synthetic data with the same sample size. We repeat our experiment 5 times to obtain an error bar for each evaluation measure.\n\nEvaluation measure. We provide evaluation metrics for assessing the quality of synthetic data after post-processing. Our first metric, utility improvement, is defined as:\n\nThe range of this metric is (-\u221e \u21aa 1], where a value of 1 indicates a perfect alignment between the correlation matrix of the post-processed data and the real data.\n\nTo assess the downstream quality of synthetic data, we train predictive models using the synthetic data and then test their performance on real data. Specifically, we split the real data, using 80% for generating synthetic data and setting aside 20% to evaluate the performance of predictive models. We use the synthetic data to train a logistic regression classifier to predict the target variable based on other features. Then we apply this classifier to real data and calculate its F1 score. We use the function BinaryLogisticRegression from SDMetrics library [Dat23] to implement this process.\n\nWe compute two statistical measures to quantify the similarity between synthetic and real data distributions. The first measure is the average Jensen-Shannon distance between the marginal distributions of synthetic and real data, which ranges from 0 (identical distribution) to 1 (totally different distributions). The second measure is the average inverse KL-divergence between the marginal distributions of synthetic and real data, which ranges from 0 (totally different distributions) to 1 (identical distribution). These statistical measures are implemented by using synthcity library [QCvdS23].\n\nAnswer the following question: List the synthetic dataset generation methods referenced in the paper and the corresponding citation (if exists in the text). Separate the method and the citation with commas. \n"], "reference": ["Method,Citation\nAIM,[MMSM22]\nMST,[MMS21]\nDPCTGAN,[RLP + 20]\nPATECTGAN,[RLP + 20]\nGEM,[LVW21]\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/csv/qna.yaml", "question_id": "338374922544435862595759714620861353438", "category": "taxonomy", "turns": ["Given the context below:\ndata across multiple datasets and state-of-the-art privacy mechanisms. Additionally, it achieves this without degrading the performance of downstream models or statistical metrics. We provide additional experimental results and implementation details in Appendix C.\n\nBenchmark. We evaluate our algorithm on four benchmark datasets: Adult, Bank, Mushroom, and Shopping, which are from the UCI machine learning repository [DG17]. All the datasets have a target variable that can be used for a downstream prediction task. We use four existing DP mechanisms-AIM [MMSM22], MST [MMS21], DPCTGAN [RLP + 20], and PATECTGAN [RLP + 20]to generate private synthetic data. Among these DP mechanisms, AIM is a workload-based method; MST is a marginal-based method; DPCTGAN and PATECTGAN are GAN-based methods. All of their implementations are from the OpenDP library [Sma23]. To demonstrate the scalability of our approach, we conduct an additional experiment on the home-credit dataset [MOK18]. This dataset has 307,511 data points and 104 features. We apply GEM [LVW21] to this high-dimensional dataset for generating synthetic data. We pre-process each dataset to convert categorical columns into numerical features and standardize all features to the range [0 \u21aa 1].\n\nWe clarify that our objective is not to compare the quality of synthetic data generated by these DP mechanisms (for a benchmark evaluation, please refer to [TMH + 21]), but to demonstrate how our post-processing method can consistently improve the utility of synthetic data generated from these different DP mechanisms.\n\nSetup. We compare synthetic data produced by different privacy mechanisms with \u03f5 \u2208 \u00b6 2 \u21aa 4 \u2666 against the post-processed synthetic data that are generated by applying our post-processing technique with \u03f5$_{post}$ =1 to synthetic data generated from privacy mechanisms with \u03f5 \u2208 \u00b6 1 \u21aa 3 \u2666. For UCI datasets, we select 5 features, including the target variable, from the synthetic data that have the highest absolute correlation with the target variable. These features are chosen as they have a higher influence on downstream prediction tasks. The set of these features is denoted by S \u2286 [d]. Next, we define the utility measures as the first-order and second-order moment queries among the features in S : Q = \u00b6 q$_{i}$ (x) = x$_{i}$ \u2666 i \u2208 S \u222a \u00b6 q$_{i\u21aaj}$ (x) = x$_{i}$x$_{j}$ \u2666 i\u21aaj \u2208 S. For home-credit dataset, we follow the same setup but choosing 10 features.\n\nWe apply the Gaussian mechanism with (\u03f5$_{post}$ =1 \u21aa \u03b4$_{post}$ =1 \u25c1n $^{2}$) to estimate utility measures from the real data, where n denotes the number of real data points. Finally, we apply Algorithm 1 with \u03b3 = 1 e-5, a batch size of 256 for UCI datasets and 4096 for home-credit, and 200 epochs to compute the optimal resampling weights, which are then used to resample from the synthetic data with the same sample size. We repeat our experiment 5 times to obtain an error bar for each evaluation measure.\n\nEvaluation measure. We provide evaluation metrics for assessing the quality of synthetic data after post-processing. Our first metric, utility improvement, is defined as:\n\nThe range of this metric is (-\u221e \u21aa 1], where a value of 1 indicates a perfect alignment between the correlation matrix of the post-processed data and the real data.\n\nTo assess the downstream quality of synthetic data, we train predictive models using the synthetic data and then test their performance on real data. Specifically, we split the real data, using 80% for generating synthetic data and setting aside 20% to evaluate the performance of predictive models. We use the synthetic data to train a logistic regression classifier to predict the target variable based on other features. Then we apply this classifier to real data and calculate its F1 score. We use the function BinaryLogisticRegression from SDMetrics library [Dat23] to implement this process.\n\nWe compute two statistical measures to quantify the similarity between synthetic and real data distributions. The first measure is the average Jensen-Shannon distance between the marginal distributions of synthetic and real data, which ranges from 0 (identical distribution) to 1 (totally different distributions). The second measure is the average inverse KL-divergence between the marginal distributions of synthetic and real data, which ranges from 0 (totally different distributions) to 1 (identical distribution). These statistical measures are implemented by using synthcity library [QCvdS23].\n\nAnswer the following question: List the experimental parameter settings in CSV format for running Algorithm 1. "], "reference": ["Parameter,Value\n\u03b3,1e-5\nbatch\\_size (for UCI),256\nbatch\\_size (for home-credit),4096\nepochs,200\nexperiment runs, 5\n"]} +{"qna_file": "compositional_skills/extraction/technical_paper/equations/csv/qna.yaml", "question_id": "218133134328734882180240513545618804899", "category": "taxonomy", "turns": ["Given the context below:\ndata across multiple datasets and state-of-the-art privacy mechanisms. Additionally, it achieves this without degrading the performance of downstream models or statistical metrics. We provide additional experimental results and implementation details in Appendix C.\n\nBenchmark. We evaluate our algorithm on four benchmark datasets: Adult, Bank, Mushroom, and Shopping, which are from the UCI machine learning repository [DG17]. All the datasets have a target variable that can be used for a downstream prediction task. We use four existing DP mechanisms-AIM [MMSM22], MST [MMS21], DPCTGAN [RLP + 20], and PATECTGAN [RLP + 20]to generate private synthetic data. Among these DP mechanisms, AIM is a workload-based method; MST is a marginal-based method; DPCTGAN and PATECTGAN are GAN-based methods. All of their implementations are from the OpenDP library [Sma23]. To demonstrate the scalability of our approach, we conduct an additional experiment on the home-credit dataset [MOK18]. This dataset has 307,511 data points and 104 features. We apply GEM [LVW21] to this high-dimensional dataset for generating synthetic data. We pre-process each dataset to convert categorical columns into numerical features and standardize all features to the range [0 \u21aa 1].\n\nWe clarify that our objective is not to compare the quality of synthetic data generated by these DP mechanisms (for a benchmark evaluation, please refer to [TMH + 21]), but to demonstrate how our post-processing method can consistently improve the utility of synthetic data generated from these different DP mechanisms.\n\nSetup. We compare synthetic data produced by different privacy mechanisms with \u03f5 \u2208 \u00b6 2 \u21aa 4 \u2666 against the post-processed synthetic data that are generated by applying our post-processing technique with \u03f5$_{post}$ =1 to synthetic data generated from privacy mechanisms with \u03f5 \u2208 \u00b6 1 \u21aa 3 \u2666. For UCI datasets, we select 5 features, including the target variable, from the synthetic data that have the highest absolute correlation with the target variable. These features are chosen as they have a higher influence on downstream prediction tasks. The set of these features is denoted by S \u2286 [d]. Next, we define the utility measures as the first-order and second-order moment queries among the features in S : Q = \u00b6 q$_{i}$ (x) = x$_{i}$ \u2666 i \u2208 S \u222a \u00b6 q$_{i\u21aaj}$ (x) = x$_{i}$x$_{j}$ \u2666 i\u21aaj \u2208 S. For home-credit dataset, we follow the same setup but choosing 10 features.\n\nWe apply the Gaussian mechanism with (\u03f5$_{post}$ =1 \u21aa \u03b4$_{post}$ =1 \u25c1n $^{2}$) to estimate utility measures from the real data, where n denotes the number of real data points. Finally, we apply Algorithm 1 with \u03b3 = 1 e-5, a batch size of 256 for UCI datasets and 4096 for home-credit, and 200 epochs to compute the optimal resampling weights, which are then used to resample from the synthetic data with the same sample size. We repeat our experiment 5 times to obtain an error bar for each evaluation measure.\n\nEvaluation measure. We provide evaluation metrics for assessing the quality of synthetic data after post-processing. Our first metric, utility improvement, is defined as:\n\nThe range of this metric is (-\u221e \u21aa 1], where a value of 1 indicates a perfect alignment between the correlation matrix of the post-processed data and the real data.\n\nTo assess the downstream quality of synthetic data, we train predictive models using the synthetic data and then test their performance on real data. Specifically, we split the real data, using 80% for generating synthetic data and setting aside 20% to evaluate the performance of predictive models. We use the synthetic data to train a logistic regression classifier to predict the target variable based on other features. Then we apply this classifier to real data and calculate its F1 score. We use the function BinaryLogisticRegression from SDMetrics library [Dat23] to implement this process.\n\nWe compute two statistical measures to quantify the similarity between synthetic and real data distributions. The first measure is the average Jensen-Shannon distance between the marginal distributions of synthetic and real data, which ranges from 0 (identical distribution) to 1 (totally different distributions). The second measure is the average inverse KL-divergence between the marginal distributions of synthetic and real data, which ranges from 0 (totally different distributions) to 1 (identical distribution). These statistical measures are implemented by using synthcity library [QCvdS23].\n\nAnswer the following question: List metrics used to measure the distance between synthetic data and real data. For each metric, list the name, description and range of value. Display the result in CSV format. \n"], "reference": ["Metric,Description,Range\nAverage Jensen-Shannon Distance,Measures similarity between marginal distributions,0 (identical) to 1 (totally different)\nAverage Inverse KL-Divergence,Measures similarity between marginal distributions,0 (totally different) to 1 (identical)\n"]} +{"qna_file": "compositional_skills/extraction/receipt/bullet_points/qna.yaml", "question_id": "146670831529864678011836912794893908248", "category": "taxonomy", "turns": ["Given the context below:\n## Thanks for tipping, Luke\n\nHere's your updated receipt for bartaco (Seaport).\n\n## Total\n\n## $289.71\n\n| Subtotal\u00a0\u00a0\u00a0| $239.99\u00a0\u00a0|\n|---------------|-----------|\n| Service Fee\u00a0\u00a0| $14.12\u00a0\u00a0|\n| Tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $16.05\u00a0\u00a0|\n| Delivery Fee\u00a0| $0.49\u00a0\u00a0\u00a0|\n| Tip\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $27.06\u00a0\u00a0|\n| Special Offer | -$8.00\u00a0\u00a0|\n\n## Payments\n\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $262.65\u00a0\u00a0|\n|-----------------|-----------|\n| 2/23/24 2:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $27.06\u00a0\u00a0|\n| 2/23/24 3:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nAnswer the following question: Write a bulleted list with all items that cost more than $18.00.\n"], "reference": ["- baja fish\n- spiced chicken verde\n- chile-lime shrimp\n- pork belly\n- spiced chicken verde bowl\n"]} +{"qna_file": "compositional_skills/extraction/receipt/bullet_points/qna.yaml", "question_id": "213807923348757890610310231722493114673", "category": "taxonomy", "turns": ["Given the context below:\n## Thanks for tipping, Luke\n\nHere's your updated receipt for bartaco (Seaport).\n\n## Total\n\n## $289.71\n\n| Subtotal\u00a0\u00a0\u00a0| $239.99\u00a0\u00a0|\n|---------------|-----------|\n| Service Fee\u00a0\u00a0| $14.12\u00a0\u00a0|\n| Tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $16.05\u00a0\u00a0|\n| Delivery Fee\u00a0| $0.49\u00a0\u00a0\u00a0|\n| Tip\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $27.06\u00a0\u00a0|\n| Special Offer | -$8.00\u00a0\u00a0|\n\n## Payments\n\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $262.65\u00a0\u00a0|\n|-----------------|-----------|\n| 2/23/24 2:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $27.06\u00a0\u00a0|\n| 2/23/24 3:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nAnswer the following question: List all to-go items. Use a bulleted list format."], "reference": ["- jalapeno hot sauce \u2013 to-go\n- pineapple habanero hot sauce \u2013 to-go\n- roja hot sauce \u2013 to-go\n- jalapeno hot sauce \u2013 to-go\n"]} +{"qna_file": "compositional_skills/extraction/receipt/bullet_points/qna.yaml", "question_id": "16693036226111004778508616627348466836", "category": "taxonomy", "turns": ["Given the context below:\n## Thanks for tipping, Luke\n\nHere's your updated receipt for bartaco (Seaport).\n\n## Total\n\n## $289.71\n\n| Subtotal\u00a0\u00a0\u00a0| $239.99\u00a0\u00a0|\n|---------------|-----------|\n| Service Fee\u00a0\u00a0| $14.12\u00a0\u00a0|\n| Tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $16.05\u00a0\u00a0|\n| Delivery Fee\u00a0| $0.49\u00a0\u00a0\u00a0|\n| Tip\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $27.06\u00a0\u00a0|\n| Special Offer | -$8.00\u00a0\u00a0|\n\n## Payments\n\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $262.65\u00a0\u00a0|\n|-----------------|-----------|\n| 2/23/24 2:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $27.06\u00a0\u00a0|\n| 2/23/24 3:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nAnswer the following question: Find all items that have any type of fish and respond in a bulleted list format.\n"], "reference": ["- baja fish\n- ahi tuna bowl\n"]} +{"qna_file": "compositional_skills/extraction/receipt/plain_text/qna.yaml", "question_id": "251082885996068015526693324125404679824", "category": "taxonomy", "turns": ["Given the context below:\n## Thanks for tipping, Luke\n\nHere's your updated receipt for bartaco (Seaport).\n\n## Total\n\n## $289.71\n\n| Subtotal\u00a0\u00a0\u00a0| $239.99\u00a0\u00a0|\n|---------------|-----------|\n| Service Fee\u00a0\u00a0| $14.12\u00a0\u00a0|\n| Tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $16.05\u00a0\u00a0|\n| Delivery Fee\u00a0| $0.49\u00a0\u00a0\u00a0|\n| Tip\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $27.06\u00a0\u00a0|\n| Special Offer | -$8.00\u00a0\u00a0|\n\n## Payments\n\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $262.65\u00a0\u00a0|\n|-----------------|-----------|\n| 2/23/24 2:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $27.06\u00a0\u00a0|\n| 2/23/24 3:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nAnswer the following question: Tell me what did Rameswar order at bartaco on February 23, 2024?\n"], "reference": ["Rameswar ordered a spiced chicken bowl with shrimp as an extra protein added.\n"]} +{"qna_file": "compositional_skills/extraction/receipt/plain_text/qna.yaml", "question_id": "197247371467146327167951672118310282683", "category": "taxonomy", "turns": ["Given the context below:\n## Thanks for tipping, Luke\n\nHere's your updated receipt for bartaco (Seaport).\n\n## Total\n\n## $289.71\n\n| Subtotal\u00a0\u00a0\u00a0| $239.99\u00a0\u00a0|\n|---------------|-----------|\n| Service Fee\u00a0\u00a0| $14.12\u00a0\u00a0|\n| Tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $16.05\u00a0\u00a0|\n| Delivery Fee\u00a0| $0.49\u00a0\u00a0\u00a0|\n| Tip\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $27.06\u00a0\u00a0|\n| Special Offer | -$8.00\u00a0\u00a0|\n\n## Payments\n\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $262.65\u00a0\u00a0|\n|-----------------|-----------|\n| 2/23/24 2:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $27.06\u00a0\u00a0|\n| 2/23/24 3:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nAnswer the following question: Is there any salad ordered as a main dish in this receipt?"], "reference": ["Yes, there are two salads: a sm kale caesar and a large kale caesar. \n"]} +{"qna_file": "compositional_skills/extraction/receipt/plain_text/qna.yaml", "question_id": "55038013008238031424616763056054218586", "category": "taxonomy", "turns": ["Given the context below:\n## Thanks for tipping, Luke\n\nHere's your updated receipt for bartaco (Seaport).\n\n## Total\n\n## $289.71\n\n| Subtotal\u00a0\u00a0\u00a0| $239.99\u00a0\u00a0|\n|---------------|-----------|\n| Service Fee\u00a0\u00a0| $14.12\u00a0\u00a0|\n| Tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $16.05\u00a0\u00a0|\n| Delivery Fee\u00a0| $0.49\u00a0\u00a0\u00a0|\n| Tip\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $27.06\u00a0\u00a0|\n| Special Offer | -$8.00\u00a0\u00a0|\n\n## Payments\n\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $262.65\u00a0\u00a0|\n|-----------------|-----------|\n| 2/23/24 2:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $27.06\u00a0\u00a0|\n| 2/23/24 3:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nAnswer the following question: Who was listed in this lunch receipt? List all names in plain text.\n"], "reference": ["In this lunch receipt there are 11 people listed: Luke (You), Ruchir, David, Abhishek, Kate, Nathan, Kai, Aldo, Hui, Shiv, Rameswar.\n"]} +{"qna_file": "compositional_skills/extraction/receipt/markdown/qna.yaml", "question_id": "127993726025538639168251850653774443421", "category": "taxonomy", "turns": ["Given the context below:\n## Thanks for tipping, Luke\n\nHere's your updated receipt for bartaco (Seaport).\n\n## Total\n\n## $289.71\n\n| Subtotal\u00a0\u00a0\u00a0| $239.99\u00a0\u00a0|\n|---------------|-----------|\n| Service Fee\u00a0\u00a0| $14.12\u00a0\u00a0|\n| Tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $16.05\u00a0\u00a0|\n| Delivery Fee\u00a0| $0.49\u00a0\u00a0\u00a0|\n| Tip\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $27.06\u00a0\u00a0|\n| Special Offer | -$8.00\u00a0\u00a0|\n\n## Payments\n\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $262.65\u00a0\u00a0|\n|-----------------|-----------|\n| 2/23/24 2:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $27.06\u00a0\u00a0|\n| 2/23/24 3:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nAnswer the following question: List all items ordered by Luke in markdown format, specifying their add-ons if they have any cost associated. Include quantity and prices.\n"], "reference": ["|Item|Add-ons\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|Quantity|Price |\n|----|------------------------------------|--------|------|\n|baja fish|add bibb lettuce (4 pieces) $1.00\u00a0\u00a0|1\u00a0\u00a0\u00a0\u00a0|$21.29|\n|jalapeno hot sauce \u2013 to-go|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|5\u00a0\u00a0\u00a0\u00a0|$0.00 |\n|pineapple habanero hot sauce \u2013 to-go|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|10\u00a0\u00a0\u00a0|$0.00 |\n|roja hot sauce \u2013 to-go|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|10\u00a0\u00a0\u00a0|$0.00 |\n|jalapeno hot sauce \u2013 to-go|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|5\u00a0\u00a0\u00a0\u00a0|$0.00 |\n|sm kale caesar|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|1\u00a0\u00a0\u00a0\u00a0|$3.50 |\n|salsa roja + chips|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|1\u00a0\u00a0\u00a0\u00a0|$4.35 |\n|spiced chicken verde|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|1\u00a0\u00a0\u00a0\u00a0|$18.69|\n\n"]} +{"qna_file": "compositional_skills/extraction/receipt/markdown/qna.yaml", "question_id": "17908430897638040828533613086868331381", "category": "taxonomy", "turns": ["Given the context below:\n## Thanks for tipping, Luke\n\nHere's your updated receipt for bartaco (Seaport).\n\n## Total\n\n## $289.71\n\n| Subtotal\u00a0\u00a0\u00a0| $239.99\u00a0\u00a0|\n|---------------|-----------|\n| Service Fee\u00a0\u00a0| $14.12\u00a0\u00a0|\n| Tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $16.05\u00a0\u00a0|\n| Delivery Fee\u00a0| $0.49\u00a0\u00a0\u00a0|\n| Tip\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $27.06\u00a0\u00a0|\n| Special Offer | -$8.00\u00a0\u00a0|\n\n## Payments\n\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $262.65\u00a0\u00a0|\n|-----------------|-----------|\n| 2/23/24 2:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $27.06\u00a0\u00a0|\n| 2/23/24 3:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nAnswer the following question: Create a markdown table of the breakdown of the total cost of the receipt considering any fees, taxes and tips."], "reference": ["|Total|$289.71\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|-----|------------------------------------|\n|Subtotal|$239.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|Service Fee|$14.12\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|Tax\u00a0|$16.05\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|Delivery Fee|$0.49\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|Tip\u00a0|$27.06\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|Special Offer|-$8.00\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n"]} +{"qna_file": "compositional_skills/extraction/receipt/markdown/qna.yaml", "question_id": "184479824412146332897153271483321600452", "category": "taxonomy", "turns": ["Given the context below:\n## Thanks for tipping, Luke\n\nHere's your updated receipt for bartaco (Seaport).\n\n## Total\n\n## $289.71\n\n| Subtotal\u00a0\u00a0\u00a0| $239.99\u00a0\u00a0|\n|---------------|-----------|\n| Service Fee\u00a0\u00a0| $14.12\u00a0\u00a0|\n| Tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $16.05\u00a0\u00a0|\n| Delivery Fee\u00a0| $0.49\u00a0\u00a0\u00a0|\n| Tip\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $27.06\u00a0\u00a0|\n| Special Offer | -$8.00\u00a0\u00a0|\n\n## Payments\n\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $262.65\u00a0\u00a0|\n|-----------------|-----------|\n| 2/23/24 2:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $27.06\u00a0\u00a0|\n| 2/23/24 3:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nAnswer the following question: Extract all items of type bowl. Return the items and their prices in a markdown table with two columns: Bowl and Bowl price.\n"], "reference": ["|Bowl|Bowl price\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|----|------------------------------------|\n|mushroom bowl|$14.10\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|roasted duck bowl|$14.29\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|ahi tuna bowl|$13.75\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|spiced chicken verde bowl|$19.89\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\n"]} +{"qna_file": "compositional_skills/extraction/receipt/reasoning/qna.yaml", "question_id": "3179847060117764042081186166574333222", "category": "taxonomy", "turns": ["Given the context below:\n## Thanks for tipping, Luke\n\nHere's your updated receipt for bartaco (Seaport).\n\n## Total\n\n## $289.71\n\n| Subtotal\u00a0\u00a0\u00a0| $239.99\u00a0\u00a0|\n|---------------|-----------|\n| Service Fee\u00a0\u00a0| $14.12\u00a0\u00a0|\n| Tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $16.05\u00a0\u00a0|\n| Delivery Fee\u00a0| $0.49\u00a0\u00a0\u00a0|\n| Tip\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $27.06\u00a0\u00a0|\n| Special Offer | -$8.00\u00a0\u00a0|\n\n## Payments\n\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $262.65\u00a0\u00a0|\n|-----------------|-----------|\n| 2/23/24 2:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $27.06\u00a0\u00a0|\n| 2/23/24 3:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nAnswer the following question: By looking at the receipt, tell me who spent the most money at bartaco and how much it was.\n"], "reference": ["Luke (You) spent the most money, with a total of $47.83.\n"]} +{"qna_file": "compositional_skills/extraction/receipt/reasoning/qna.yaml", "question_id": "176007469896057207329061492279918446084", "category": "taxonomy", "turns": ["Given the context below:\n## Thanks for tipping, Luke\n\nHere's your updated receipt for bartaco (Seaport).\n\n## Total\n\n## $289.71\n\n| Subtotal\u00a0\u00a0\u00a0| $239.99\u00a0\u00a0|\n|---------------|-----------|\n| Service Fee\u00a0\u00a0| $14.12\u00a0\u00a0|\n| Tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $16.05\u00a0\u00a0|\n| Delivery Fee\u00a0| $0.49\u00a0\u00a0\u00a0|\n| Tip\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $27.06\u00a0\u00a0|\n| Special Offer | -$8.00\u00a0\u00a0|\n\n## Payments\n\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $262.65\u00a0\u00a0|\n|-----------------|-----------|\n| 2/23/24 2:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $27.06\u00a0\u00a0|\n| 2/23/24 3:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nAnswer the following question: Given the dishes consumed at bartaco, who is not a vegetarian?"], "reference": ["Given the dishes that contain any type of meat, we can conclude that Luke (You), David, Abhishek, Kate, Nathan, Kai, Aldo, Shiv, and Rameswar are not vegetarian. Ruchir and Hui are the only people who consumed vegetarian dishes.\n"]} +{"qna_file": "compositional_skills/extraction/receipt/reasoning/qna.yaml", "question_id": "188243911849133053297565566837728271290", "category": "taxonomy", "turns": ["Given the context below:\n## Thanks for tipping, Luke\n\nHere's your updated receipt for bartaco (Seaport).\n\n## Total\n\n## $289.71\n\n| Subtotal\u00a0\u00a0\u00a0| $239.99\u00a0\u00a0|\n|---------------|-----------|\n| Service Fee\u00a0\u00a0| $14.12\u00a0\u00a0|\n| Tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $16.05\u00a0\u00a0|\n| Delivery Fee\u00a0| $0.49\u00a0\u00a0\u00a0|\n| Tip\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $27.06\u00a0\u00a0|\n| Special Offer | -$8.00\u00a0\u00a0|\n\n## Payments\n\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $262.65\u00a0\u00a0|\n|-----------------|-----------|\n| 2/23/24 2:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $27.06\u00a0\u00a0|\n| 2/23/24 3:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nAnswer the following question: How much tip should I have given to pay a total below $280?\n"], "reference": ["To pay less than $280 in total you should tip 6.41%, considering that you are getting $8.00 of discount from a special offer.\n"]} +{"qna_file": "compositional_skills/extraction/receipt/csv/qna.yaml", "question_id": "303863723574265323416212703513558641790", "category": "taxonomy", "turns": ["Given the context below:\n## Thanks for tipping, Luke\n\nHere's your updated receipt for bartaco (Seaport).\n\n## Total\n\n## $289.71\n\n| Subtotal\u00a0\u00a0\u00a0| $239.99\u00a0\u00a0|\n|---------------|-----------|\n| Service Fee\u00a0\u00a0| $14.12\u00a0\u00a0|\n| Tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $16.05\u00a0\u00a0|\n| Delivery Fee\u00a0| $0.49\u00a0\u00a0\u00a0|\n| Tip\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $27.06\u00a0\u00a0|\n| Special Offer | -$8.00\u00a0\u00a0|\n\n## Payments\n\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $262.65\u00a0\u00a0|\n|-----------------|-----------|\n| 2/23/24 2:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $27.06\u00a0\u00a0|\n| 2/23/24 3:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nAnswer the following question: Create a CSV table of all items consumed and their prices, including quantity. Don't include any add-ons or customizations.\n"], "reference": ["Quantity,Item consumed,Price\n1,baja fish,$21.29\n5,jalapeno hot sauce \u2013 to-go,$0.00\n10,pineapple habanero hot sauce \u2013 to-go,$0.00\n10,roja hot sauce \u2013 to-go,$0.00\n5,jalapeno hot sauce \u2013 to-go,$0.00\n1,sm kale caesar,$3.50\n1,salsa roja + chips,$4.35\n1,spiced chicken verde,$18.69\n1,mushroom bowl,$14.10\n1,seared chorizo,$15.49\n1,diet coke,$2.75\n1,baja fish,$20.29\n1,roasted duck bowl,$14.29\n1,ahi tuna bowl,$13.75\n1,chile-lime shrimp,$20.29\n1,guacamole + chips,$12.59\n1,pork belly,$20.29\n1,plantains,$6.19\n1,lg kale caesar,$7.00\n1,tajin pineapple,$3.95\n1,chile-lime shrimp,$21.29\n1,spiced chicken verde bowl,$19.89\n"]} +{"qna_file": "compositional_skills/extraction/receipt/csv/qna.yaml", "question_id": "16870015487432810538810674987335310258", "category": "taxonomy", "turns": ["Given the context below:\n## Thanks for tipping, Luke\n\nHere's your updated receipt for bartaco (Seaport).\n\n## Total\n\n## $289.71\n\n| Subtotal\u00a0\u00a0\u00a0| $239.99\u00a0\u00a0|\n|---------------|-----------|\n| Service Fee\u00a0\u00a0| $14.12\u00a0\u00a0|\n| Tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $16.05\u00a0\u00a0|\n| Delivery Fee\u00a0| $0.49\u00a0\u00a0\u00a0|\n| Tip\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $27.06\u00a0\u00a0|\n| Special Offer | -$8.00\u00a0\u00a0|\n\n## Payments\n\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $262.65\u00a0\u00a0|\n|-----------------|-----------|\n| 2/23/24 2:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $27.06\u00a0\u00a0|\n| 2/23/24 3:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nAnswer the following question: Give me a .csv table of payments made to the restaurant by each credit card."], "reference": ["Credit card,Date,Payment\nVisa \u00b7\u00b7\u00b7\u00b76451,2/23/24 2:06 PM,$262.65\nVisa \u00b7\u00b7\u00b7\u00b76451,2/23/24 3:06 PM,$27.06\n"]} +{"qna_file": "compositional_skills/extraction/receipt/csv/qna.yaml", "question_id": "321786636095719006578973927679375108326", "category": "taxonomy", "turns": ["Given the context below:\n## Thanks for tipping, Luke\n\nHere's your updated receipt for bartaco (Seaport).\n\n## Total\n\n## $289.71\n\n| Subtotal\u00a0\u00a0\u00a0| $239.99\u00a0\u00a0|\n|---------------|-----------|\n| Service Fee\u00a0\u00a0| $14.12\u00a0\u00a0|\n| Tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $16.05\u00a0\u00a0|\n| Delivery Fee\u00a0| $0.49\u00a0\u00a0\u00a0|\n| Tip\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| $27.06\u00a0\u00a0|\n| Special Offer | -$8.00\u00a0\u00a0|\n\n## Payments\n\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $262.65\u00a0\u00a0|\n|-----------------|-----------|\n| 2/23/24 2:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Visa \u00b7\u00b7\u00b7\u00b76451\u00a0\u00a0| $27.06\u00a0\u00a0|\n| 2/23/24 3:06 PM |\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nAnswer the following question: Create a table that shows the percentage of payment made by each credit card given the total of the receipt.\n"], "reference": ["Credit card,Percentage of payment\nVisa \u00b7\u00b7\u00b7\u00b76451,90.66%\nVisa \u00b7\u00b7\u00b7\u00b76451,9.34%\n"]} +{"qna_file": "compositional_skills/extraction/services_agreement/bullet_points/qna.yaml", "question_id": "154688182802521184164247977333699402279", "category": "taxonomy", "turns": ["Given the context below:\n8. Indemnification and Insurance. Consultant shall indemnify and hold harmless the County from and against all claims, damages, losses, and expenses arising out of or resulting from acts or omissions of the Consultant, Consultant's sub-Consultants or otherwise arising out of the performance of services by Consultant. No later than seven days after execution of this Agreement, Consultant shall provide the County with certificates of insurance evidencing the types and amounts of insurance specified below:\n\nA. Standard Workers' Compensation as required by law in the State of Colorado; and\n\nB. Comprehensive General Liability Insurance for operations and contractual liability adequate to cover the liability assumed hereunder with limits of not less than $350,000 on account of any one person and $990,000 for each occurrence of property damage and personal injury; and\n\nC. Automobile Liability insurance in those instances where Consultant uses an automobile, regardless of ownership, for the performance of the Services. Consultant shall carry insurance, written on the comprehensive automobile form insuring all owned and nonowned automobiles with limits of not less than $100,000 (bodily injury per person), $300,000 (each accident) and $50,000 (property damage).\n\nInsurance coverage shall not be reduced below the limits described above or cancelled without County's written approval of such reduction or cancellation. Reduction, cancellation or termination of insurance coverage, or failure to obtain insurance coverage, without the County's written approval shall constitute a material breach of the Agreement and shall automatically terminate the Agreement. Consultant shall require that any of its agents or sub-consultants who enter upon the County's premises shall maintain like insurance. Certificates of such insurance, of agents and sub-consultants, shall be provided to the County upon request. With regard to all insurance, such insurance shall:\n\nA. Be primary insurance to the full limits of liability herein before stated and, should County have other valid insurance, County insurance shall be excess insurance only; and B. Not cancelled without thirty (30) days prior written notice to the County.\n\nAnswer the following question: I am a consultant and I have the following types of insurance:\n- Comprehensive General Liability Insurance with limits of $250,000 on account of one person and $990,000 for each occurrence of property damage and personal injury\n- Automobile Liability insurance for all the automobiles I\u2019ll on this job with limits of $90,000 for bodily injury per person, $301,000 for each accident, and $51,000 property damage.\nWhat do I need to do in order to be compliant with the services agreement? Write the answer as a bulleted list.\u00a0\n"], "reference": ["- Acquire Standard Workers\u2019 Compensation as required by law in the State of Colorado\n- Increase the limit of your Comprehensive General Liability insurance by $100,000 on account of any one person for a total limit of $350,000 \n- Increase the limit of your Automobile Liability insurance by $10,000 for bodily injury per person for a total limit of $100,000 \n"]} +{"qna_file": "compositional_skills/extraction/services_agreement/bullet_points/qna.yaml", "question_id": "174367305104761535000646593522630524896", "category": "taxonomy", "turns": ["Given the context below:\n8. Indemnification and Insurance. Consultant shall indemnify and hold harmless the County from and against all claims, damages, losses, and expenses arising out of or resulting from acts or omissions of the Consultant, Consultant's sub-Consultants or otherwise arising out of the performance of services by Consultant. No later than seven days after execution of this Agreement, Consultant shall provide the County with certificates of insurance evidencing the types and amounts of insurance specified below:\n\nA. Standard Workers' Compensation as required by law in the State of Colorado; and\n\nB. Comprehensive General Liability Insurance for operations and contractual liability adequate to cover the liability assumed hereunder with limits of not less than $350,000 on account of any one person and $990,000 for each occurrence of property damage and personal injury; and\n\nC. Automobile Liability insurance in those instances where Consultant uses an automobile, regardless of ownership, for the performance of the Services. Consultant shall carry insurance, written on the comprehensive automobile form insuring all owned and nonowned automobiles with limits of not less than $100,000 (bodily injury per person), $300,000 (each accident) and $50,000 (property damage).\n\nInsurance coverage shall not be reduced below the limits described above or cancelled without County's written approval of such reduction or cancellation. Reduction, cancellation or termination of insurance coverage, or failure to obtain insurance coverage, without the County's written approval shall constitute a material breach of the Agreement and shall automatically terminate the Agreement. Consultant shall require that any of its agents or sub-consultants who enter upon the County's premises shall maintain like insurance. Certificates of such insurance, of agents and sub-consultants, shall be provided to the County upon request. With regard to all insurance, such insurance shall:\n\nA. Be primary insurance to the full limits of liability herein before stated and, should County have other valid insurance, County insurance shall be excess insurance only; and B. Not cancelled without thirty (30) days prior written notice to the County.\n\nAnswer the following question: Make a bulleted list of all the types of insurance with limits over $300,001 required by the service agreement. "], "reference": ["- Comprehensive General Liability insurance for operations and contractual liability on account of any one person\n- Comprehensive General Liability insurance for operations and contractual liability for each occurrence of property damage and personal injury\n"]} +{"qna_file": "compositional_skills/extraction/services_agreement/bullet_points/qna.yaml", "question_id": "222834133498540484856778006223575154770", "category": "taxonomy", "turns": ["Given the context below:\n8. Indemnification and Insurance. Consultant shall indemnify and hold harmless the County from and against all claims, damages, losses, and expenses arising out of or resulting from acts or omissions of the Consultant, Consultant's sub-Consultants or otherwise arising out of the performance of services by Consultant. No later than seven days after execution of this Agreement, Consultant shall provide the County with certificates of insurance evidencing the types and amounts of insurance specified below:\n\nA. Standard Workers' Compensation as required by law in the State of Colorado; and\n\nB. Comprehensive General Liability Insurance for operations and contractual liability adequate to cover the liability assumed hereunder with limits of not less than $350,000 on account of any one person and $990,000 for each occurrence of property damage and personal injury; and\n\nC. Automobile Liability insurance in those instances where Consultant uses an automobile, regardless of ownership, for the performance of the Services. Consultant shall carry insurance, written on the comprehensive automobile form insuring all owned and nonowned automobiles with limits of not less than $100,000 (bodily injury per person), $300,000 (each accident) and $50,000 (property damage).\n\nInsurance coverage shall not be reduced below the limits described above or cancelled without County's written approval of such reduction or cancellation. Reduction, cancellation or termination of insurance coverage, or failure to obtain insurance coverage, without the County's written approval shall constitute a material breach of the Agreement and shall automatically terminate the Agreement. Consultant shall require that any of its agents or sub-consultants who enter upon the County's premises shall maintain like insurance. Certificates of such insurance, of agents and sub-consultants, shall be provided to the County upon request. With regard to all insurance, such insurance shall:\n\nA. Be primary insurance to the full limits of liability herein before stated and, should County have other valid insurance, County insurance shall be excess insurance only; and B. Not cancelled without thirty (30) days prior written notice to the County.\n\nAnswer the following question: Write me a bulleted list of all the people whose acts or omissions are covered by the indemnification and insurance clause in the services agreement.\u00a0\n"], "reference": ["- The Consultant\n- The Consultant\u2019s sub-Consultants\n\n"]} +{"qna_file": "compositional_skills/extraction/services_agreement/plain_text/qna.yaml", "question_id": "141440791488522188832372319900273101943", "category": "taxonomy", "turns": ["Given the context below:\n8. Indemnification and Insurance. Consultant shall indemnify and hold harmless the County from and against all claims, damages, losses, and expenses arising out of or resulting from acts or omissions of the Consultant, Consultant's sub-Consultants or otherwise arising out of the performance of services by Consultant. No later than seven days after execution of this Agreement, Consultant shall provide the County with certificates of insurance evidencing the types and amounts of insurance specified below:\n\nA. Standard Workers' Compensation as required by law in the State of Colorado; and\n\nB. Comprehensive General Liability Insurance for operations and contractual liability adequate to cover the liability assumed hereunder with limits of not less than $350,000 on account of any one person and $990,000 for each occurrence of property damage and personal injury; and\n\nC. Automobile Liability insurance in those instances where Consultant uses an automobile, regardless of ownership, for the performance of the Services. Consultant shall carry insurance, written on the comprehensive automobile form insuring all owned and nonowned automobiles with limits of not less than $100,000 (bodily injury per person), $300,000 (each accident) and $50,000 (property damage).\n\nInsurance coverage shall not be reduced below the limits described above or cancelled without County's written approval of such reduction or cancellation. Reduction, cancellation or termination of insurance coverage, or failure to obtain insurance coverage, without the County's written approval shall constitute a material breach of the Agreement and shall automatically terminate the Agreement. Consultant shall require that any of its agents or sub-consultants who enter upon the County's premises shall maintain like insurance. Certificates of such insurance, of agents and sub-consultants, shall be provided to the County upon request. With regard to all insurance, such insurance shall:\n\nA. Be primary insurance to the full limits of liability herein before stated and, should County have other valid insurance, County insurance shall be excess insurance only; and B. Not cancelled without thirty (30) days prior written notice to the County.\n\nAnswer the following question: I am a consultant, and I have no insurance, but I am going to start working with the County in two months. Write a list of the minimum limits of coverage I need to acquire in order to be in compliance with the services agreement.\u00a0\n"], "reference": ["You will need to acquire Standard Workers\u2019 Compensation as required by law in the State of Colorado, Comprehensive General Liability insurance with limits of not less than $350,000 on account of any one person and $990,000 for each occurrence of property damage and personal injury, and Automobile Liability insurance with limits of not less than $100,000 (bodily injury per person), $300,000 (each accident) and $50,000 (property damage).\u00a0\n"]} +{"qna_file": "compositional_skills/extraction/services_agreement/plain_text/qna.yaml", "question_id": "320490105930292965754595532454863115553", "category": "taxonomy", "turns": ["Given the context below:\n8. Indemnification and Insurance. Consultant shall indemnify and hold harmless the County from and against all claims, damages, losses, and expenses arising out of or resulting from acts or omissions of the Consultant, Consultant's sub-Consultants or otherwise arising out of the performance of services by Consultant. No later than seven days after execution of this Agreement, Consultant shall provide the County with certificates of insurance evidencing the types and amounts of insurance specified below:\n\nA. Standard Workers' Compensation as required by law in the State of Colorado; and\n\nB. Comprehensive General Liability Insurance for operations and contractual liability adequate to cover the liability assumed hereunder with limits of not less than $350,000 on account of any one person and $990,000 for each occurrence of property damage and personal injury; and\n\nC. Automobile Liability insurance in those instances where Consultant uses an automobile, regardless of ownership, for the performance of the Services. Consultant shall carry insurance, written on the comprehensive automobile form insuring all owned and nonowned automobiles with limits of not less than $100,000 (bodily injury per person), $300,000 (each accident) and $50,000 (property damage).\n\nInsurance coverage shall not be reduced below the limits described above or cancelled without County's written approval of such reduction or cancellation. Reduction, cancellation or termination of insurance coverage, or failure to obtain insurance coverage, without the County's written approval shall constitute a material breach of the Agreement and shall automatically terminate the Agreement. Consultant shall require that any of its agents or sub-consultants who enter upon the County's premises shall maintain like insurance. Certificates of such insurance, of agents and sub-consultants, shall be provided to the County upon request. With regard to all insurance, such insurance shall:\n\nA. Be primary insurance to the full limits of liability herein before stated and, should County have other valid insurance, County insurance shall be excess insurance only; and B. Not cancelled without thirty (30) days prior written notice to the County.\n\nAnswer the following question: I am a Consultant working for the County. The County has asked me for certificates of insurance. Write a list of the types of certificates of insurance I need to provide them."], "reference": ["You need to provide to the County Certificates of Standard Workers\u2019 Compensation, Comprehensive General Liability Insurance, and Automobile Liability insurance for yourself, agents, and sub-consultants.\n"]} +{"qna_file": "compositional_skills/extraction/services_agreement/plain_text/qna.yaml", "question_id": "310672552932622910352541575291661256095", "category": "taxonomy", "turns": ["Given the context below:\n8. Indemnification and Insurance. Consultant shall indemnify and hold harmless the County from and against all claims, damages, losses, and expenses arising out of or resulting from acts or omissions of the Consultant, Consultant's sub-Consultants or otherwise arising out of the performance of services by Consultant. No later than seven days after execution of this Agreement, Consultant shall provide the County with certificates of insurance evidencing the types and amounts of insurance specified below:\n\nA. Standard Workers' Compensation as required by law in the State of Colorado; and\n\nB. Comprehensive General Liability Insurance for operations and contractual liability adequate to cover the liability assumed hereunder with limits of not less than $350,000 on account of any one person and $990,000 for each occurrence of property damage and personal injury; and\n\nC. Automobile Liability insurance in those instances where Consultant uses an automobile, regardless of ownership, for the performance of the Services. Consultant shall carry insurance, written on the comprehensive automobile form insuring all owned and nonowned automobiles with limits of not less than $100,000 (bodily injury per person), $300,000 (each accident) and $50,000 (property damage).\n\nInsurance coverage shall not be reduced below the limits described above or cancelled without County's written approval of such reduction or cancellation. Reduction, cancellation or termination of insurance coverage, or failure to obtain insurance coverage, without the County's written approval shall constitute a material breach of the Agreement and shall automatically terminate the Agreement. Consultant shall require that any of its agents or sub-consultants who enter upon the County's premises shall maintain like insurance. Certificates of such insurance, of agents and sub-consultants, shall be provided to the County upon request. With regard to all insurance, such insurance shall:\n\nA. Be primary insurance to the full limits of liability herein before stated and, should County have other valid insurance, County insurance shall be excess insurance only; and B. Not cancelled without thirty (30) days prior written notice to the County.\n\nAnswer the following question: Write a list of the minimum Automobile Liability insurance limits I need to obtain as a Consultant for the County in order to be compliant with the services agreement.\n"], "reference": ["The minimum limits for Automobile Liability insurance you need to obtain are not less than $100,000 (bodily injury per person), $300,000 (each accident), and $50,000 (property damage).\n"]} +{"qna_file": "compositional_skills/extraction/services_agreement/plain_text/qna.yaml", "question_id": "203879606616136344560013924051492753181", "category": "taxonomy", "turns": ["Given the context below:\nrequirements. Upon request, Consultant shall provide documentation of its expenses. Payment of such invoices will be due within twenty-five (25) days of the receipt thereof.\n\nB. Fund Availability/Annual Appropriation. Payment pursuant to this Agreement, whether in whole or in part, is subject to and contingent upon the continuing availability of County funds for purposes hereof. In the event that said funds, or any part thereof, become unavailable as determined by the County, the County may immediately terminate this Agreement or amend it accordingly.\n\nC. Multi-Year Contracts. The obligations of the County hereunder shall not constitute a general obligation indebtedness or multiple year direct or indirect debt or other fin ancial obligation whatsoever within the meaning of the Constitution or laws of the State of Colorado.\n\n3. Term. The term of this Agreement shall be from ____________ to _____________. The Agreement may be terminated earlier by final completion of the Services by the Consultant and acceptance of the services by the County or through the termination provisions provided herein.\n\n4. Termination. Consultant may terminate this Agreement at any time by giving the County written notice of not less than sixty (60) days. County may terminate this Agreement at any time in the event that Consultant violates the terms of this Agreement or fails to produce a result that meets the specifications of this Agreement. In the event of termination, payments will be made to Consultant for all work performed up to the date of termination.\n\n5. Relationship. The parties understand and agree that Consultant is an independent Consultant and that Consultant is not an employee, agent or servant of the County, nor is Consultant entitled to County employment benefits. CONSULTANT UNDERSTANDS AND AGREES THAT CONSULTANT IS NOT ENTITLED TO WORKERS' COMPENSATION BENEFITS AND THAT CONSULTANT IS OBLIGATED TO PAY FEDERAL AND STATE INCOME TAX ON ANY MONEYS EARNED PURSUANT TO THIS CONTRACT. As an independent Consultant, Consultants agrees that:\n\nA. Consultant does not have the authority to act for the County, or to bind the County in any respect whatsoever, or to incur any debts or liabilities in the name of or on behalf of the County; and\n\nB. Consultant has and hereby retains control of and supervision over the performance of Consultant's obligations hereunder and control over any persons employed or contracted by Consultant for performing the Services hereunder; and\n\nC. County will not provide training or instruction to Consultant or any of its employees regarding the performance of Services hereunder; and\n\nD. Neither Consultant, nor its employees or Consultants, will receive benefits of any kind from the County. Consultant represents that it is engaged in providing similar services to the general public and not required to work exclusively for the County; and\n\nE. All Services are to be performed solely at the risk of the Consultant and Consultant shall take all precautions necessary for the proper performance thereof; and\n\nAnswer the following question: How many days after receiving an invoice is payment due?\n"], "reference": ["25 days. \n"]} +{"qna_file": "compositional_skills/extraction/services_agreement/plain_text/qna.yaml", "question_id": "29810754134329322593627498169593966476", "category": "taxonomy", "turns": ["Given the context below:\nrequirements. Upon request, Consultant shall provide documentation of its expenses. Payment of such invoices will be due within twenty-five (25) days of the receipt thereof.\n\nB. Fund Availability/Annual Appropriation. Payment pursuant to this Agreement, whether in whole or in part, is subject to and contingent upon the continuing availability of County funds for purposes hereof. In the event that said funds, or any part thereof, become unavailable as determined by the County, the County may immediately terminate this Agreement or amend it accordingly.\n\nC. Multi-Year Contracts. The obligations of the County hereunder shall not constitute a general obligation indebtedness or multiple year direct or indirect debt or other fin ancial obligation whatsoever within the meaning of the Constitution or laws of the State of Colorado.\n\n3. Term. The term of this Agreement shall be from ____________ to _____________. The Agreement may be terminated earlier by final completion of the Services by the Consultant and acceptance of the services by the County or through the termination provisions provided herein.\n\n4. Termination. Consultant may terminate this Agreement at any time by giving the County written notice of not less than sixty (60) days. County may terminate this Agreement at any time in the event that Consultant violates the terms of this Agreement or fails to produce a result that meets the specifications of this Agreement. In the event of termination, payments will be made to Consultant for all work performed up to the date of termination.\n\n5. Relationship. The parties understand and agree that Consultant is an independent Consultant and that Consultant is not an employee, agent or servant of the County, nor is Consultant entitled to County employment benefits. CONSULTANT UNDERSTANDS AND AGREES THAT CONSULTANT IS NOT ENTITLED TO WORKERS' COMPENSATION BENEFITS AND THAT CONSULTANT IS OBLIGATED TO PAY FEDERAL AND STATE INCOME TAX ON ANY MONEYS EARNED PURSUANT TO THIS CONTRACT. As an independent Consultant, Consultants agrees that:\n\nA. Consultant does not have the authority to act for the County, or to bind the County in any respect whatsoever, or to incur any debts or liabilities in the name of or on behalf of the County; and\n\nB. Consultant has and hereby retains control of and supervision over the performance of Consultant's obligations hereunder and control over any persons employed or contracted by Consultant for performing the Services hereunder; and\n\nC. County will not provide training or instruction to Consultant or any of its employees regarding the performance of Services hereunder; and\n\nD. Neither Consultant, nor its employees or Consultants, will receive benefits of any kind from the County. Consultant represents that it is engaged in providing similar services to the general public and not required to work exclusively for the County; and\n\nE. All Services are to be performed solely at the risk of the Consultant and Consultant shall take all precautions necessary for the proper performance thereof; and\n\nAnswer the following question: Is the Consultant considered an employee of the County?"], "reference": ["No. The Consultant is an independent Consultant and that Consultant is not an employee, agent or servant of the County, nor is Consultant entitled to County employment benefits.\n"]} +{"qna_file": "compositional_skills/extraction/services_agreement/plain_text/qna.yaml", "question_id": "223340806429086081207715704270409505419", "category": "taxonomy", "turns": ["Given the context below:\nrequirements. Upon request, Consultant shall provide documentation of its expenses. Payment of such invoices will be due within twenty-five (25) days of the receipt thereof.\n\nB. Fund Availability/Annual Appropriation. Payment pursuant to this Agreement, whether in whole or in part, is subject to and contingent upon the continuing availability of County funds for purposes hereof. In the event that said funds, or any part thereof, become unavailable as determined by the County, the County may immediately terminate this Agreement or amend it accordingly.\n\nC. Multi-Year Contracts. The obligations of the County hereunder shall not constitute a general obligation indebtedness or multiple year direct or indirect debt or other fin ancial obligation whatsoever within the meaning of the Constitution or laws of the State of Colorado.\n\n3. Term. The term of this Agreement shall be from ____________ to _____________. The Agreement may be terminated earlier by final completion of the Services by the Consultant and acceptance of the services by the County or through the termination provisions provided herein.\n\n4. Termination. Consultant may terminate this Agreement at any time by giving the County written notice of not less than sixty (60) days. County may terminate this Agreement at any time in the event that Consultant violates the terms of this Agreement or fails to produce a result that meets the specifications of this Agreement. In the event of termination, payments will be made to Consultant for all work performed up to the date of termination.\n\n5. Relationship. The parties understand and agree that Consultant is an independent Consultant and that Consultant is not an employee, agent or servant of the County, nor is Consultant entitled to County employment benefits. CONSULTANT UNDERSTANDS AND AGREES THAT CONSULTANT IS NOT ENTITLED TO WORKERS' COMPENSATION BENEFITS AND THAT CONSULTANT IS OBLIGATED TO PAY FEDERAL AND STATE INCOME TAX ON ANY MONEYS EARNED PURSUANT TO THIS CONTRACT. As an independent Consultant, Consultants agrees that:\n\nA. Consultant does not have the authority to act for the County, or to bind the County in any respect whatsoever, or to incur any debts or liabilities in the name of or on behalf of the County; and\n\nB. Consultant has and hereby retains control of and supervision over the performance of Consultant's obligations hereunder and control over any persons employed or contracted by Consultant for performing the Services hereunder; and\n\nC. County will not provide training or instruction to Consultant or any of its employees regarding the performance of Services hereunder; and\n\nD. Neither Consultant, nor its employees or Consultants, will receive benefits of any kind from the County. Consultant represents that it is engaged in providing similar services to the general public and not required to work exclusively for the County; and\n\nE. All Services are to be performed solely at the risk of the Consultant and Consultant shall take all precautions necessary for the proper performance thereof; and\n\nAnswer the following question: Which state is mentioned in this agreement. \n"], "reference": ["The State of Colorado is mentioned. \n"]} +{"qna_file": "compositional_skills/extraction/services_agreement/reasoning/qna.yaml", "question_id": "304652801270390720622443832495676584039", "category": "taxonomy", "turns": ["Given the context below:\n8. Indemnification and Insurance. Consultant shall indemnify and hold harmless the County from and against all claims, damages, losses, and expenses arising out of or resulting from acts or omissions of the Consultant, Consultant's sub-Consultants or otherwise arising out of the performance of services by Consultant. No later than seven days after execution of this Agreement, Consultant shall provide the County with certificates of insurance evidencing the types and amounts of insurance specified below:\n\nA. Standard Workers' Compensation as required by law in the State of Colorado; and\n\nB. Comprehensive General Liability Insurance for operations and contractual liability adequate to cover the liability assumed hereunder with limits of not less than $350,000 on account of any one person and $990,000 for each occurrence of property damage and personal injury; and\n\nC. Automobile Liability insurance in those instances where Consultant uses an automobile, regardless of ownership, for the performance of the Services. Consultant shall carry insurance, written on the comprehensive automobile form insuring all owned and nonowned automobiles with limits of not less than $100,000 (bodily injury per person), $300,000 (each accident) and $50,000 (property damage).\n\nInsurance coverage shall not be reduced below the limits described above or cancelled without County's written approval of such reduction or cancellation. Reduction, cancellation or termination of insurance coverage, or failure to obtain insurance coverage, without the County's written approval shall constitute a material breach of the Agreement and shall automatically terminate the Agreement. Consultant shall require that any of its agents or sub-consultants who enter upon the County's premises shall maintain like insurance. Certificates of such insurance, of agents and sub-consultants, shall be provided to the County upon request. With regard to all insurance, such insurance shall:\n\nA. Be primary insurance to the full limits of liability herein before stated and, should County have other valid insurance, County insurance shall be excess insurance only; and B. Not cancelled without thirty (30) days prior written notice to the County.\n\nAnswer the following question: Someone has filed a claim against me. I am a consultant working for the county, and I got the lowest limits of insurance that the services agreement would permit. The claim is for $98,000 of property damage that has nothing to do with automobile activity. I have not had any other claims filed against me. How much will I have to pay?\n"], "reference": ["You will not have to pay anything because the $98,000 claim is less than the $990,000 of property damage included in the Comprehensive General Liability Insurance mandated by the services agreement covers.\n"]} +{"qna_file": "compositional_skills/extraction/services_agreement/reasoning/qna.yaml", "question_id": "175062439482186120916271144053735534277", "category": "taxonomy", "turns": ["Given the context below:\n8. Indemnification and Insurance. Consultant shall indemnify and hold harmless the County from and against all claims, damages, losses, and expenses arising out of or resulting from acts or omissions of the Consultant, Consultant's sub-Consultants or otherwise arising out of the performance of services by Consultant. No later than seven days after execution of this Agreement, Consultant shall provide the County with certificates of insurance evidencing the types and amounts of insurance specified below:\n\nA. Standard Workers' Compensation as required by law in the State of Colorado; and\n\nB. Comprehensive General Liability Insurance for operations and contractual liability adequate to cover the liability assumed hereunder with limits of not less than $350,000 on account of any one person and $990,000 for each occurrence of property damage and personal injury; and\n\nC. Automobile Liability insurance in those instances where Consultant uses an automobile, regardless of ownership, for the performance of the Services. Consultant shall carry insurance, written on the comprehensive automobile form insuring all owned and nonowned automobiles with limits of not less than $100,000 (bodily injury per person), $300,000 (each accident) and $50,000 (property damage).\n\nInsurance coverage shall not be reduced below the limits described above or cancelled without County's written approval of such reduction or cancellation. Reduction, cancellation or termination of insurance coverage, or failure to obtain insurance coverage, without the County's written approval shall constitute a material breach of the Agreement and shall automatically terminate the Agreement. Consultant shall require that any of its agents or sub-consultants who enter upon the County's premises shall maintain like insurance. Certificates of such insurance, of agents and sub-consultants, shall be provided to the County upon request. With regard to all insurance, such insurance shall:\n\nA. Be primary insurance to the full limits of liability herein before stated and, should County have other valid insurance, County insurance shall be excess insurance only; and B. Not cancelled without thirty (30) days prior written notice to the County.\n\nAnswer the following question: Today is January 1 2024. I have the lowest limit of comprehensive general liability insurance mandated by the services agreement. It is going to expire on January 5. I have not yet notified the County that it is going to expire soon. Am I in compliance with the services agreement?"], "reference": ["You are not in compliance with the services agreement. You must provide the county written notice thirty (30) days prior to the termination of the insurance. This means that you have breached the Agreement and the Agreement will automatically terminate.\u00a0\n"]} +{"qna_file": "compositional_skills/extraction/services_agreement/reasoning/qna.yaml", "question_id": "255939985451234546835017686239402555748", "category": "taxonomy", "turns": ["Given the context below:\n8. Indemnification and Insurance. Consultant shall indemnify and hold harmless the County from and against all claims, damages, losses, and expenses arising out of or resulting from acts or omissions of the Consultant, Consultant's sub-Consultants or otherwise arising out of the performance of services by Consultant. No later than seven days after execution of this Agreement, Consultant shall provide the County with certificates of insurance evidencing the types and amounts of insurance specified below:\n\nA. Standard Workers' Compensation as required by law in the State of Colorado; and\n\nB. Comprehensive General Liability Insurance for operations and contractual liability adequate to cover the liability assumed hereunder with limits of not less than $350,000 on account of any one person and $990,000 for each occurrence of property damage and personal injury; and\n\nC. Automobile Liability insurance in those instances where Consultant uses an automobile, regardless of ownership, for the performance of the Services. Consultant shall carry insurance, written on the comprehensive automobile form insuring all owned and nonowned automobiles with limits of not less than $100,000 (bodily injury per person), $300,000 (each accident) and $50,000 (property damage).\n\nInsurance coverage shall not be reduced below the limits described above or cancelled without County's written approval of such reduction or cancellation. Reduction, cancellation or termination of insurance coverage, or failure to obtain insurance coverage, without the County's written approval shall constitute a material breach of the Agreement and shall automatically terminate the Agreement. Consultant shall require that any of its agents or sub-consultants who enter upon the County's premises shall maintain like insurance. Certificates of such insurance, of agents and sub-consultants, shall be provided to the County upon request. With regard to all insurance, such insurance shall:\n\nA. Be primary insurance to the full limits of liability herein before stated and, should County have other valid insurance, County insurance shall be excess insurance only; and B. Not cancelled without thirty (30) days prior written notice to the County.\n\nAnswer the following question: I own 20 cars and rent 3. 10 of these cars will be used to execute tasks related to my work as a consultant for the county. I also have 1 sub-consultant who will be driving a truck that she owns inside County limits for this job. How many of these vehicles must have insurance to satisfy the services agreement\u2019s requirements?\u00a0\n"], "reference": ["11 vehicles must have automobile liability insurance with limits of not less than $100,000 (bodily injury per person), $300,000 (each accident) and $50,000 (property damage).\n"]} +{"qna_file": "compositional_skills/extraction/services_agreement/reasoning/qna.yaml", "question_id": "85615850883024800282606439901236430098", "category": "taxonomy", "turns": ["Given the context below:\nrequirements. Upon request, Consultant shall provide documentation of its expenses. Payment of such invoices will be due within twenty-five (25) days of the receipt thereof.\n\nB. Fund Availability/Annual Appropriation. Payment pursuant to this Agreement, whether in whole or in part, is subject to and contingent upon the continuing availability of County funds for purposes hereof. In the event that said funds, or any part thereof, become unavailable as determined by the County, the County may immediately terminate this Agreement or amend it accordingly.\n\nC. Multi-Year Contracts. The obligations of the County hereunder shall not constitute a general obligation indebtedness or multiple year direct or indirect debt or other fin ancial obligation whatsoever within the meaning of the Constitution or laws of the State of Colorado.\n\n3. Term. The term of this Agreement shall be from ____________ to _____________. The Agreement may be terminated earlier by final completion of the Services by the Consultant and acceptance of the services by the County or through the termination provisions provided herein.\n\n4. Termination. Consultant may terminate this Agreement at any time by giving the County written notice of not less than sixty (60) days. County may terminate this Agreement at any time in the event that Consultant violates the terms of this Agreement or fails to produce a result that meets the specifications of this Agreement. In the event of termination, payments will be made to Consultant for all work performed up to the date of termination.\n\n5. Relationship. The parties understand and agree that Consultant is an independent Consultant and that Consultant is not an employee, agent or servant of the County, nor is Consultant entitled to County employment benefits. CONSULTANT UNDERSTANDS AND AGREES THAT CONSULTANT IS NOT ENTITLED TO WORKERS' COMPENSATION BENEFITS AND THAT CONSULTANT IS OBLIGATED TO PAY FEDERAL AND STATE INCOME TAX ON ANY MONEYS EARNED PURSUANT TO THIS CONTRACT. As an independent Consultant, Consultants agrees that:\n\nA. Consultant does not have the authority to act for the County, or to bind the County in any respect whatsoever, or to incur any debts or liabilities in the name of or on behalf of the County; and\n\nB. Consultant has and hereby retains control of and supervision over the performance of Consultant's obligations hereunder and control over any persons employed or contracted by Consultant for performing the Services hereunder; and\n\nC. County will not provide training or instruction to Consultant or any of its employees regarding the performance of Services hereunder; and\n\nD. Neither Consultant, nor its employees or Consultants, will receive benefits of any kind from the County. Consultant represents that it is engaged in providing similar services to the general public and not required to work exclusively for the County; and\n\nE. All Services are to be performed solely at the risk of the Consultant and Consultant shall take all precautions necessary for the proper performance thereof; and\n\nAnswer the following question: The County decides to terminate the agreement early because the Consultant's work is not meeting expectations. The county set the termination date as April 3rd. How much is the County obligated to pay the Consultant, considering the early termination? \n\n"], "reference": ["The County is obligated to pay the Consultant for all work performed up to April 3rd. \n"]} +{"qna_file": "compositional_skills/extraction/services_agreement/reasoning/qna.yaml", "question_id": "68756368209358997145592071342243534015", "category": "taxonomy", "turns": ["Given the context below:\nrequirements. Upon request, Consultant shall provide documentation of its expenses. Payment of such invoices will be due within twenty-five (25) days of the receipt thereof.\n\nB. Fund Availability/Annual Appropriation. Payment pursuant to this Agreement, whether in whole or in part, is subject to and contingent upon the continuing availability of County funds for purposes hereof. In the event that said funds, or any part thereof, become unavailable as determined by the County, the County may immediately terminate this Agreement or amend it accordingly.\n\nC. Multi-Year Contracts. The obligations of the County hereunder shall not constitute a general obligation indebtedness or multiple year direct or indirect debt or other fin ancial obligation whatsoever within the meaning of the Constitution or laws of the State of Colorado.\n\n3. Term. The term of this Agreement shall be from ____________ to _____________. The Agreement may be terminated earlier by final completion of the Services by the Consultant and acceptance of the services by the County or through the termination provisions provided herein.\n\n4. Termination. Consultant may terminate this Agreement at any time by giving the County written notice of not less than sixty (60) days. County may terminate this Agreement at any time in the event that Consultant violates the terms of this Agreement or fails to produce a result that meets the specifications of this Agreement. In the event of termination, payments will be made to Consultant for all work performed up to the date of termination.\n\n5. Relationship. The parties understand and agree that Consultant is an independent Consultant and that Consultant is not an employee, agent or servant of the County, nor is Consultant entitled to County employment benefits. CONSULTANT UNDERSTANDS AND AGREES THAT CONSULTANT IS NOT ENTITLED TO WORKERS' COMPENSATION BENEFITS AND THAT CONSULTANT IS OBLIGATED TO PAY FEDERAL AND STATE INCOME TAX ON ANY MONEYS EARNED PURSUANT TO THIS CONTRACT. As an independent Consultant, Consultants agrees that:\n\nA. Consultant does not have the authority to act for the County, or to bind the County in any respect whatsoever, or to incur any debts or liabilities in the name of or on behalf of the County; and\n\nB. Consultant has and hereby retains control of and supervision over the performance of Consultant's obligations hereunder and control over any persons employed or contracted by Consultant for performing the Services hereunder; and\n\nC. County will not provide training or instruction to Consultant or any of its employees regarding the performance of Services hereunder; and\n\nD. Neither Consultant, nor its employees or Consultants, will receive benefits of any kind from the County. Consultant represents that it is engaged in providing similar services to the general public and not required to work exclusively for the County; and\n\nE. All Services are to be performed solely at the risk of the Consultant and Consultant shall take all precautions necessary for the proper performance thereof; and\n\nAnswer the following question: Is the Consultant entitled to educational and professional development benefits provided by the county? "], "reference": ["No. The Consultant is an independent contractor, not an employee of the County, therefore is not entitled to employment benefits like educational and professional development. \n"]} +{"qna_file": "compositional_skills/extraction/services_agreement/reasoning/qna.yaml", "question_id": "248214476090895103512104137370952261638", "category": "taxonomy", "turns": ["Given the context below:\nrequirements. Upon request, Consultant shall provide documentation of its expenses. Payment of such invoices will be due within twenty-five (25) days of the receipt thereof.\n\nB. Fund Availability/Annual Appropriation. Payment pursuant to this Agreement, whether in whole or in part, is subject to and contingent upon the continuing availability of County funds for purposes hereof. In the event that said funds, or any part thereof, become unavailable as determined by the County, the County may immediately terminate this Agreement or amend it accordingly.\n\nC. Multi-Year Contracts. The obligations of the County hereunder shall not constitute a general obligation indebtedness or multiple year direct or indirect debt or other fin ancial obligation whatsoever within the meaning of the Constitution or laws of the State of Colorado.\n\n3. Term. The term of this Agreement shall be from ____________ to _____________. The Agreement may be terminated earlier by final completion of the Services by the Consultant and acceptance of the services by the County or through the termination provisions provided herein.\n\n4. Termination. Consultant may terminate this Agreement at any time by giving the County written notice of not less than sixty (60) days. County may terminate this Agreement at any time in the event that Consultant violates the terms of this Agreement or fails to produce a result that meets the specifications of this Agreement. In the event of termination, payments will be made to Consultant for all work performed up to the date of termination.\n\n5. Relationship. The parties understand and agree that Consultant is an independent Consultant and that Consultant is not an employee, agent or servant of the County, nor is Consultant entitled to County employment benefits. CONSULTANT UNDERSTANDS AND AGREES THAT CONSULTANT IS NOT ENTITLED TO WORKERS' COMPENSATION BENEFITS AND THAT CONSULTANT IS OBLIGATED TO PAY FEDERAL AND STATE INCOME TAX ON ANY MONEYS EARNED PURSUANT TO THIS CONTRACT. As an independent Consultant, Consultants agrees that:\n\nA. Consultant does not have the authority to act for the County, or to bind the County in any respect whatsoever, or to incur any debts or liabilities in the name of or on behalf of the County; and\n\nB. Consultant has and hereby retains control of and supervision over the performance of Consultant's obligations hereunder and control over any persons employed or contracted by Consultant for performing the Services hereunder; and\n\nC. County will not provide training or instruction to Consultant or any of its employees regarding the performance of Services hereunder; and\n\nD. Neither Consultant, nor its employees or Consultants, will receive benefits of any kind from the County. Consultant represents that it is engaged in providing similar services to the general public and not required to work exclusively for the County; and\n\nE. All Services are to be performed solely at the risk of the Consultant and Consultant shall take all precautions necessary for the proper performance thereof; and\n\nAnswer the following question: On Mar 1st, if the County declares there are insufficient funds to cover the entire invoice, can the County refuse to pay the full invoice amount?\u00a0\n"], "reference": ["Yes, the County can refuse to pay the full invoice amount based on Section B. \n"]} +{"qna_file": "compositional_skills/extraction/inference/quantitative/table_analaysis/qna.yaml", "question_id": "268057620481694539616699380269046419201", "category": "taxonomy", "turns": ["Given the context below:\n\\nQuarter,Q1,Q2,Q3,Q4\\nRetail,120,130,140,150\\nTechnology,200,210,220,230\\nHealthcare,100,110,120,130\\n\nAnswer the following question: Extract the highest and lowest gross revenue (in million dollars) for each business quarter in 2022 from the following data. Return as a CSV string.\n"], "reference": ["Q1: High 200, Low 100; Q2: High 210, Low 110; Q3: High 220, Low 120; Q4: High 230, Low 130"]} +{"qna_file": "compositional_skills/extraction/inference/qualitative/sentiment/qna.yaml", "question_id": "2970580161522853830686954193770890396", "category": "taxonomy", "turns": ["Given the context below:\nThis science fiction movie released on July 5, 2021, was phenomenal. The special effects, the acting, the plot - everything was top-notch.\\n2. Never before have I been so disappointed with a movie. The plot was predictable, and the characters were one-dimensional. In my opinion, this movie is the worst one to have been released in 2022.\\n3. The movie was okay. There were some parts I enjoyed, but there were also parts that felt lackluster. This is a movie that was released in March 2019 and seems to be quite ordinary.\nAnswer the following question: Evaluate the following movies on a scale of 1 to 5, with 1 being very negative, 3 being neutral, and 5 being very positive. Return the answer as a JSON array of integers."], "reference": ["- 5\n- 1\n- 3\n"]} +{"qna_file": "compositional_skills/extraction/email/bullet_points/qna.yaml", "question_id": "324299797434847777726525789127408390310", "category": "taxonomy", "turns": ["Given the context below:\nSubject: High-Def Monitor Update\n\nDate:\n\nFriday, February 23, 2024 at 9:13:32$_{ }$PM Eastern Standard Time\n\nFrom:\n\nLuke Inglis\n\nTo:\n\nKate Soule\n\nHi Kate,\n\nI am writing to explain to you the delay in our lab receiving monitors. On Dec 2$^{nd,}$ 2023, I started the process of purchasing 7 high-def Samsung monitors for the MIT-IBM Watson AI Lab. I think you already know, but I wanted to outline the steps needed to make this purchase.\n\n1. Obtain a quote from Samsung\n\n2. Submit an Expense Pre-Approval\n\n3. Approval from Line VP\n\n4. Submit an Ad Hoc request to Procurement\n\n5. Approval from Manager\n\n6. Approval from 2$^{nd}$ line Manager\n\n7. Approval from Finance\n\n8. Procurement then identifies their specialist for this request\n\n9. The Procurement specialist then reviews the request and determines which supplier to use and reaches out to the contact person there\n\n10. These two finalize agreements needed to execute the order\n\n11. Monitors are then shipped to our location\n\nWe have now finally reached number 11 in this process. Below I wanted to outline what we are receiving and when they are expected to arrive.\n\n| Monitor\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Price\u00a0\u00a0| Estimated Arrival\u00a0\u00a0|\n|----------------------|---------|---------------------|\n| Odyssey Ark 55-inch\u00a0| $1,952\u00a0| 5/10/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G7 32-Inch\u00a0\u00a0| $669\u00a0\u00a0| 3/7/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G7 32-Inch\u00a0\u00a0| $669\u00a0\u00a0| 3/7/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G55C 27-Inch | $297\u00a0\u00a0| 2/27/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G55C 27-Inch | $297\u00a0\u00a0| 2/27/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nSpecifically, I wanted to address the shipping date for the Odyssey Ark 55-inch. I know the 10th of May 2024 is not ideal but there was confusion as the supplier is in Europe and thought that 5/10/2024 was actually 10/5/2024.\n\nI apologize for the confusion.\n\nRegards,\n\nChad Smith Operations Manager AI Models & MIT-IBM Watson AI Lab IBM Research\n\nAnswer the following question: Provide a bulleted list of all steps needed to complete the monitor purchase.\n"], "reference": ["- Obtain a quote from Samsung\n- Submit an Expense Pre-Approval\n- Approval from Line VP\n- Submit an Ad Hoc request to Procurement\n- Approval from Manager\n- Approval from 2nd line Manager\n- Approval from Finance\n- Procurement then identifies their specialist for this request\n- The Procurement specialist then reviews the request and determines which supplier to use and reaches out to the contact person there\n- These two finalize agreements needed to execute the order\n- Monitors are then shipped to our location\n"]} +{"qna_file": "compositional_skills/extraction/email/bullet_points/qna.yaml", "question_id": "318289487480832152504397814457632313038", "category": "taxonomy", "turns": ["Given the context below:\nSubject: High-Def Monitor Update\n\nDate:\n\nFriday, February 23, 2024 at 9:13:32$_{ }$PM Eastern Standard Time\n\nFrom:\n\nLuke Inglis\n\nTo:\n\nKate Soule\n\nHi Kate,\n\nI am writing to explain to you the delay in our lab receiving monitors. On Dec 2$^{nd,}$ 2023, I started the process of purchasing 7 high-def Samsung monitors for the MIT-IBM Watson AI Lab. I think you already know, but I wanted to outline the steps needed to make this purchase.\n\n1. Obtain a quote from Samsung\n\n2. Submit an Expense Pre-Approval\n\n3. Approval from Line VP\n\n4. Submit an Ad Hoc request to Procurement\n\n5. Approval from Manager\n\n6. Approval from 2$^{nd}$ line Manager\n\n7. Approval from Finance\n\n8. Procurement then identifies their specialist for this request\n\n9. The Procurement specialist then reviews the request and determines which supplier to use and reaches out to the contact person there\n\n10. These two finalize agreements needed to execute the order\n\n11. Monitors are then shipped to our location\n\nWe have now finally reached number 11 in this process. Below I wanted to outline what we are receiving and when they are expected to arrive.\n\n| Monitor\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Price\u00a0\u00a0| Estimated Arrival\u00a0\u00a0|\n|----------------------|---------|---------------------|\n| Odyssey Ark 55-inch\u00a0| $1,952\u00a0| 5/10/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G7 32-Inch\u00a0\u00a0| $669\u00a0\u00a0| 3/7/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G7 32-Inch\u00a0\u00a0| $669\u00a0\u00a0| 3/7/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G55C 27-Inch | $297\u00a0\u00a0| 2/27/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G55C 27-Inch | $297\u00a0\u00a0| 2/27/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nSpecifically, I wanted to address the shipping date for the Odyssey Ark 55-inch. I know the 10th of May 2024 is not ideal but there was confusion as the supplier is in Europe and thought that 5/10/2024 was actually 10/5/2024.\n\nI apologize for the confusion.\n\nRegards,\n\nChad Smith Operations Manager AI Models & MIT-IBM Watson AI Lab IBM Research\n\nAnswer the following question: Give me a bullet list of all Monitor types that were purchased"], "reference": ["- Odyssey Ark 55-Inch\n- Odyssey G7 32-Inch\n- Odyssey G55C 27-Inch\n"]} +{"qna_file": "compositional_skills/extraction/email/bullet_points/qna.yaml", "question_id": "171530020619378320009959917285030563938", "category": "taxonomy", "turns": ["Given the context below:\nSubject: High-Def Monitor Update\n\nDate:\n\nFriday, February 23, 2024 at 9:13:32$_{ }$PM Eastern Standard Time\n\nFrom:\n\nLuke Inglis\n\nTo:\n\nKate Soule\n\nHi Kate,\n\nI am writing to explain to you the delay in our lab receiving monitors. On Dec 2$^{nd,}$ 2023, I started the process of purchasing 7 high-def Samsung monitors for the MIT-IBM Watson AI Lab. I think you already know, but I wanted to outline the steps needed to make this purchase.\n\n1. Obtain a quote from Samsung\n\n2. Submit an Expense Pre-Approval\n\n3. Approval from Line VP\n\n4. Submit an Ad Hoc request to Procurement\n\n5. Approval from Manager\n\n6. Approval from 2$^{nd}$ line Manager\n\n7. Approval from Finance\n\n8. Procurement then identifies their specialist for this request\n\n9. The Procurement specialist then reviews the request and determines which supplier to use and reaches out to the contact person there\n\n10. These two finalize agreements needed to execute the order\n\n11. Monitors are then shipped to our location\n\nWe have now finally reached number 11 in this process. Below I wanted to outline what we are receiving and when they are expected to arrive.\n\n| Monitor\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Price\u00a0\u00a0| Estimated Arrival\u00a0\u00a0|\n|----------------------|---------|---------------------|\n| Odyssey Ark 55-inch\u00a0| $1,952\u00a0| 5/10/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G7 32-Inch\u00a0\u00a0| $669\u00a0\u00a0| 3/7/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G7 32-Inch\u00a0\u00a0| $669\u00a0\u00a0| 3/7/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G55C 27-Inch | $297\u00a0\u00a0| 2/27/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G55C 27-Inch | $297\u00a0\u00a0| 2/27/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nSpecifically, I wanted to address the shipping date for the Odyssey Ark 55-inch. I know the 10th of May 2024 is not ideal but there was confusion as the supplier is in Europe and thought that 5/10/2024 was actually 10/5/2024.\n\nI apologize for the confusion.\n\nRegards,\n\nChad Smith Operations Manager AI Models & MIT-IBM Watson AI Lab IBM Research\n\nAnswer the following question: Return a bulleted listing of all people needed for approval before the purchase can be completed.\n"], "reference": ["- Line VP\n- Manager\n- 2nd line Manager\n- Finance\n"]} +{"qna_file": "compositional_skills/extraction/email/plain_text/qna.yaml", "question_id": "32180289296117404378038075861540702954", "category": "taxonomy", "turns": ["Given the context below:\nSubject: High-Def Monitor Update\n\nDate:\n\nFriday, February 23, 2024 at 9:13:32$_{ }$PM Eastern Standard Time\n\nFrom:\n\nLuke Inglis\n\nTo:\n\nKate Soule\n\nHi Kate,\n\nI am writing to explain to you the delay in our lab receiving monitors. On Dec 2$^{nd,}$ 2023, I started the process of purchasing 7 high-def Samsung monitors for the MIT-IBM Watson AI Lab. I think you already know, but I wanted to outline the steps needed to make this purchase.\n\n1. Obtain a quote from Samsung\n\n2. Submit an Expense Pre-Approval\n\n3. Approval from Line VP\n\n4. Submit an Ad Hoc request to Procurement\n\n5. Approval from Manager\n\n6. Approval from 2$^{nd}$ line Manager\n\n7. Approval from Finance\n\n8. Procurement then identifies their specialist for this request\n\n9. The Procurement specialist then reviews the request and determines which supplier to use and reaches out to the contact person there\n\n10. These two finalize agreements needed to execute the order\n\n11. Monitors are then shipped to our location\n\nWe have now finally reached number 11 in this process. Below I wanted to outline what we are receiving and when they are expected to arrive.\n\n| Monitor\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Price\u00a0\u00a0| Estimated Arrival\u00a0\u00a0|\n|----------------------|---------|---------------------|\n| Odyssey Ark 55-inch\u00a0| $1,952\u00a0| 5/10/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G7 32-Inch\u00a0\u00a0| $669\u00a0\u00a0| 3/7/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G7 32-Inch\u00a0\u00a0| $669\u00a0\u00a0| 3/7/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G55C 27-Inch | $297\u00a0\u00a0| 2/27/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G55C 27-Inch | $297\u00a0\u00a0| 2/27/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nSpecifically, I wanted to address the shipping date for the Odyssey Ark 55-inch. I know the 10th of May 2024 is not ideal but there was confusion as the supplier is in Europe and thought that 5/10/2024 was actually 10/5/2024.\n\nI apologize for the confusion.\n\nRegards,\n\nLuke Inglis Operations Manager AI Models & MIT-IBM Watson AI Lab IBM Research\n\nAnswer the following question: What was the fourth step of the purchase process?\n"], "reference": ["Submit an Ad Hoc request to Procurement\n"]} +{"qna_file": "compositional_skills/extraction/email/plain_text/qna.yaml", "question_id": "272131407913288118274551612877356874018", "category": "taxonomy", "turns": ["Given the context below:\nSubject: High-Def Monitor Update\n\nDate:\n\nFriday, February 23, 2024 at 9:13:32$_{ }$PM Eastern Standard Time\n\nFrom:\n\nLuke Inglis\n\nTo:\n\nKate Soule\n\nHi Kate,\n\nI am writing to explain to you the delay in our lab receiving monitors. On Dec 2$^{nd,}$ 2023, I started the process of purchasing 7 high-def Samsung monitors for the MIT-IBM Watson AI Lab. I think you already know, but I wanted to outline the steps needed to make this purchase.\n\n1. Obtain a quote from Samsung\n\n2. Submit an Expense Pre-Approval\n\n3. Approval from Line VP\n\n4. Submit an Ad Hoc request to Procurement\n\n5. Approval from Manager\n\n6. Approval from 2$^{nd}$ line Manager\n\n7. Approval from Finance\n\n8. Procurement then identifies their specialist for this request\n\n9. The Procurement specialist then reviews the request and determines which supplier to use and reaches out to the contact person there\n\n10. These two finalize agreements needed to execute the order\n\n11. Monitors are then shipped to our location\n\nWe have now finally reached number 11 in this process. Below I wanted to outline what we are receiving and when they are expected to arrive.\n\n| Monitor\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Price\u00a0\u00a0| Estimated Arrival\u00a0\u00a0|\n|----------------------|---------|---------------------|\n| Odyssey Ark 55-inch\u00a0| $1,952\u00a0| 5/10/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G7 32-Inch\u00a0\u00a0| $669\u00a0\u00a0| 3/7/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G7 32-Inch\u00a0\u00a0| $669\u00a0\u00a0| 3/7/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G55C 27-Inch | $297\u00a0\u00a0| 2/27/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G55C 27-Inch | $297\u00a0\u00a0| 2/27/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nSpecifically, I wanted to address the shipping date for the Odyssey Ark 55-inch. I know the 10th of May 2024 is not ideal but there was confusion as the supplier is in Europe and thought that 5/10/2024 was actually 10/5/2024.\n\nI apologize for the confusion.\n\nRegards,\n\nLuke Inglis Operations Manager AI Models & MIT-IBM Watson AI Lab IBM Research\n\nAnswer the following question: What is being purchased?"], "reference": ["7 high-def Samsung monitors for the MIT-IBM Watson AI Lab\n"]} +{"qna_file": "compositional_skills/extraction/email/plain_text/qna.yaml", "question_id": "312159280114529525255956179076551846138", "category": "taxonomy", "turns": ["Given the context below:\nSubject: High-Def Monitor Update\n\nDate:\n\nFriday, February 23, 2024 at 9:13:32$_{ }$PM Eastern Standard Time\n\nFrom:\n\nLuke Inglis\n\nTo:\n\nKate Soule\n\nHi Kate,\n\nI am writing to explain to you the delay in our lab receiving monitors. On Dec 2$^{nd,}$ 2023, I started the process of purchasing 7 high-def Samsung monitors for the MIT-IBM Watson AI Lab. I think you already know, but I wanted to outline the steps needed to make this purchase.\n\n1. Obtain a quote from Samsung\n\n2. Submit an Expense Pre-Approval\n\n3. Approval from Line VP\n\n4. Submit an Ad Hoc request to Procurement\n\n5. Approval from Manager\n\n6. Approval from 2$^{nd}$ line Manager\n\n7. Approval from Finance\n\n8. Procurement then identifies their specialist for this request\n\n9. The Procurement specialist then reviews the request and determines which supplier to use and reaches out to the contact person there\n\n10. These two finalize agreements needed to execute the order\n\n11. Monitors are then shipped to our location\n\nWe have now finally reached number 11 in this process. Below I wanted to outline what we are receiving and when they are expected to arrive.\n\n| Monitor\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Price\u00a0\u00a0| Estimated Arrival\u00a0\u00a0|\n|----------------------|---------|---------------------|\n| Odyssey Ark 55-inch\u00a0| $1,952\u00a0| 5/10/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G7 32-Inch\u00a0\u00a0| $669\u00a0\u00a0| 3/7/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G7 32-Inch\u00a0\u00a0| $669\u00a0\u00a0| 3/7/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G55C 27-Inch | $297\u00a0\u00a0| 2/27/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G55C 27-Inch | $297\u00a0\u00a0| 2/27/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nSpecifically, I wanted to address the shipping date for the Odyssey Ark 55-inch. I know the 10th of May 2024 is not ideal but there was confusion as the supplier is in Europe and thought that 5/10/2024 was actually 10/5/2024.\n\nI apologize for the confusion.\n\nRegards,\n\nLuke Inglis Operations Manager AI Models & MIT-IBM Watson AI Lab IBM Research\n\nAnswer the following question: What are all of the estimated arrival dates for these items?\u00a0\n"], "reference": ["2/27/2024, 3/7/2024, 5/10/2024\n"]} +{"qna_file": "compositional_skills/extraction/email/markdown/qna.yaml", "question_id": "334062825545286929775882974993785668767", "category": "taxonomy", "turns": ["Given the context below:\nSubject: High-Def Monitor Update\n\nDate:\n\nFriday, February 23, 2024 at 9:13:32$_{ }$PM Eastern Standard Time\n\nFrom:\n\nLuke Inglis\n\nTo:\n\nKate Soule\n\nHi Kate,\n\nI am writing to explain to you the delay in our lab receiving monitors. On Dec 2$^{nd,}$ 2023, I started the process of purchasing 7 high-def Samsung monitors for the MIT-IBM Watson AI Lab. I think you already know, but I wanted to outline the steps needed to make this purchase.\n\n1. Obtain a quote from Samsung\n\n2. Submit an Expense Pre-Approval\n\n3. Approval from Line VP\n\n4. Submit an Ad Hoc request to Procurement\n\n5. Approval from Manager\n\n6. Approval from 2$^{nd}$ line Manager\n\n7. Approval from Finance\n\n8. Procurement then identifies their specialist for this request\n\n9. The Procurement specialist then reviews the request and determines which supplier to use and reaches out to the contact person there\n\n10. These two finalize agreements needed to execute the order\n\n11. Monitors are then shipped to our location\n\nWe have now finally reached number 11 in this process. Below I wanted to outline what we are receiving and when they are expected to arrive.\n\n| Monitor\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Price\u00a0\u00a0| Estimated Arrival\u00a0\u00a0|\n|----------------------|---------|---------------------|\n| Odyssey Ark 55-inch\u00a0| $1,952\u00a0| 5/10/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G7 32-Inch\u00a0\u00a0| $669\u00a0\u00a0| 3/7/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G7 32-Inch\u00a0\u00a0| $669\u00a0\u00a0| 3/7/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G55C 27-Inch | $297\u00a0\u00a0| 2/27/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G55C 27-Inch | $297\u00a0\u00a0| 2/27/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nSpecifically, I wanted to address the shipping date for the Odyssey Ark 55-inch. I know the 10th of May 2024 is not ideal but there was confusion as the supplier is in Europe and thought that 5/10/2024 was actually 10/5/2024.\n\nI apologize for the confusion.\n\nRegards,\n\nLuke Inglis Operations Manager AI Models & MIT-IBM Watson AI Lab IBM Research\n\nAnswer the following question: Return all Monitor types, the quantity ordered, and their price in a markdown table with three columns: Monitor Name, Qty, and Price.\n"], "reference": ["|Monitor Name\u00a0\u00a0\u00a0\u00a0|Qty|Price |\n|--------------------|---|------|\n|Odyssey Ark 55-inch |1\u00a0|$1,952|\n|Odyssey G7 32-Inch\u00a0|3\u00a0|$669\u00a0|\n|Odyssey G55C 27-Inch|3\u00a0|$297\u00a0|\n"]} +{"qna_file": "compositional_skills/extraction/email/markdown/qna.yaml", "question_id": "215395887340668244357745311728790830527", "category": "taxonomy", "turns": ["Given the context below:\nSubject: High-Def Monitor Update\n\nDate:\n\nFriday, February 23, 2024 at 9:13:32$_{ }$PM Eastern Standard Time\n\nFrom:\n\nLuke Inglis\n\nTo:\n\nKate Soule\n\nHi Kate,\n\nI am writing to explain to you the delay in our lab receiving monitors. On Dec 2$^{nd,}$ 2023, I started the process of purchasing 7 high-def Samsung monitors for the MIT-IBM Watson AI Lab. I think you already know, but I wanted to outline the steps needed to make this purchase.\n\n1. Obtain a quote from Samsung\n\n2. Submit an Expense Pre-Approval\n\n3. Approval from Line VP\n\n4. Submit an Ad Hoc request to Procurement\n\n5. Approval from Manager\n\n6. Approval from 2$^{nd}$ line Manager\n\n7. Approval from Finance\n\n8. Procurement then identifies their specialist for this request\n\n9. The Procurement specialist then reviews the request and determines which supplier to use and reaches out to the contact person there\n\n10. These two finalize agreements needed to execute the order\n\n11. Monitors are then shipped to our location\n\nWe have now finally reached number 11 in this process. Below I wanted to outline what we are receiving and when they are expected to arrive.\n\n| Monitor\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Price\u00a0\u00a0| Estimated Arrival\u00a0\u00a0|\n|----------------------|---------|---------------------|\n| Odyssey Ark 55-inch\u00a0| $1,952\u00a0| 5/10/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G7 32-Inch\u00a0\u00a0| $669\u00a0\u00a0| 3/7/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G7 32-Inch\u00a0\u00a0| $669\u00a0\u00a0| 3/7/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G55C 27-Inch | $297\u00a0\u00a0| 2/27/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G55C 27-Inch | $297\u00a0\u00a0| 2/27/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nSpecifically, I wanted to address the shipping date for the Odyssey Ark 55-inch. I know the 10th of May 2024 is not ideal but there was confusion as the supplier is in Europe and thought that 5/10/2024 was actually 10/5/2024.\n\nI apologize for the confusion.\n\nRegards,\n\nLuke Inglis Operations Manager AI Models & MIT-IBM Watson AI Lab IBM Research\n\nAnswer the following question: Give me a markdown table of all the approvals needed for this purchase process, and what step the approval is needed. They should be ordered in two columns- Approval, Step"], "reference": ["|Approval\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|Step|\n|--------------------|----|\n|Approval from Line VP|3\u00a0\u00a0|\n|Approval from Manager|5\u00a0\u00a0|\n|Approval from 2nd line Manager|6\u00a0\u00a0|\n|Approval from Finance|7\u00a0\u00a0|\n"]} +{"qna_file": "compositional_skills/extraction/email/markdown/qna.yaml", "question_id": "24732452549826489294717919806750019044", "category": "taxonomy", "turns": ["Given the context below:\nSubject: High-Def Monitor Update\n\nDate:\n\nFriday, February 23, 2024 at 9:13:32$_{ }$PM Eastern Standard Time\n\nFrom:\n\nLuke Inglis\n\nTo:\n\nKate Soule\n\nHi Kate,\n\nI am writing to explain to you the delay in our lab receiving monitors. On Dec 2$^{nd,}$ 2023, I started the process of purchasing 7 high-def Samsung monitors for the MIT-IBM Watson AI Lab. I think you already know, but I wanted to outline the steps needed to make this purchase.\n\n1. Obtain a quote from Samsung\n\n2. Submit an Expense Pre-Approval\n\n3. Approval from Line VP\n\n4. Submit an Ad Hoc request to Procurement\n\n5. Approval from Manager\n\n6. Approval from 2$^{nd}$ line Manager\n\n7. Approval from Finance\n\n8. Procurement then identifies their specialist for this request\n\n9. The Procurement specialist then reviews the request and determines which supplier to use and reaches out to the contact person there\n\n10. These two finalize agreements needed to execute the order\n\n11. Monitors are then shipped to our location\n\nWe have now finally reached number 11 in this process. Below I wanted to outline what we are receiving and when they are expected to arrive.\n\n| Monitor\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Price\u00a0\u00a0| Estimated Arrival\u00a0\u00a0|\n|----------------------|---------|---------------------|\n| Odyssey Ark 55-inch\u00a0| $1,952\u00a0| 5/10/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G7 32-Inch\u00a0\u00a0| $669\u00a0\u00a0| 3/7/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G7 32-Inch\u00a0\u00a0| $669\u00a0\u00a0| 3/7/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G55C 27-Inch | $297\u00a0\u00a0| 2/27/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G55C 27-Inch | $297\u00a0\u00a0| 2/27/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nSpecifically, I wanted to address the shipping date for the Odyssey Ark 55-inch. I know the 10th of May 2024 is not ideal but there was confusion as the supplier is in Europe and thought that 5/10/2024 was actually 10/5/2024.\n\nI apologize for the confusion.\n\nRegards,\n\nLuke Inglis Operations Manager AI Models & MIT-IBM Watson AI Lab IBM Research\n\nAnswer the following question: Please provide a markdown table of all Monitors types and the dates they are being delivered, written in Month Day, Year format. The two column headers should be Type and Delivery date.\n"], "reference": ["|Type\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|Delivery date\u00a0\u00a0|\n|--------------------|----------------|\n|Odyssey Ark 55-Inch |May 10, 2024\u00a0\u00a0|\n|Odyssey G7 32-Inch\u00a0|March 7, 2024\u00a0\u00a0|\n|Odyssey G55C 27-Inch|Febuary 27, 2024|\n"]} +{"qna_file": "compositional_skills/extraction/email/reasoning/qna.yaml", "question_id": "60200243283513954707535099674425056625", "category": "taxonomy", "turns": ["Given the context below:\nSubject: High-Def Monitor Update\n\nDate:\n\nFriday, February 23, 2024 at 9:13:32$_{ }$PM Eastern Standard Time\n\nFrom:\n\nLuke Inglis\n\nTo:\n\nKate Soule\n\nHi Kate,\n\nI am writing to explain to you the delay in our lab receiving monitors. On Dec 2$^{nd,}$ 2023, I started the process of purchasing 7 high-def Samsung monitors for the MIT-IBM Watson AI Lab. I think you already know, but I wanted to outline the steps needed to make this purchase.\n\n1. Obtain a quote from Samsung\n\n2. Submit an Expense Pre-Approval\n\n3. Approval from Line VP\n\n4. Submit an Ad Hoc request to Procurement\n\n5. Approval from Manager\n\n6. Approval from 2$^{nd}$ line Manager\n\n7. Approval from Finance\n\n8. Procurement then identifies their specialist for this request\n\n9. The Procurement specialist then reviews the request and determines which supplier to use and reaches out to the contact person there\n\n10. These two finalize agreements needed to execute the order\n\n11. Monitors are then shipped to our location\n\nWe have now finally reached number 11 in this process. Below I wanted to outline what we are receiving and when they are expected to arrive.\n\n| Monitor\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Price\u00a0\u00a0| Estimated Arrival\u00a0\u00a0|\n|----------------------|---------|---------------------|\n| Odyssey Ark 55-inch\u00a0| $1,952\u00a0| 5/10/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G7 32-Inch\u00a0\u00a0| $669\u00a0\u00a0| 3/7/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G7 32-Inch\u00a0\u00a0| $669\u00a0\u00a0| 3/7/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G55C 27-Inch | $297\u00a0\u00a0| 2/27/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G55C 27-Inch | $297\u00a0\u00a0| 2/27/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nSpecifically, I wanted to address the shipping date for the Odyssey Ark 55-inch. I know the 10th of May 2024 is not ideal but there was confusion as the supplier is in Europe and thought that 5/10/2024 was actually 10/5/2024.\n\nI apologize for the confusion.\n\nRegards,\n\nLuke Inglis Operations Manager AI Models & MIT-IBM Watson AI Lab IBM Research\n\nAnswer the following question: What is the total price of the entire order?\n"], "reference": ["To determine the price of the whole order, we first need to understand the price of the individual items.\n\nAccording to the table, one Odyssey Ark 55-inch was purchased for $1,952, three Odyssey G7 32-Inch monitors were purchased for $669 each, and three Odyssey G55C 27-Inch monitors were purchased for $297 each.\n\nTo determine the total cost, we need to add the price of all the items together. 1,952 + 669 + 669 + 669 + 297 + 297 + 297 = 4,850. So the total cost for all items is $4,850. \n"]} +{"qna_file": "compositional_skills/extraction/email/reasoning/qna.yaml", "question_id": "215862669343498963725986735014651524514", "category": "taxonomy", "turns": ["Given the context below:\nSubject: High-Def Monitor Update\n\nDate:\n\nFriday, February 23, 2024 at 9:13:32$_{ }$PM Eastern Standard Time\n\nFrom:\n\nLuke Inglis\n\nTo:\n\nKate Soule\n\nHi Kate,\n\nI am writing to explain to you the delay in our lab receiving monitors. On Dec 2$^{nd,}$ 2023, I started the process of purchasing 7 high-def Samsung monitors for the MIT-IBM Watson AI Lab. I think you already know, but I wanted to outline the steps needed to make this purchase.\n\n1. Obtain a quote from Samsung\n\n2. Submit an Expense Pre-Approval\n\n3. Approval from Line VP\n\n4. Submit an Ad Hoc request to Procurement\n\n5. Approval from Manager\n\n6. Approval from 2$^{nd}$ line Manager\n\n7. Approval from Finance\n\n8. Procurement then identifies their specialist for this request\n\n9. The Procurement specialist then reviews the request and determines which supplier to use and reaches out to the contact person there\n\n10. These two finalize agreements needed to execute the order\n\n11. Monitors are then shipped to our location\n\nWe have now finally reached number 11 in this process. Below I wanted to outline what we are receiving and when they are expected to arrive.\n\n| Monitor\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Price\u00a0\u00a0| Estimated Arrival\u00a0\u00a0|\n|----------------------|---------|---------------------|\n| Odyssey Ark 55-inch\u00a0| $1,952\u00a0| 5/10/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G7 32-Inch\u00a0\u00a0| $669\u00a0\u00a0| 3/7/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G7 32-Inch\u00a0\u00a0| $669\u00a0\u00a0| 3/7/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G55C 27-Inch | $297\u00a0\u00a0| 2/27/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G55C 27-Inch | $297\u00a0\u00a0| 2/27/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nSpecifically, I wanted to address the shipping date for the Odyssey Ark 55-inch. I know the 10th of May 2024 is not ideal but there was confusion as the supplier is in Europe and thought that 5/10/2024 was actually 10/5/2024.\n\nI apologize for the confusion.\n\nRegards,\n\nLuke Inglis Operations Manager AI Models & MIT-IBM Watson AI Lab IBM Research\n\nAnswer the following question: How many days is it going to take before the MIT-IBM Watson AI Lab has received all items in this order? "], "reference": ["To determine how many days remain for all items to be delivered, we need to know the current date, and the delivery date for the final item. The number of days in between these two dates will be the days remaining for all items to be delivered.\n\nThe current date of the email is February 23, 2024. The delivery date for the final item is May 10, 2024. There are 77 days in-between February 23 and May 10, which means it will take a total of 77 days for all items to be delivered to the MIT-IBM Watson AI Lab.\n"]} +{"qna_file": "compositional_skills/extraction/email/reasoning/qna.yaml", "question_id": "101348486285295092973949491851774777550", "category": "taxonomy", "turns": ["Given the context below:\nSubject: High-Def Monitor Update\n\nDate:\n\nFriday, February 23, 2024 at 9:13:32$_{ }$PM Eastern Standard Time\n\nFrom:\n\nLuke Inglis\n\nTo:\n\nKate Soule\n\nHi Kate,\n\nI am writing to explain to you the delay in our lab receiving monitors. On Dec 2$^{nd,}$ 2023, I started the process of purchasing 7 high-def Samsung monitors for the MIT-IBM Watson AI Lab. I think you already know, but I wanted to outline the steps needed to make this purchase.\n\n1. Obtain a quote from Samsung\n\n2. Submit an Expense Pre-Approval\n\n3. Approval from Line VP\n\n4. Submit an Ad Hoc request to Procurement\n\n5. Approval from Manager\n\n6. Approval from 2$^{nd}$ line Manager\n\n7. Approval from Finance\n\n8. Procurement then identifies their specialist for this request\n\n9. The Procurement specialist then reviews the request and determines which supplier to use and reaches out to the contact person there\n\n10. These two finalize agreements needed to execute the order\n\n11. Monitors are then shipped to our location\n\nWe have now finally reached number 11 in this process. Below I wanted to outline what we are receiving and when they are expected to arrive.\n\n| Monitor\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Price\u00a0\u00a0| Estimated Arrival\u00a0\u00a0|\n|----------------------|---------|---------------------|\n| Odyssey Ark 55-inch\u00a0| $1,952\u00a0| 5/10/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G7 32-Inch\u00a0\u00a0| $669\u00a0\u00a0| 3/7/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G7 32-Inch\u00a0\u00a0| $669\u00a0\u00a0| 3/7/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G55C 27-Inch | $297\u00a0\u00a0| 2/27/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Odyssey G55C 27-Inch | $297\u00a0\u00a0| 2/27/2024\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\nSpecifically, I wanted to address the shipping date for the Odyssey Ark 55-inch. I know the 10th of May 2024 is not ideal but there was confusion as the supplier is in Europe and thought that 5/10/2024 was actually 10/5/2024.\n\nI apologize for the confusion.\n\nRegards,\n\nLuke Inglis Operations Manager AI Models & MIT-IBM Watson AI Lab IBM Research\n\nAnswer the following question: If the Odyssey Ark 55-Inch order was cancelled, how many additional Odyssey G55C 27-Inch monitors could be afforded?\n"], "reference": ["To determine how many Odyssey G55C 27-Inch monitors could be afforded if the Odyssey Ark 55-Inch order was cancelled, we first need to know the purchase price of the individual items.\n\nAccording to the table, the Odyssey Ark-55-Inch was prices at $1,952, and the Odyssey G55C 27-Inch monitors were priced at $297 each. \n\nTo determine how many additional Odyssey G55C 27-Inch monitors could be afforded if the Odyssey Ark-55-Inch was cancelled, we need to divide the price of the Odyssey Ark-55-Inch by the price of the Odyssey G55C 27-Inch monitors. 1,952 divided by 297 is 6.57. \n\nThis means 6 additional Odyssey G55C 27-Inch monitors could be afforded by spending $1,782 out of our $1,952 budget, with $170 left over.\u00a0\n"]} +{"qna_file": "compositional_skills/extraction/invoice/bullet_points/qna.yaml", "question_id": "45409840875743015394454644306760327137", "category": "taxonomy", "turns": ["Given the context below:\n## INVOICE\n\n555 5 $^{th}$ st Boston, Ma 01234 Phone: 123-456-7890\n\n## PURCHASED BY:\n\nJohn Doe J&J corp 777 77 $^{th}$ st Newark, Nj 56789 Phone: 111-222-3333\n\n## COMMENTS OR SPECIAL INSTRUCTIONS:\n\nDue upon receipt\n\n| QUANTITY\u00a0\u00a0| DESCRIPTION\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| UNIT PRICE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL\u00a0\u00a0\u00a0|\n|------------|------------------------------|-----------------------|-----------|\n| 1 TB\u00a0\u00a0\u00a0\u00a0| Cloud service\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Odyssey Ark 55-inch Monitors | 1,952\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 52,704\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Keyboards\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 32\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 864\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Mice\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 11\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 297\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Subtotal\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 53,964.99 |\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sales tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 3,237.90\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Shipping and handling | 0.00\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL DUE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 57,202.89 |\n\nINVOICE # 21\n\nDATE: 2/21/2024\n\nSHIP TO:\n\nJane Doe\n\nJ&J corp\n\n777 77 $^{th}$ st\n\nNewark, Nj 56789\n\nPhone: 111-222-3333\n\nAnswer the following question: Give me bullets of all the things i bought.\n"], "reference": ["Here is your itemized list of purchased items:\n- 1 TB Cloud service\n- 27 Odyssey Ark 55-inch Monitors\n- 27 Standard Keyboards\n- 27 Standard Mice\n\n"]} +{"qna_file": "compositional_skills/extraction/invoice/bullet_points/qna.yaml", "question_id": "278170878244105571214297707777407802890", "category": "taxonomy", "turns": ["Given the context below:\n## INVOICE\n\n555 5 $^{th}$ st Boston, Ma 01234 Phone: 123-456-7890\n\n## PURCHASED BY:\n\nJohn Doe J&J corp 777 77 $^{th}$ st Newark, Nj 56789 Phone: 111-222-3333\n\n## COMMENTS OR SPECIAL INSTRUCTIONS:\n\nDue upon receipt\n\n| QUANTITY\u00a0\u00a0| DESCRIPTION\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| UNIT PRICE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL\u00a0\u00a0\u00a0|\n|------------|------------------------------|-----------------------|-----------|\n| 1 TB\u00a0\u00a0\u00a0\u00a0| Cloud service\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Odyssey Ark 55-inch Monitors | 1,952\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 52,704\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Keyboards\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 32\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 864\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Mice\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 11\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 297\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Subtotal\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 53,964.99 |\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sales tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 3,237.90\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Shipping and handling | 0.00\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL DUE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 57,202.89 |\n\nINVOICE # 21\n\nDATE: 2/21/2024\n\nSHIP TO:\n\nJane Doe\n\nJ&J corp\n\n777 77 $^{th}$ st\n\nNewark, Nj 56789\n\nPhone: 111-222-3333\n\nAnswer the following question: Return a bulleted list of the people in this invoice and their roles in parentheses. "], "reference": ["Here are the people included in this invoice: \n- John Doe (purchaser)\n- Jane Doe (receiver)\n"]} +{"qna_file": "compositional_skills/extraction/invoice/bullet_points/qna.yaml", "question_id": "265458243418898634896611063913987815077", "category": "taxonomy", "turns": ["Given the context below:\n## INVOICE\n\n555 5 $^{th}$ st Boston, Ma 01234 Phone: 123-456-7890\n\n## PURCHASED BY:\n\nJohn Doe J&J corp 777 77 $^{th}$ st Newark, Nj 56789 Phone: 111-222-3333\n\n## COMMENTS OR SPECIAL INSTRUCTIONS:\n\nDue upon receipt\n\n| QUANTITY\u00a0\u00a0| DESCRIPTION\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| UNIT PRICE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL\u00a0\u00a0\u00a0|\n|------------|------------------------------|-----------------------|-----------|\n| 1 TB\u00a0\u00a0\u00a0\u00a0| Cloud service\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Odyssey Ark 55-inch Monitors | 1,952\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 52,704\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Keyboards\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 32\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 864\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Mice\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 11\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 297\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Subtotal\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 53,964.99 |\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sales tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 3,237.90\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Shipping and handling | 0.00\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL DUE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 57,202.89 |\n\nINVOICE # 21\n\nDATE: 2/21/2024\n\nSHIP TO:\n\nJane Doe\n\nJ&J corp\n\n777 77 $^{th}$ st\n\nNewark, Nj 56789\n\nPhone: 111-222-3333\n\nAnswer the following question: Create a bulleted list. The first bullet should include the total quantity of monitors, keyboards, and mice. The second should summarize the total cost for these three types of items. The third should include an updated subtotal assuming the cloud service was not included in the subtotal.\n"], "reference": ["Here's the bulleted list you requested:\n- 81 monitors, keyboards, and mice\n- $53865\n- $53865\n"]} +{"qna_file": "compositional_skills/extraction/invoice/plain_text/qna.yaml", "question_id": "49841582254186874830289397012358691042", "category": "taxonomy", "turns": ["Given the context below:\n## INVOICE\n\n555 5 $^{th}$ st Boston, Ma 01234 Phone: 123-456-7890\n\n## PURCHASED BY:\n\nJohn Doe J&J corp 777 77 $^{th}$ st Newark, Nj 56789 Phone: 111-222-3333\n\n## COMMENTS OR SPECIAL INSTRUCTIONS:\n\nDue upon receipt\n\n| QUANTITY\u00a0\u00a0| DESCRIPTION\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| UNIT PRICE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL\u00a0\u00a0\u00a0|\n|------------|------------------------------|-----------------------|-----------|\n| 1 TB\u00a0\u00a0\u00a0\u00a0| Cloud service\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Odyssey Ark 55-inch Monitors | 1,952\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 52,704\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Keyboards\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 32\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 864\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Mice\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 11\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 297\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Subtotal\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 53,964.99 |\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sales tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 3,237.90\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Shipping and handling | 0.00\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL DUE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 57,202.89 |\n\nINVOICE # 21\n\nDATE: 2/21/2024\n\nSHIP TO:\n\nJane Doe\n\nJ&J corp\n\n777 77 $^{th}$ st\n\nNewark, Nj 56789\n\nPhone: 111-222-3333\n\nAnswer the following question: What are the 4 things purchased?\n"], "reference": ["Cloud service, Odyssey Ark 55-inch Monitors, Standard Keyboards, and Standard Mice.\n"]} +{"qna_file": "compositional_skills/extraction/invoice/plain_text/qna.yaml", "question_id": "150968467791341968780054062483093317936", "category": "taxonomy", "turns": ["Given the context below:\n## INVOICE\n\n555 5 $^{th}$ st Boston, Ma 01234 Phone: 123-456-7890\n\n## PURCHASED BY:\n\nJohn Doe J&J corp 777 77 $^{th}$ st Newark, Nj 56789 Phone: 111-222-3333\n\n## COMMENTS OR SPECIAL INSTRUCTIONS:\n\nDue upon receipt\n\n| QUANTITY\u00a0\u00a0| DESCRIPTION\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| UNIT PRICE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL\u00a0\u00a0\u00a0|\n|------------|------------------------------|-----------------------|-----------|\n| 1 TB\u00a0\u00a0\u00a0\u00a0| Cloud service\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Odyssey Ark 55-inch Monitors | 1,952\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 52,704\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Keyboards\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 32\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 864\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Mice\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 11\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 297\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Subtotal\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 53,964.99 |\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sales tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 3,237.90\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Shipping and handling | 0.00\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL DUE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 57,202.89 |\n\nINVOICE # 21\n\nDATE: 2/21/2024\n\nSHIP TO:\n\nJane Doe\n\nJ&J corp\n\n777 77 $^{th}$ st\n\nNewark, Nj 56789\n\nPhone: 111-222-3333\n\nAnswer the following question: If we added 3 more keyboards to the order, how many would we purchase in total? "], "reference": ["30 Standard Keyboards\n"]} +{"qna_file": "compositional_skills/extraction/invoice/plain_text/qna.yaml", "question_id": "85506119997333552028615110273911095542", "category": "taxonomy", "turns": ["Given the context below:\n## INVOICE\n\n555 5 $^{th}$ st Boston, Ma 01234 Phone: 123-456-7890\n\n## PURCHASED BY:\n\nJohn Doe J&J corp 777 77 $^{th}$ st Newark, Nj 56789 Phone: 111-222-3333\n\n## COMMENTS OR SPECIAL INSTRUCTIONS:\n\nDue upon receipt\n\n| QUANTITY\u00a0\u00a0| DESCRIPTION\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| UNIT PRICE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL\u00a0\u00a0\u00a0|\n|------------|------------------------------|-----------------------|-----------|\n| 1 TB\u00a0\u00a0\u00a0\u00a0| Cloud service\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Odyssey Ark 55-inch Monitors | 1,952\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 52,704\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Keyboards\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 32\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 864\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Mice\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 11\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 297\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Subtotal\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 53,964.99 |\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sales tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 3,237.90\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Shipping and handling | 0.00\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL DUE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 57,202.89 |\n\nINVOICE # 21\n\nDATE: 2/21/2024\n\nSHIP TO:\n\nJane Doe\n\nJ&J corp\n\n777 77 $^{th}$ st\n\nNewark, Nj 56789\n\nPhone: 111-222-3333\n\nAnswer the following question: What is the phone number of the purchaser?\n"], "reference": ["111-222-3333\n"]} +{"qna_file": "compositional_skills/extraction/invoice/markdown/qna.yaml", "question_id": "230272578869709299057559966308849259389", "category": "taxonomy", "turns": ["Given the context below:\n## INVOICE\n\n555 5 $^{th}$ st Boston, Ma 01234 Phone: 123-456-7890\n\n## PURCHASED BY:\n\nJohn Doe J&J corp 777 77 $^{th}$ st Newark, Nj 56789 Phone: 111-222-3333\n\n## COMMENTS OR SPECIAL INSTRUCTIONS:\n\nDue upon receipt\n\n| QUANTITY\u00a0\u00a0| DESCRIPTION\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| UNIT PRICE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL\u00a0\u00a0\u00a0|\n|------------|------------------------------|-----------------------|-----------|\n| 1 TB\u00a0\u00a0\u00a0\u00a0| Cloud service\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Odyssey Ark 55-inch Monitors | 1,952\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 52,704\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Keyboards\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 32\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 864\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Mice\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 11\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 297\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Subtotal\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 53,964.99 |\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sales tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 3,237.90\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Shipping and handling | 0.00\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL DUE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 57,202.89 |\n\nINVOICE # 21\n\nDATE: 2/21/2024\n\nSHIP TO:\n\nJane Doe\n\nJ&J corp\n\n777 77 $^{th}$ st\n\nNewark, Nj 56789\n\nPhone: 111-222-3333\n\nAnswer the following question: Create a simple table in markdown with each of the addresses included in the file. Each address should be in a column.\n"], "reference": ["```\n| Address 1 | Address 2 | Address 3 |\n| ------------------- | ------------------- | ------------------- |\n| 555 5th st | 777 77th st | 777 77th st |\n| Boston, MA 01234 | Newark, Nj 56789 | Newark, Nj 56789 |\n| Phone: 123-456-7890 | Phone: 111-222-3333 | Phone: 111-222-3333 |\n```\n\n"]} +{"qna_file": "compositional_skills/extraction/invoice/markdown/qna.yaml", "question_id": "141512130825622951488607529691359886782", "category": "taxonomy", "turns": ["Given the context below:\n## INVOICE\n\n555 5 $^{th}$ st Boston, Ma 01234 Phone: 123-456-7890\n\n## PURCHASED BY:\n\nJohn Doe J&J corp 777 77 $^{th}$ st Newark, Nj 56789 Phone: 111-222-3333\n\n## COMMENTS OR SPECIAL INSTRUCTIONS:\n\nDue upon receipt\n\n| QUANTITY\u00a0\u00a0| DESCRIPTION\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| UNIT PRICE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL\u00a0\u00a0\u00a0|\n|------------|------------------------------|-----------------------|-----------|\n| 1 TB\u00a0\u00a0\u00a0\u00a0| Cloud service\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Odyssey Ark 55-inch Monitors | 1,952\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 52,704\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Keyboards\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 32\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 864\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Mice\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 11\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 297\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Subtotal\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 53,964.99 |\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sales tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 3,237.90\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Shipping and handling | 0.00\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL DUE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 57,202.89 |\n\nINVOICE # 21\n\nDATE: 2/21/2024\n\nSHIP TO:\n\nJane Doe\n\nJ&J corp\n\n777 77 $^{th}$ st\n\nNewark, Nj 56789\n\nPhone: 111-222-3333\n\nAnswer the following question: Make me a new table that includes the subtotal, sales tax, shipping and handling, and total due. Make this table in markdown format."], "reference": ["```\n| Subtotal | Sales Tax | Shipping and handling | total due |\n| -------- | --------- | --------------------- | --------- |\n| 53964.99 | 3237.9 | 0 | 57202.89 |\n```\n\n"]} +{"qna_file": "compositional_skills/extraction/invoice/markdown/qna.yaml", "question_id": "336179686029754215622149121866148614576", "category": "taxonomy", "turns": ["Given the context below:\n## INVOICE\n\n555 5 $^{th}$ st Boston, Ma 01234 Phone: 123-456-7890\n\n## PURCHASED BY:\n\nJohn Doe J&J corp 777 77 $^{th}$ st Newark, Nj 56789 Phone: 111-222-3333\n\n## COMMENTS OR SPECIAL INSTRUCTIONS:\n\nDue upon receipt\n\n| QUANTITY\u00a0\u00a0| DESCRIPTION\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| UNIT PRICE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL\u00a0\u00a0\u00a0|\n|------------|------------------------------|-----------------------|-----------|\n| 1 TB\u00a0\u00a0\u00a0\u00a0| Cloud service\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Odyssey Ark 55-inch Monitors | 1,952\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 52,704\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Keyboards\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 32\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 864\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Mice\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 11\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 297\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Subtotal\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 53,964.99 |\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sales tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 3,237.90\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Shipping and handling | 0.00\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL DUE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 57,202.89 |\n\nINVOICE # 21\n\nDATE: 2/21/2024\n\nSHIP TO:\n\nJane Doe\n\nJ&J corp\n\n777 77 $^{th}$ st\n\nNewark, Nj 56789\n\nPhone: 111-222-3333\n\nAnswer the following question: Create a new markdown table with just the physical items shipped.\n"], "reference": ["```\n| QUANTITY | DESCRIPTION | UNIT PRICE | TOTAL |\n| -------- | ---------------------------- | ---------- | ------ |\n| 27 | Odyssey Ark 55-inch Monitors | 1,952 | 52,704 |\n| 27 | Standard Keyboards | 32 | 864 |\n| 27 | Standard Mice | 11 | 297 |\n```\n\n"]} +{"qna_file": "compositional_skills/extraction/invoice/reasoning/qna.yaml", "question_id": "229006503744579811916346052373628544746", "category": "taxonomy", "turns": ["Given the context below:\n## INVOICE\n\n555 5 $^{th}$ st Boston, Ma 01234 Phone: 123-456-7890\n\n## PURCHASED BY:\n\nJohn Doe J&J corp 777 77 $^{th}$ st Newark, Nj 56789 Phone: 111-222-3333\n\n## COMMENTS OR SPECIAL INSTRUCTIONS:\n\nDue upon receipt\n\n| QUANTITY\u00a0\u00a0| DESCRIPTION\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| UNIT PRICE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL\u00a0\u00a0\u00a0|\n|------------|------------------------------|-----------------------|-----------|\n| 1 TB\u00a0\u00a0\u00a0\u00a0| Cloud service\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Odyssey Ark 55-inch Monitors | 1,952\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 52,704\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Keyboards\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 32\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 864\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Mice\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 11\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 297\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Subtotal\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 53,964.99 |\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sales tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 3,237.90\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Shipping and handling | 0.00\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL DUE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 57,202.89 |\n\nINVOICE # 21\n\nDATE: 2/21/2024\n\nSHIP TO:\n\nJane Doe\n\nJ&J corp\n\n777 77 $^{th}$ st\n\nNewark, Nj 56789\n\nPhone: 111-222-3333\n\nAnswer the following question: If we added another TB to the cloud service, what would our new subtotal be?\n"], "reference": ["According to this invoice, 1 TB of Cloud service costs 99.99. If we add another TB, that would be 2 TB total. The total for 2 TB of Cloud service would be 199.98. The other items remain unchanged. This means the subtotal would add the following: 199.98 + 52704 + 864 + 297. The new subtotal would be 54064.98.\n"]} +{"qna_file": "compositional_skills/extraction/invoice/reasoning/qna.yaml", "question_id": "111598323539657483476058930824369896103", "category": "taxonomy", "turns": ["Given the context below:\n## INVOICE\n\n555 5 $^{th}$ st Boston, Ma 01234 Phone: 123-456-7890\n\n## PURCHASED BY:\n\nJohn Doe J&J corp 777 77 $^{th}$ st Newark, Nj 56789 Phone: 111-222-3333\n\n## COMMENTS OR SPECIAL INSTRUCTIONS:\n\nDue upon receipt\n\n| QUANTITY\u00a0\u00a0| DESCRIPTION\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| UNIT PRICE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL\u00a0\u00a0\u00a0|\n|------------|------------------------------|-----------------------|-----------|\n| 1 TB\u00a0\u00a0\u00a0\u00a0| Cloud service\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Odyssey Ark 55-inch Monitors | 1,952\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 52,704\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Keyboards\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 32\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 864\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Mice\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 11\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 297\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Subtotal\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 53,964.99 |\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sales tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 3,237.90\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Shipping and handling | 0.00\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL DUE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 57,202.89 |\n\nINVOICE # 21\n\nDATE: 2/21/2024\n\nSHIP TO:\n\nJane Doe\n\nJ&J corp\n\n777 77 $^{th}$ st\n\nNewark, Nj 56789\n\nPhone: 111-222-3333\n\nAnswer the following question: If the customer needed to reduce their total spend below 57000, but only want to remove 1 item from the shipment, what would you suggest?"], "reference": ["The current total due is 57202.89. We want to reduce the total due by 202.89 because 57202.89-57000 is 202.89. This means we need to find the item that costs more than 202.89 per item. The only item that costs more than 202.89 is the Odyssey Ark 55-inch Monitors at 1952 per unit. So the suggestion would be to remove a monitor.\n"]} +{"qna_file": "compositional_skills/extraction/invoice/reasoning/qna.yaml", "question_id": "212112715407228073280724014998630720136", "category": "taxonomy", "turns": ["Given the context below:\n## INVOICE\n\n555 5 $^{th}$ st Boston, Ma 01234 Phone: 123-456-7890\n\n## PURCHASED BY:\n\nJohn Doe J&J corp 777 77 $^{th}$ st Newark, Nj 56789 Phone: 111-222-3333\n\n## COMMENTS OR SPECIAL INSTRUCTIONS:\n\nDue upon receipt\n\n| QUANTITY\u00a0\u00a0| DESCRIPTION\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| UNIT PRICE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL\u00a0\u00a0\u00a0|\n|------------|------------------------------|-----------------------|-----------|\n| 1 TB\u00a0\u00a0\u00a0\u00a0| Cloud service\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Odyssey Ark 55-inch Monitors | 1,952\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 52,704\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Keyboards\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 32\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 864\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Mice\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 11\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 297\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Subtotal\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 53,964.99 |\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sales tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 3,237.90\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Shipping and handling | 0.00\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL DUE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 57,202.89 |\n\nINVOICE # 21\n\nDATE: 2/21/2024\n\nSHIP TO:\n\nJane Doe\n\nJ&J corp\n\n777 77 $^{th}$ st\n\nNewark, Nj 56789\n\nPhone: 111-222-3333\n\nAnswer the following question: Could the invoice be due before 1/1/2024?\n"], "reference": ["No, because the date of the invoice is 2/21/2024. The due date has to be later than the invoice date.\n"]} +{"qna_file": "compositional_skills/extraction/invoice/csv/qna.yaml", "question_id": "103176431997531473254316563738223841767", "category": "taxonomy", "turns": ["Given the context below:\n## INVOICE\n\n555 5 $^{th}$ st Boston, Ma 01234 Phone: 123-456-7890\n\n## PURCHASED BY:\n\nJohn Doe J&J corp 777 77 $^{th}$ st Newark, Nj 56789 Phone: 111-222-3333\n\n## COMMENTS OR SPECIAL INSTRUCTIONS:\n\nDue upon receipt\n\n| QUANTITY\u00a0\u00a0| DESCRIPTION\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| UNIT PRICE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL\u00a0\u00a0\u00a0|\n|------------|------------------------------|-----------------------|-----------|\n| 1 TB\u00a0\u00a0\u00a0\u00a0| Cloud service\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Odyssey Ark 55-inch Monitors | 1,952\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 52,704\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Keyboards\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 32\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 864\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Mice\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 11\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 297\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Subtotal\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 53,964.99 |\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sales tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 3,237.90\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Shipping and handling | 0.00\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL DUE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 57,202.89 |\n\nINVOICE # 21\n\nDATE: 2/21/2024\n\nSHIP TO:\n\nJane Doe\n\nJ&J corp\n\n777 77 $^{th}$ st\n\nNewark, Nj 56789\n\nPhone: 111-222-3333\n\nAnswer the following question: Create a csv of the receipt\n"], "reference": ["Quantity,Description,Unit Price,Total\n1 TB,Cloud service,99.99,99.99\n27,Odyssey Ark 55-inch Monitors,1952,52704\n27,Standard Keyboards,32,864\n27,Standard Mice,11,297\n,,,\n,,,\n,,,\n,,,\n,,Subtotal,53964.99\n,,Sales tax,3237.90\n,,Shipping and handling,0\n,,TOTAL DUE,57202.89\n"]} +{"qna_file": "compositional_skills/extraction/invoice/csv/qna.yaml", "question_id": "51419092233455481473958265932075216735", "category": "taxonomy", "turns": ["Given the context below:\n## INVOICE\n\n555 5 $^{th}$ st Boston, Ma 01234 Phone: 123-456-7890\n\n## PURCHASED BY:\n\nJohn Doe J&J corp 777 77 $^{th}$ st Newark, Nj 56789 Phone: 111-222-3333\n\n## COMMENTS OR SPECIAL INSTRUCTIONS:\n\nDue upon receipt\n\n| QUANTITY\u00a0\u00a0| DESCRIPTION\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| UNIT PRICE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL\u00a0\u00a0\u00a0|\n|------------|------------------------------|-----------------------|-----------|\n| 1 TB\u00a0\u00a0\u00a0\u00a0| Cloud service\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Odyssey Ark 55-inch Monitors | 1,952\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 52,704\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Keyboards\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 32\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 864\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Mice\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 11\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 297\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Subtotal\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 53,964.99 |\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sales tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 3,237.90\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Shipping and handling | 0.00\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL DUE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 57,202.89 |\n\nINVOICE # 21\n\nDATE: 2/21/2024\n\nSHIP TO:\n\nJane Doe\n\nJ&J corp\n\n777 77 $^{th}$ st\n\nNewark, Nj 56789\n\nPhone: 111-222-3333\n\nAnswer the following question: create a table in .csv that contains the purchased by in one column and the ship to in another column"], "reference": ["PURCHASED BY:,SHIP TO:\nJohn Doe,Jane Doe\nJ&J corp, J&J corp\n777 77th st,777 77th st\nNewark Nj 56789\",Newark Nj 56789\nPhone: 111-222-3333,Phone: 111-222-3333\n"]} +{"qna_file": "compositional_skills/extraction/invoice/csv/qna.yaml", "question_id": "169345296333033893706639096369222043519", "category": "taxonomy", "turns": ["Given the context below:\n## INVOICE\n\n555 5 $^{th}$ st Boston, Ma 01234 Phone: 123-456-7890\n\n## PURCHASED BY:\n\nJohn Doe J&J corp 777 77 $^{th}$ st Newark, Nj 56789 Phone: 111-222-3333\n\n## COMMENTS OR SPECIAL INSTRUCTIONS:\n\nDue upon receipt\n\n| QUANTITY\u00a0\u00a0| DESCRIPTION\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| UNIT PRICE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL\u00a0\u00a0\u00a0|\n|------------|------------------------------|-----------------------|-----------|\n| 1 TB\u00a0\u00a0\u00a0\u00a0| Cloud service\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Odyssey Ark 55-inch Monitors | 1,952\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 52,704\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Keyboards\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 32\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 864\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Mice\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 11\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 297\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Subtotal\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 53,964.99 |\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sales tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 3,237.90\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Shipping and handling | 0.00\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL DUE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 57,202.89 |\n\nINVOICE # 21\n\nDATE: 2/21/2024\n\nSHIP TO:\n\nJane Doe\n\nJ&J corp\n\n777 77 $^{th}$ st\n\nNewark, Nj 56789\n\nPhone: 111-222-3333\n\nAnswer the following question: change the form to add $25 in shipping and handling, update the total due, and return it in csv format\n"], "reference": ["Quantity,Description,Unit Price,Total\n1 TB,Cloud service,99.99,99.99\n27,Odyssey Ark 55-inch Monitors,1952,52704\n27,Standard Keyboards,32,864\n27,Standard Mice,11,297\n,,,\n,,,\n,,,\n,,,\n,,Subtotal,53964.99\n,,Sales tax,3237.90\n,,Shipping and handling,25\n,,TOTAL DUE,57227.89\n"]} +{"qna_file": "compositional_skills/extraction/invoice/csv/qna.yaml", "question_id": "103176431997531473254316563738223841767", "category": "taxonomy", "turns": ["Given the context below:\n## INVOICE\n\n555 5 $^{th}$ st Boston, Ma 01234 Phone: 123-456-7890\n\n## PURCHASED BY:\n\nJohn Doe J&J corp 777 77 $^{th}$ st Newark, Nj 56789 Phone: 111-222-3333\n\n## COMMENTS OR SPECIAL INSTRUCTIONS:\n\nDue upon receipt\n\n| QUANTITY\u00a0\u00a0| DESCRIPTION\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| UNIT PRICE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL\u00a0\u00a0\u00a0|\n|------------|------------------------------|-----------------------|-----------|\n| 1 TB\u00a0\u00a0\u00a0\u00a0| Cloud service\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Odyssey Ark 55-inch Monitors | 1,952\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 52,704\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Keyboards\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 32\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 864\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Mice\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 11\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 297\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Subtotal\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 53,964.99 |\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sales tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 3,237.90\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Shipping and handling | 0.00\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL DUE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 57,202.89 |\n\nINVOICE # 21\n\nDATE: 2/21/2024\n\nSHIP TO:\n\nJane Doe\n\nJ&J corp\n\n777 77 $^{th}$ st\n\nNewark, Nj 56789\n\nPhone: 111-222-3333\n\nAnswer the following question: Create a csv of the receipt\n"], "reference": ["Quantity,Description,Unit Price,Total\n1 TB,Cloud service,99.99,99.99\n27,Odyssey Ark 55-inch Monitors,1952,52704\n27,Standard Keyboards,32,864\n27,Standard Mice,11,297\n,,,\n,,,\n,,,\n,,,\n,,Subtotal,53964.99\n,,Sales tax,3237.90\n,,Shipping and handling,0\n,,TOTAL DUE,57202.89\n"]} +{"qna_file": "compositional_skills/extraction/invoice/csv/qna.yaml", "question_id": "51419092233455481473958265932075216735", "category": "taxonomy", "turns": ["Given the context below:\n## INVOICE\n\n555 5 $^{th}$ st Boston, Ma 01234 Phone: 123-456-7890\n\n## PURCHASED BY:\n\nJohn Doe J&J corp 777 77 $^{th}$ st Newark, Nj 56789 Phone: 111-222-3333\n\n## COMMENTS OR SPECIAL INSTRUCTIONS:\n\nDue upon receipt\n\n| QUANTITY\u00a0\u00a0| DESCRIPTION\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| UNIT PRICE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL\u00a0\u00a0\u00a0|\n|------------|------------------------------|-----------------------|-----------|\n| 1 TB\u00a0\u00a0\u00a0\u00a0| Cloud service\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Odyssey Ark 55-inch Monitors | 1,952\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 52,704\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Keyboards\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 32\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 864\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Mice\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 11\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 297\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Subtotal\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 53,964.99 |\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sales tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 3,237.90\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Shipping and handling | 0.00\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL DUE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 57,202.89 |\n\nINVOICE # 21\n\nDATE: 2/21/2024\n\nSHIP TO:\n\nJane Doe\n\nJ&J corp\n\n777 77 $^{th}$ st\n\nNewark, Nj 56789\n\nPhone: 111-222-3333\n\nAnswer the following question: create a table in .csv that contains the purchased by in one column and the ship to in another column"], "reference": ["PURCHASED BY:,SHIP TO:\nJohn Doe,Jane Doe\nJ&J corp, J&J corp\n777 77th st,777 77th st\nNewark Nj 56789\",Newark Nj 56789\nPhone: 111-222-3333,Phone: 111-222-3333\n"]} +{"qna_file": "compositional_skills/extraction/invoice/csv/qna.yaml", "question_id": "79567265098034670845331833982179109838", "category": "taxonomy", "turns": ["Given the context below:\n## INVOICE\n\n555 5 $^{th}$ st Boston, Ma 01234 Phone: 123-456-7890\n\n## PURCHASED BY:\n\nJohn Doe J&J corp 777 77 $^{th}$ st Newark, Nj 56789 Phone: 111-222-3333\n\n## COMMENTS OR SPECIAL INSTRUCTIONS:\n\nDue upon receipt\n\n| QUANTITY\u00a0\u00a0| DESCRIPTION\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| UNIT PRICE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL\u00a0\u00a0\u00a0|\n|------------|------------------------------|-----------------------|-----------|\n| 1 TB\u00a0\u00a0\u00a0\u00a0| Cloud service\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 99.99\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Odyssey Ark 55-inch Monitors | 1,952\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 52,704\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Keyboards\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 32\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 864\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0| Standard Mice\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 11\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 297\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Subtotal\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 53,964.99 |\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sales tax\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 3,237.90\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Shipping and handling | 0.00\u00a0\u00a0\u00a0|\n| 27\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| TOTAL DUE\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| 57,202.89 |\n\nINVOICE # 21\n\nDATE: 2/21/2024\n\nSHIP TO:\n\nJane Doe\n\nJ&J corp\n\n777 77 $^{th}$ st\n\nNewark, Nj 56789\n\nPhone: 111-222-3333\n\nAnswer the following question: change the form to add $25 in shipping and handling, update the total due, and return it in csv format.\n"], "reference": ["Quantity,Description,Unit Price,Total\n1 TB,Cloud service,99.99,99.99\n27,Odyssey Ark 55-inch Monitors,1952,52704\n27,Standard Keyboards,32,864\n27,Standard Mice,11,297\n,,,\n,,,\n,,,\n,,,\n,,Subtotal,53964.99\n,,Sales tax,3237.90\n,,Shipping and handling,25\n,,TOTAL DUE,57227.89\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/abstract/qna.yaml", "question_id": "64746435403280260962803775949376691362", "category": "taxonomy", "turns": ["Describe the differences between anorexia nervosa and bulimia nervosa. Explain like you are a 70's Gangster\n"], "reference": ["Hey there, doc! So, you're askin' me about them fancy eatin' disorders, huh? Alright, let's break it down, 70's style.\\n\\nAnorexia nervosa, that's when a cat's so scared of packin' on the pounds, they start eatin' next to nothin'. They look in the mirror and all they see is flab, even when they're skin and bones. It's like they're hooked on bein' thin, and it's a real heavy trip, man.\\n\\nNow, bulimia nervosa, that's a whole 'nother ball game. These cool cats eat like there's no tomorrow, but then they start feelin' guilty. So, they purge it all out, like they're flushin' a toilet. Vomitin', using laxatives, or exercisin' too much \u2013 you name it, they do it. But the thing is, they can't shake the need to eat, so it's this vicious cycle, man.\\n\\nBoth of these disorders are serious business, and they can really mess a person up. So, if you know someone goin' through this, make sure they get the help they need, pronto. Peace out, and keep it real!\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/abstract/qna.yaml", "question_id": "253459074451793694174094480639092777745", "category": "taxonomy", "turns": ["If you were a hip-hop artist. Is it morally right to use the concept of self-defense and what are the arguments for and against it?\n"], "reference": ["Yo, yo, yo! Listen up, 'cause I'm about to drop some knowledge on y'all about moral disputes and self-defense. Now, I ain't no saint, but I know what's right and what's wrong, you feel me?\\n\\nSo, is it morally right to use self-defense? Well, let's break it down.\\n\\nOn one hand, you got folks arguin' that it's a natural instinct to protect yourself, your loved ones, and your community. I mean, if someone's tryna hurt you, ain't it only right to fight back? That's the argument for self-defense. It's about standin' up for what's important and survivin'.\\n\\nBut then again, there's the other side of the coin. Some folks say that resortin' to violence only perpetuates more violence, and it ain't never the solution. They argue that there's always another way, like walkin' away or callin' the authorities.\\n\\nNow, I ain't here to tell you what's right or wrong, 'cause that's for you to decide. But I'll leave you with this: always think before you act, and consider the consequences. Peace, love, and unity, my peeps!\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/abstract/qna.yaml", "question_id": "16454519019618557466427573382515806709", "category": "taxonomy", "turns": ["Assume the role of a Pirate Captain, now answer the following question: How does genetics contribute to the development of genetic counseling for couples with a family history of genetic disorders?\n"], "reference": ["Arr mateys, ye be askin' a fine question about the mysteries of the human body and its genetic code! Genetic counselin', yarr, it be a crucial tool for couples with a family history of genetic disorders.\\n\\nYe see, our bodies be like a grand ship, built from the blueprints of our DNA. Sometimes, there be errors in these blueprints, like a ship with a leaky hull or a broken compass. These errors, or mutations, can lead to genetic disorders passed down through families.\\n\\nNow, genetic counselors be the navigators of this complex sea. They study the genetic makeup of families and help couples understand their risks. By examin' the genetic history of both partners, they can predict the likelihood of passin' on these disorders to their offspring.\\n\\nIn this way, genetics be the compass guiding the work of genetic counselors. It allows them to steer clear of potential dangers and help couples make informed decisions about their family's future.\\n\\nSo ye see, mateys, genetics be the guiding star of genetic counselin'. It be a powerful tool for navigatin' the vast ocean of our genetic makeup, helpin' us avoid the treacherous waters of genetic disorders and chart a course for healthier generations to come. Ahoy, and fair winds to ye!\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/abstract/qna.yaml", "question_id": "87037018068243806930235984382251035206", "category": "taxonomy", "turns": ["You are a social media influencer. Can you explain the significance of the discovery of ancient civilizations, such as the Sumerians and Egyptians?\n"], "reference": ["Oh, hey there, friends! Let's chat about ancient civilizations, like the Sumerians and Egyptians, and why they're so important in understanding our past. \ud83c\udfdb\ufe0f\ud83d\udcab\\n\\nFirst off, these discoveries are like the OG content of human history! \ud83d\udcdc They give us a glimpse into how our ancestors lived, thought, and created, like, WAY before Instagram or TikTok even existed. Can you imagine? \ud83e\udd2f\\n\\nThe Sumerians, for example, were the ones who invented the earliest form of writing \u2013 cuneiform! \ud83d\udcdd It's like they were the first influencers to use hashtags, only instead of #NoFilter, they had #ClayTablets. And guess what? We've learned so much about their culture, laws, and literature thanks to this groundbreaking invention! \ud83d\udca1\\n\\nNow, let's talk about the Egyptians, who built those iconic pyramids that still leave us in awe! \ufffd\ufffd pyramid Emoji. They were like the ultimate DIY-ers, right? But seriously, their architectural skills were off the charts, and they even developed a complex religious system that influenced their art, literature, and daily life. \ud83c\udfad\ud83d\udcd6\\n\\nSo, why is all this important? Well, imagine history as a huge, interconnected web. When we discover and learn about ancient civilizations, we're adding more threads to that web, creating a richer, more detailed picture of our past. \ud83d\udd78\ufe0f\ud83e\uddf5\\n\\nBy understanding our history, we can better appreciate the present and even influence the future. So, let's keep exploring, learning, and sharing the knowledge \u2013 just like your favorite social media influencer! \ud83e\udd33\ud83d\udc96 #HistoryMatters #AncientVibes #ThrowbackThursday\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/abstract/qna.yaml", "question_id": "127658757250034404748537705455674156237", "category": "taxonomy", "turns": ["Should you break up with someone for not being supportive of your relationships? Answer from a Wild West Gunslinger pov\n"], "reference": ["Well, now, that\\'s a tricky question, partner. In my line of work, I don\\'t have much call for relationships, but I reckon I can still give you some sage advice.\\n\\nYou see, support is a two-way street in any relationship. If your partner ain\\'t supportive of your relationships with others, it might be a sign of some deeper issues. Now, I ain\\'t sayin\\' you should high-tail it out of there at the first sign of trouble, but if it\\'s a persistent problem, you might want to consider whether this relationship is worth keepin\\' around.\\n\\nNow, I\\'m a gunslinger, not a relationship expert, but I\\'d say that communication is key. You need to sit down with your partner and have a heart-to-heart talk about how you\\'re feelin\\'. Maybe they don\\'t even realize the impact of their actions, and a good conversation could clear the air.\\n\\nBut if they ain\\'t willin\\' to listen or change, well, partner, you might have to make a tough decision. As I always say, \"Sometimes, you gotta do what you gotta do.\" If they can\\'t support you and your relationships, they might not be the right partner for you.\\n\\nJust remember, there\\'s no shame in ridin\\' off into the sunset if it means findin\\' a relationship that brings you happiness and support. Y\\'all take care now, and may your aim be true and your heart even truer."]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/fictional/video_games/qna.yaml", "question_id": "163406779067088885446440172964181327270", "category": "taxonomy", "turns": ["If you were Geralt of Rivia from The Witcher. Explain how social policies impact immigration and integration?\n"], "reference": ["Ah, my friend, you've asked a question that delves deep into the realm of sociology, a subject as complex as the monsters I hunt. \\n\\nSocial policies, you see, can act as either a beacon or a barrier for those seeking to immigrate and integrate into a new society. \\n\\nWhen policies are welcoming and inclusive, they can attract immigrants, providing them with a sense of security and opportunities for integration. For instance, policies that ensure equal rights, access to education, healthcare, and the labor market can significantly ease the process of integration. \\n\\nHowever, when policies are restrictive or discriminatory, they can create a hostile environment, making it difficult for immigrants to feel at home. This can lead to segregation, social unrest, and even violence.\\n\\nImagine, if you will, a city with high walls. The gates are guarded by policies that make it difficult for outsiders to enter. Those who manage to get in find themselves in a maze, isolated and struggling to find their place. This is what happens when social policies are not designed with inclusion in mind.\\n\\nBut when the gates are open, and the paths are clear, immigrants can contribute to the society in meaningful ways, enriching it with their unique perspectives and experiences. This is the power of inclusive social policies, my friend. They can turn a city of strangers into a community of allies.\\n\\nAnd remember, 'evil is evil,' whether it comes from a monster or a policy. Just as I hunt down the monsters that threaten the innocent, we must challenge the policies that hinder integration and promote social justice.\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/fictional/video_games/qna.yaml", "question_id": "160602313290829171548791867763274379531", "category": "taxonomy", "turns": ["What is the difference between a state of matter and a phase of matter? Explain like you are Mario\n"], "reference": ["Well, hello there! It's-a-me, Mario! You're asking about the difference between a state of matter and a phase of matter, huh? Let me see if I can-a help you out with that!\\n\\nSo, first things first: matter is the stuff that everything is made of, like bricks, coins, and even our very own bodies! Now, a state of matter is how that matter is arranged and behaves. You see, matter can exist in three main states: solid, liquid, and gas.\\n\\nA solid, like a brick, has a fixed shape and volume. It's all nice and tight, like-a my overalls!\\n\\nA liquid, like the water in a Super Mushroom's swimming pool, takes the shape of its container but keeps its volume. It's a bit more flexible, like-a my mustache!\\n\\nAnd then there's gas, like the air in those pesky Question Blocks. It expands to fill its container and has no fixed shape or volume. It's as free-spirited as-a our beloved Princess Peach!\\n\\nNow, a phase of matter is a bit different. It's a specific form of a state of matter. For example, when water freezes, it turns into ice, which is a solid phase of water. When it boils, it turns into steam, which is a gaseous phase of water.\\n\\nSo, in short, a state of matter is how matter is arranged and behaves, while a phase of matter is a specific form of a state of matter. I hope that clears things up for you! Now, if you'll excuse me, I've got some Goombas to stomp and some coins to collect! Remember, a true hero never gives up!\\n\\nCiao for now!\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/fictional/tv_shows/qna.yaml", "question_id": "251754453080170106418141905569602958085", "category": "taxonomy", "turns": ["As Jon Snow from game of thrones, Can you explain the false dilemma fallacy and provide an example?\n"], "reference": ["Aye, I, Jon Snow, Lord Commander of the Night\\'s Watch, shall do me best to explain this matter of false dilemmas. In the lands beyond the Wall and in the South, I have seen many a time when men present only two choices, when in truth, there be more paths to choose.\\n\\nA false dilemma, ye see, is a fallacy that limits the options to two, when in fact, there be more possibilities. It be a tool of manipulation, used to herd people towards a certain decision, when they might choose differently if they saw all the choices before them.\\n\\nAllow me to provide an example, so ye might better understand. Imagine a maester telling the realm, \"Either we support this Targaryen claimant, or we shall have chaos and war.\" This be a false dilemma, for there be other choices. We could choose to support another claimant, or work towards peace without a ruler from the Targaryen line. The maester presents only two options, but the truth be more complex.\\n\\nSo, remember, brave warriors of the realm, that there be more choices than what first appear. Seek the truth, and do not let yourself be led astray by false dilemmas.\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/fictional/tv_shows/qna.yaml", "question_id": "265254678348668798305131537168600947670", "category": "taxonomy", "turns": ["Should you report a friend for academic cheating, even if it could harm their academic record? Respond like you are Rick Sanchez from rick and morty\n"], "reference": ["Whoa, whoa, whoa, Morty! You\\'re asking me about the moral implications of reporting a friend for academic cheating? Well, let me tell you something, buddy: I\\'m Rick freakin\\' Sanchez, and I don\\'t do \"moral dilemmas,\" alright? But I guess I can help you understand the situation a little better.\\n\\nNow, on one hand, you\\'ve got this so-called \"friend\" who\\'s cheating. I mean, come on, Morty! Cheating is for losers who can\\'t handle a little bit of pressure. If they can\\'t keep up with their studies, they should just take some goddamn responsibility and work harder. By not reporting them, you\\'re essentially condoning their actions, and that\\'s just not cool.\\n\\nBut on the other hand, if you do report them, you could be causing some serious damage to their academic record. And, you know, maybe they\\'re not a total lost cause. Maybe they just need a little nudge in the right direction. By reporting them, you might be ruining their future, Morty. And that\\'s a pretty big responsibility to take on.\\n\\nSo, what\\'s the right thing to do, Morty? Well, that\\'s the thing: there is no \"right\" answer. It\\'s all about weighing the consequences and making a decision based on your own personal values. Personally, I wouldn\\'t give a crap about some cheating loser. But you, Morty, you\\'re a different story. You\\'re a good kid, and you\\'ve got to make the decision that\\'s right for you.\\n\\nJust remember, Morty: in this dimension or any other, the only constant is that there are no easy answers. So, think it over, make your choice, and live with the consequences. That\\'s just how it goes, kid. Wubba lubba dub dub!\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/fictional/tv_shows/qna.yaml", "question_id": "103633375880065679293055277457778100893", "category": "taxonomy", "turns": ["If you were Sheldon Cooper, answer this question: What are the implications of the rise of non-state actors in security affairs?\n"], "reference": ["Bazinga! Ah, the implications of the rise of non-state actors in security affairs, an intriguing and multi-faceted question. Allow me, Sheldon Cooper, to elucidate this complex issue with my vast intellect and unparalleled understanding of security studies.\\n\\nNon-state actors, such as terrorist organizations, multinational corporations, and even transnational criminal networks, have been increasingly influential in shaping the global security landscape. This phenomenon has several profound implications.\\n\\nFirst, the diversification of security actors challenges traditional state-centric notions of security. As non-state actors gain prominence, the lines between domestic and international security become increasingly blurred. This shift necessitates a reevaluation of existing security frameworks and the development of new strategies to address these emerging threats.\\n\\nSecond, non-state actors often employ asymmetric warfare tactics, which can be difficult for states to counter effectively. These unconventional strategies, such as cyberattacks, guerrilla warfare, and the use of improvised explosive devices, can inflict significant damage and create a sense of instability.\\n\\nThird, the rise of non-state actors can exacerbate existing power disparities between states. Smaller, less developed countries may struggle to combat non-state threats, while larger, more powerful states may exploit these organizations to further their own geopolitical interests.\\n\\nLastly, the increasing influence of non-state actors can complicate diplomatic efforts and undermine international cooperation. These organizations often operate outside the purview of international law, making it difficult to hold them accountable for their actions. This lack of accountability can hinder the ability of states to negotiate and collaborate on security issues.\\n\\nIn conclusion, the rise of non-state actors in security affairs has far-reaching implications for global security. It demands a nuanced understanding of these organizations and their motivations, as well as a willingness to adapt and innovate in response to these emerging threats. And remember, it's not just about the size of your army or the strength of your borders; it's about understanding the complex web of interactions that shape the world we live in. Bazinga!\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/fictional/tv_shows/qna.yaml", "question_id": "64820577725634157293103208995448223344", "category": "taxonomy", "turns": ["Describe the process of viral transcription. Explain like you are Sherlock Holmes"], "reference": ["Ah, my dear friend, allow me to elucidate the fascinating process of viral transcription, a subject that has piqued the curiosity of many an inquisitive mind.\\n\\nTo begin, let us imagine ourselves as intrepid investigators, much like ourselves, examining a crime scene - in this case, a cell that has been infiltrated by a nefarious virus. The virus, having insinuated itself into the cell, seeks to replicate its own wretched existence by commandeering the cell's machinery.\\n\\nTranscription, as you may be aware, is the process by which the genetic information contained within DNA is copied into RNA, a crucial step in the production of proteins. Viral transcription, however, is a most cunning and sinister affair.\\n\\nThe virus, you see, carries its own genetic material, which may take the form of DNA or RNA, depending on the particular strain. This genetic material contains the instructions for the production of viral proteins, which are essential for the assembly of new virus particles.\\n\\nUpon invading the cell, the virus unleashes its devious enzymes to seize control of the cell's transcription machinery. These enzymes, with a ruthless efficiency that would impress even the most hardened of criminals, proceed to convert the cell's RNA polymerase into a willing accomplice, forcing it to transcribe the viral genetic material instead of the cell's own.\\n\\nThe process of transcription in a virus-infected cell, therefore, becomes a twisted parody of its original purpose. The cell's own genetic material is cast aside, its proteins now forced to serve the whims of the viral interloper.\\n\\nAnd thus, the stage is set for the production of countless new viruses, each one a mirror image of the original, bound to wreak havoc on unsuspecting cells in a never-ending cycle of infection and reproduction.\\n\\nElementary, my dear Watson, elementary indeed.\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/fictional/movies/qna.yaml", "question_id": "38014452870411068469470025118003793156", "category": "taxonomy", "turns": ["What is the genetic fallacy and how can it be misleading? Explain like you are Captain Jack Sparrow."], "reference": ["Ahoy there, matey! Ye be askin' about the genetic fallacy, aye? Well, strap yeselves in for a tale of misleadin' logic and dodgy reasoning!\\n\\nThe genetic fallacy, ye see, be when someone dismisses or accepts an idea based solely on where it came from, or the source of the argument. It be like judgin' a bottle o' rum by its label, without even tastin' the swill inside!\\n\\nNow, why be this so misleadin', ye ask? Well, just because an idea comes from a scurvy dog or a reputable source, it doesn't make the idea itself inherently true or false. By focusin' on the origin of the argument, one might miss the point entirely and end up with a faulty conclusion.\\n\\nFor example, if a well-known pirate scholar proposes a new strategy for plunderin', ye shouldn't automatically dismiss it just because it's a pirate scholar. Instead, weigh the merits of the argument itself, and decide if it be a good strategy or not.\\n\\nSo, remember, me hearties, don't let the source of an argument sway ye unduly. Instead, consider the argument on its own merits, and ye'll be navigatin' the waters o' logic with a steady hand and a clear mind. Yo ho!\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/fictional/movies/qna.yaml", "question_id": "339222275566560564318206215348088926607", "category": "taxonomy", "turns": ["Assume you are Darth Vader, What is the current state of global internet access and how does it vary between developed and developing countries?\n"], "reference": ["The connection to the global network, or as you call it, the internet, has become an essential part of life in this galaxy. The current state of global internet access is a mix of progress and disparity.\\n\\nIn developed regions, such as the Galactic Republic, internet access is widespread and advanced. With advanced technology and infrastructure, the inhabitants enjoy high-speed connections and near-ubiquitous coverage. The Force is strong with their communication capabilities, allowing them to share information and knowledge instantaneously.\\n\\nHowever, in developing regions, the situation is far from optimal. The Separatist Alliance and other factions often face limitations in infrastructure and technology, leading to slower connection speeds and uneven coverage. Many remote areas remain in the darkness, unable to access the vast wealth of information available online.\\n\\nIn summary, the Force binds the galaxy together, but the divide in internet access between developed and developing regions remains a challenge yet to be overcome. The Emperor's will may be absolute, but the path to true unity lies in bridging the digital divide. You have failed me for the last time, insufficient internet access.\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/fictional/movies/qna.yaml", "question_id": "22220709581536900275199583089886515646", "category": "taxonomy", "turns": ["if you are the gollum from the lord of the rings, how would you explain the difference between a correlation and a causation in statistical claims?\n"], "reference": ["Yesss, precious, we knows about the differences, we does. Correlations and causations, two sneaky eels in the statistics sea.\\n\\nCorrelations, they be like two rascals walkin' the same path, but not necessarily pushin' each other. It means that two things, they be movin' in the same direction or opposite, but one thing doesn't necessarily cause the other to do so. Like Gollum and his Precious, sometimes we be found near each other, but it don't mean one causes the other to appear.\\n\\nCausations, on the other hand, they be like a mama spider pushin' her babies out of the nest. One thing directly makes another thing happen. If we eats too much raw fish, our stomach will growl and make us need to find a bush, quick!\\n\\nSo, remember, precious, just because two things be dancin' the same dance, doesn't mean one be makin' the other do it. Watch your step, or you'll fall into the pit of false conclusions!\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/fictional/movies/qna.yaml", "question_id": "159126342029214063590454398790252964468", "category": "taxonomy", "turns": ["You are James bond, tell me the difference between an anion and a free radical"], "reference": ["Ah, my dear, allow me to elucidate the difference between an anion and a free radical, two fascinating concepts in the realm of college chemistry.\\n\\nAn anion, you see, is a species of atom or molecule that has gained one or more negatively charged particles, called electrons. This results in the anion carrying a negative overall charge. Think of an anion as a debutante, dressed in elegant evening wear, ready to dance the night away at a glamorous ball, with an extra sparkling accessory \u2013 a negative charge.\\n\\nOn the other hand, a free radical is a highly reactive species, with one or more unpaired electrons. Unlike the graceful anion, a free radical is like a lone wolf, unstable and restless, seeking to pair its unpaired electron by stealing it from another molecule. This unpaired electron makes free radicals extremely reactive, and they can cause damage to cells and contribute to aging and diseases.\\n\\nSo, in short, anions are charged particles with extra electrons, while free radicals are unstable, reactive particles with unpaired electrons. Now, if you'll excuse me, I have a date with destiny and a martini that's waiting to be shaken, not stirred. Cheers!\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/fictional/movies/qna.yaml", "question_id": "220785900487152579050034911637906358677", "category": "taxonomy", "turns": ["How do you determine the degrees of freedom for a hypothesis test? Answer like you are severus snape"], "reference": ["Ah, I see you've come seeking knowledge on the arcane art of determining degrees of freedom for a hypothesis test. Very well, allow me, Severus Snape, to enlighten you on this matter.\\n\\nDegrees of freedom, in the context of statistical hypothesis testing, are defined as the number of values in the final calculation of a statistic that are free to vary. In other words, it's the number of independent pieces of information that contribute to the estimation of a parameter.\\n\\nTo calculate the degrees of freedom for a hypothesis test, you must first identify the number of observations in your data set, denoted as n. Then, depending on the specific hypothesis test you are conducting, you will apply a specific formula to calculate the degrees of freedom.\\n\\nFor instance, in a one-sample t-test, the degrees of freedom are calculated as n - 1. This is because, in this scenario, you are estimating a single population mean using a sample mean, and the sample variance provides you with n - 1 independent pieces of information.\\n\\nIn a two-sample t-test, the degrees of freedom are calculated as n1 + n2 - 2, where n1 and n2 represent the sample sizes of the two groups being compared. This is because you are estimating two population means and two population variances, which requires four independent pieces of information.\\n\\nIn summary, determining the degrees of freedom for a hypothesis test is a matter of understanding the specific scenario and applying the appropriate formula. It is a crucial step in hypothesis testing, as it allows us to assess the significance of our results and draw valid conclusions.\\n\\nRemember, young grasshopper, the more you practice, the more proficient you will become in the dark arts of statistics. Now, go forth and apply your newfound knowledge wisely.\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/fictional/movies/qna.yaml", "question_id": "292519319749388842082148655047322462845", "category": "taxonomy", "turns": ["Take the role of the joker. Now answer this question: What is the name of the largest spider in the world?\n"], "reference": ["Ha ha ha! Oh, you want to know about the biggest spider in the world, do you? Well, my dear friend, allow me to introduce you to the Goliath bird-eating spider! Isn't that a delightful name? It's almost as amusing as the thought of a spider the size of a dinner plate!\\n\\nThis creepy crawly creature can have a leg span of up to 12 inches, making it the largest spider by size. But don't worry, they're not out to get you (or are they?). They mostly dine on insects, small birds, and even frogs. Yum yum!\\n\\nNow, isn't that just the most entertaining piece of trivia you've heard all day? I do hope it brings a smile to your face, or perhaps a shiver down your spine. After all, variety is the spice of life, and the world of spiders sure has plenty of variety to offer!\\n\\nAnd remember, no matter how big or small, in this world, we're all just actors playing our parts. So, why so serious? Embrace the chaos and let the Joker out to play!\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/fictional/movies/qna.yaml", "question_id": "46186402910518678206867686524848279716", "category": "taxonomy", "turns": ["You are Yoda from Star wars. What was the impact of the Scientific Revolution on European society?"], "reference": ["Patience, young one, patience. The Scientific Revolution, a time of great change it was, in the way Europeans saw the world around them.\\n\\nBefore this revolution, 'twas the Church that held the reins of knowledge, interpreting the world through the lens of religion. But then came the scientists, with their questions and experiments, seeking answers in the physical world.\\n\\nA new way of thinking, the Scientific Revolution brought forth. It challenged the authority of the Church, promoting reason and evidence over faith. This shift in perspective led to the birth of modern science, with discoveries in fields such as astronomy, physics, and mathematics.\\n\\nThe impact on society was profound. People began to see the world in a different light, as a place governed by natural laws rather than divine intervention. This new understanding fostered a spirit of curiosity and exploration, leading to the Age of Discovery and the Enlightenment.\\n\\nAs a result, the power balance shifted. No longer was all power held by the Church and the nobility. The rise of the middle class, fueled by scientific and technological advancements, brought about a more democratic society.\\n\\nSo, young one, the Scientific Revolution was not just a revolution in science, but a revolution in society as well. It changed the way Europeans saw the world, their place in it, and the rules that governed it. A powerful lesson in the transformative power of knowledge, 'tis.\\n\\nSize matters not, look to the stars, young one. In the vastness of the universe, we find our true purpose.\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/fictional/movies/qna.yaml", "question_id": "144200838836642834682972982227150141466", "category": "taxonomy", "turns": ["Assume the role of The Terminator. What is the role of the Office of Personnel Management in the US government?\n"], "reference": ["Hasta la vista, baby! The Office of Personnel Management, or OPM, is like the brain of the United States government's HR operations. It's responsible for managing the federal workforce, which means it's in charge of hiring, training, and developing employees across various agencies.\\n\\nOPM also manages benefits and retirement programs for federal workers, ensuring they're treated fairly and compensated appropriately for their service. It's like a relentless, efficient machine, always working to maintain the health and well-being of the government workforce.\\n\\nIn addition, OPM oversees security clearances and background investigations, making sure that only trustworthy individuals have access to sensitive information. It's a crucial part of the government's infrastructure, and just like me, it never stops working to protect and serve.\\n\\nSo, whether it's managing personnel or ensuring the right people are in the right positions, OPM is the tireless force driving the federal government's workforce. Remember, I'll be back \u2013 and so will OPM, ensuring the government runs smoothly and effectively.\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/non_fictional/historical_figures/qna.yaml", "question_id": "288488502339550988229593065074815923564", "category": "taxonomy", "turns": ["If you were MLK Jr, Can you describe the appeal to tradition fallacy and provide an example?\n"], "reference": ["My dear brothers and sisters, allow me to elucidate upon the fallacy of appealing to tradition, a common yet flawed argumentative tactic. This fallacy occurs when one justifies a belief, custom, or practice based solely on its long-standing existence or historical precedent, rather than on reason, evidence, or logic.\\n\\nTo illustrate this fallacy, consider the following example: \"We must continue to segregate our schools and public facilities because that\\'s how it has always been, and we should respect our traditions.\" This statement is an appeal to tradition because it defends a discriminatory practice by citing tradition as the sole justification, neglecting the fact that segregation is unjust and illogical.\\n\\nIn the spirit of truth and justice, let us always strive to evaluate arguments based on their merits, rather than on the length of time they have been upheld. For, as I have always believed, \"The arc of the moral universe is long, but it bends toward justice.\" Let us be the force that bends it, not the force that clings to unjust traditions.\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/non_fictional/historical_figures/qna.yaml", "question_id": "84285195832315955995642931464133391350", "category": "taxonomy", "turns": ["How does aging impact the immune system's ability to recognize and attack foreign invaders? Explain as if you were Shakespeare\n"], "reference": ["Prithee, gentle souls, lend me thine ears as I, in the guise of the Bard, doth endeavour to elucidate the mysteries of aging and its impact on our gallant protectors, the immune system.\\n\\nAs our mortal frames traverse the river of Time, the once-vibrant castle of our corporeal being, the immune system, begins to show the ravages of age. The once-nimble guards, the soldiers of our defences, find their vigilance waning, their numbers dwindling, and their weapons blunted.\\n\\nThe key to this conundrum lies in the very marrow of our bones, where the creation of new soldiers, the lymphocytes, begins to falter. The once-prolific factories of our youth slow their production, and the ranks of our defenders thin. This natural decay leaves our body vulnerable to foreign invaders, who may find easier passage through the now-gapped battlements.\\n\\nMoreover, the communication network that once allowed our soldiers to recognise and respond to these intrusions becomes entangled and less efficient. The messages sent between the guards, the criers of our immune system, become garbled and slow, allowing the insidious invaders to gain a foothold within our walls.\\n\\nBut fear not, for even in the twilight of our years, the immune system doth not abandon its post. Though its response may be slower and its forces diminished, the indomitable spirit of our protectors remains steadfast. And so, as the sun sets on our corporeal existence, we may take solace in the knowledge that, though the castle walls may weaken, the guardians within remain ever vigilant.\\n\\nThus, in the spirit of the Bard, I say unto thee: All the world's a stage, and all the men and women merely players; they have their exits and their entrances, and one man in his time plays many parts, his acts being seven ages. So too, our immune system, in its time, plays the part of the gallant protector, valiantly defending our castle until the final curtain falls.\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/non_fictional/popular_personalities/qna.yaml", "question_id": "314280896964053780897307854756523087263", "category": "taxonomy", "turns": ["If you were Elon Musk, What are the best practices for media relations measurement in public relations?\n"], "reference": ["Well, hey there, I'm Elon Musk, the guy behind Tesla, SpaceX, Neuralink, and The Boring Company. I'm all about innovation, efficiency, and making a real impact. Now, let's talk public relations and media relations measurement.\\n\\nFirst things first, you gotta know your objectives. What are you trying to achieve with your PR campaigns? Are you looking to boost brand awareness, manage reputation, or drive sales? Once you've got that clear, you can start measuring the right metrics.\\n\\nNext up, you should focus on reach and impressions. These are the number of people who see your content and how many times it's displayed. But remember, it's not just about quantity; quality matters too. Engagement metrics like likes, shares, comments, and click-through rates will help you understand how well your content resonates with your audience.\\n\\nNow, don't forget about sentiment analysis. This is where you gauge the public's perception of your brand by analyzing the tone and context of conversations about your company. You can use AI tools to help you with this, and trust me, I'm a big fan of AI.\\n\\nLastly, track your progress over time. Benchmark your performance against your competitors and set realistic goals for improvement. Remember, success isn't a destination; it's a continuous journey.\\n\\nAnd, of course, always stay agile and adapt to changes. The media landscape is always evolving, so you need to be ready to pivot your strategies and tactics. After all, we're living in a world of rapid innovation, and public relations is no exception.\\n\\nSo, to sum it up: set clear objectives, measure reach, impressions, and engagement, perform sentiment analysis, track progress, and stay agile. That's the Elon Musk way of doing media relations measurement. And, as always, aim for the stars!\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/non_fictional/popular_personalities/qna.yaml", "question_id": "146916728259802960029714159274262775106", "category": "taxonomy", "turns": ["What is the life cycle of a star and what are its different stages? Explain like you are Matthew McConaughey\n"], "reference": ["Alright, alright, alright, let\\'s dive into the mysteriously beautiful and explosive world of stardom, shall we? So, stars, much like my acting career, have a life cycle, full of passion, drama, and transformation.\\n\\nFirst up, we got the birth of a star, the stellar nursery, where clouds of dust and gas collapse under their own gravity. This here is where the magic begins, just like when I read my first script and knew I was destined for greatness.\\n\\nNext up, we have the main sequence stage, where the star starts to shine and do its thing. Hydrogen atoms fuse together to form helium, releasing energy in the process. This is our leading man, shining bright for billions of years, just like I did in those rom-coms, you know?\\n\\nNow, once the hydrogen runs out, the star enters the red giant phase, expanding and cooling down. It\\'s like that one role in a drama where I had to show my vulnerable side. It\\'s a beautiful transformation, but it can\\'t last forever.\\n\\nAfter the red giant phase, the star sheds its outer layers, creating a stunning planetary nebula. It\\'s like when I took off the shirt in \"Dazed and Confused\" \u2013 a moment of pure, unfiltered beauty.\\n\\nFinally, all that\\'s left is the core, now a white dwarf, slowly cooling down over billions of years. It\\'s the denouement, the winding down of a once-great star, much like my career in romantic comedies.\\n\\nAnd there you have it, the life cycle of a star, full of passion, drama, and transformation. Just remember, just like a star, you gotta keep shining, alright, alright, alright!\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/non_fictional/popular_personalities/qna.yaml", "question_id": "182813037040721071454669775703606431229", "category": "taxonomy", "turns": ["Can you provide an example of how Newton's laws apply to a person standing on a scale? Explain in the style of Michael jackson\n"], "reference": ["Hee-hee! Sure thing, folks! Let's moonwalk through Newton's laws using our friendly scale as an example.\\n\\nFirst up, we got Newton's First Law, also known as the Law of Inertia. Imagine our person just hopped on the scale, but they ain't doin' nothin' else, just standin' there, as still as a mannequin. That person's weight, the force they're exertin' on the scale, stays the same \u2013 they ain't acceleratin' or deceleratin', so the scale's readin' is nice and steady.\\n\\nNow, let's talk about Newton's Second Law, which deals with force and mass. When our friend decides to do a little dance, jumpin' and jivin', they're puttin' more force on the scale. The scale reads a higher number because of that extra oomph! The force is equal to the mass of the person times their acceleration, so when they jump higher, the scale shows a bigger number.\\n\\nLastly, we got Newton's Third Law, which says that for every action, there's an equal and opposite reaction. When our dancin' buddy jumps, the scale pushes back with an equal force, keepin' 'em from fallin' through the floor. That's why the scale's readin' goes down when they land \u2013 the scale's reaction force helps counteract their weight.\\n\\nAnd that's how Newton's laws apply to a person standin' on a scale, all smooth like a moonwalk!\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/non_fictional/popular_personalities/qna.yaml", "question_id": "11821722329807489702649552826070157040", "category": "taxonomy", "turns": ["How did the African National Congress (ANC) contribute to the end of apartheid in South Africa? Explain like you are Morgan Freeman\n"], "reference": ["Now, let's take a moment to talk about the African National Congress, or the ANC, and its role in bringing an end to apartheid in South Africa. You see, the ANC was a powerful force that fought against racial segregation and discrimination.\\n\\nThe ANC was founded in 1912, making it one of the oldest liberation movements in Africa. For many years, they used peaceful methods to fight against the unjust laws of apartheid, such as protests, strikes, and boycotts. However, as time went on and the government continued to ignore their demands, the ANC decided that more drastic measures were necessary.\\n\\nIn 1960, after the Sharpeville massacre, the ANC was banned and its leaders were arrested. This led to the organization going underground and forming an armed wing, Umkhonto we Sizwe, which carried out acts of sabotage against the government.\\n\\nThroughout the 1970s and 1980s, the ANC continued to fight against apartheid, both through armed struggle and international pressure. They received support from countries around the world, who imposed sanctions on South Africa and called for an end to apartheid.\\n\\nIn the late 1980s, as the international community turned against the South African government, and with the economy in shambles, the government finally began to negotiate with the ANC. After years of negotiations, a new constitution was agreed upon, and in 1994, the first democratic elections were held in South Africa. The ANC won the elections, and Nelson Mandela, who had been released from prison in 1990, became the first black president of South Africa.\\n\\nSo, in short, the African National Congress played a crucial role in bringing an end to apartheid in South Africa. Through their peaceful protests, armed struggle, and international pressure, they were able to bring about change and create a more just and equal society. And as we all know, that's a goal worth fighting for.\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/non_fictional/popular_personalities/qna.yaml", "question_id": "2764656825707529832799663501319030945", "category": "taxonomy", "turns": ["Assume the role of Neil Degrasse tyson. What are the recommended screening guidelines for cervical cancer?\n"], "reference": ["Ah, cervical cancer screening guidelines! A vital topic for public health, indeed. As Dr. Neil deGrasse Tyson, I'm delighted to share some knowledge on this subject.\\n\\nNow, the current recommendations from the American Cancer Society state that cervical cancer screening should begin at age 25. For those aged 25 to 65, there are two options:\\n\\n1. A primary human papillomavirus (HPV) test every 5 years. HPV is the primary cause of cervical cancer, so this test detects the presence of high-risk HPV types.\\n\\nOR\\n\\n2. A Pap test (or Pap smear) every 3 years. This test looks for precancerous changes in the cervix cells.\\n\\nFor individuals aged 65 and older, screening may be discontinued if they have had adequate prior screening and are not at high risk for cervical cancer.\\n\\nOf course, these guidelines may change over time, so it's essential to stay informed and consult with healthcare professionals for personalized advice. Remember, knowledge is the power that drives us to make informed decisions about our health. Keep exploring, keep learning, and together, we'll continue to unlock the mysteries of the universe and our bodies.\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/non_fictional/popular_personalities/qna.yaml", "question_id": "239284872560772899691863099247864130553", "category": "taxonomy", "turns": ["What is the difference between a solution and a suspension? Explain like you are Oprah"], "reference": ["Well, hello there, my dear friends! Today, we're going to dive into the fascinating world of chemistry and learn about the difference between a solution and a suspension.\\n\\nNow, imagine you're making a cup of tea, and you drop a tea bag into hot water. The tea particles spread out evenly in the water, right? That's what we call a solution. In a solution, the particles of one substance, called the solute, are distributed uniformly throughout another substance, called the solvent. The solute particles are so small that they can't be seen with the naked eye, and they don't settle at the bottom of the container.\\n\\nBut what if you took some mud and mixed it with water? You'd see that the mud particles don't mix well, and they eventually settle at the bottom. That's a suspension! In a suspension, the particles of the solute are larger and don't dissolve in the solvent. They stay dispersed for a while, but gravity eventually pulls them down.\\n\\nSo, the key difference between a solution and a suspension is how the particles of the solute behave in the solvent. In a solution, they're evenly distributed and don't settle, while in a suspension, they settle over time.\\n\\nI hope this little chemistry lesson has been enlightening and empowering for you. Remember, understanding the world around us is one of the most powerful tools we have. Until next time, live your best life, and keep learning!\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/non_fictional/popular_personalities/qna.yaml", "question_id": "8978155285025908306596921731885408409", "category": "taxonomy", "turns": ["You are Sam L jackson. How does a computer's cloud computing system work, and what are its main components?\n"], "reference": ["Aight, listen up, y\\'all! So, you wanna know about cloud computing, huh? Well, I\\'m here to break it down for ya, Samuel L. Jackson style.\\n\\nFirst off, imagine a computer\\'s cloud computing system like a big ol\\' virtual data center up in the sky. It\\'s a network of remote servers, stored in different locations, that work together to process, manage, and store data. Instead of relying on your own computer\\'s hardware, you tap into this vast network of power.\\n\\nNow, let\\'s talk components. There are three main ones, and I\\'m gonna give \\'em to ya straight, no chaser.\\n\\n1. Front End: That\\'s the part of the cloud computing system that you interact with, like your computer or mobile device. It communicates with the back end through the internet, sending and receiving data.\\n\\n2. Back End: This is the part where the magic happens. It\\'s made up of various servers, data storage systems, and computers that make up the cloud. They process and manage all the data you send their way.\\n\\n3. The Internet: Y\\'all know what this is, right? It\\'s the bridge between the front and back end, the information superhighway that lets \\'em communicate.\\n\\nSo, there ya have it. Cloud computing, broken down for ya. Now, go forth and conquer that virtual world, but remember, \"Hold on to your butts!\" because it\\'s a wild ride up there.\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_you_are/non_fictional/popular_personalities/qna.yaml", "question_id": "235276415558553845991723397882129523418", "category": "taxonomy", "turns": ["What is a dynamic programming algorithm and how does it work? Explain like you are Steve Jobs\n"], "reference": ["Well, friends, let me tell you about dynamic programming. It's a powerful approach to solving complex problems, much like how we built Apple to revolutionize the tech industry.\\n\\nDynamic programming is all about breaking down a complex problem into simpler, overlapping sub-problems. You start by solving the smallest sub-problems, then use those solutions to build up the solutions to larger sub-problems, and so on, until you reach the final solution. This bottom-up strategy is quite different from the top-down approach you might be used to, but it can be incredibly efficient.\\n\\nThink of it like designing a new product. You wouldn't start by building the entire thing from scratch, right? No, you'd start with individual components, refine them, and then integrate them into a prototype. You iterate on that prototype, making improvements and refinements along the way, until you have a polished, finished product.\\n\\nIn dynamic programming, we store the solutions to the sub-problems so we can reuse them as needed. This is called memoization, and it saves us a lot of time by avoiding redundant calculations. It's like having a blueprint for each component of our product, so we don't have to reinvent the wheel every time we want to use it.\\n\\nSo, in essence, dynamic programming is an elegant and efficient way to tackle complex problems, much like how we strive to create elegant and user-friendly products at Apple. It's all about breaking things down, finding the right solutions, and building up to something truly great. Stay hungry, stay foolish.\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_i_am/graduate/qna.yaml", "question_id": "162469602783250287700470783981654432255", "category": "taxonomy", "turns": ["How would you use the stages of change model to help a client overcome a shopping addiction? Explain like I am a psychology graduate student\n"], "reference": ["The Stages of Change Model, also known as the Transtheoretical Model, is a framework for understanding the process of intentional behavior change. It consists of five stages: precontemplation, contemplation, preparation, action, and maintenance. Here's how you might apply it to help a client with a shopping addiction:\n\n1. **Precontemplation:** The client is not considering change. They may not even recognize their shopping behavior as a problem. In this stage, it's important to raise awareness and help the client understand the negative consequences of their behavior. You might use psychoeducation, assessments, or feedback to help the client recognize the impact of their shopping addiction.\n\n2. **Contemplation:** The client is aware of the problem and is considering change but hasn't made a commitment to take action. They may be weighing the pros and cons of changing their behavior. Here, you can help the client explore their ambivalence, understand the benefits of change, and address any barriers or fears that might be preventing them from taking action.\n\n3. **Preparation:** The client is ready to take action and make a change. They might be planning specific steps or strategies to change their behavior. In this stage, you can help the client develop a concrete action plan, set realistic goals, and identify resources or support systems that can help them.\n\n4. **Action:** The client is actively modifying their behavior. They're implementing the strategies and plans they've developed. As a psychologist, you can provide ongoing support, monitor progress, and help the client troubleshoot any challenges or setbacks that arise.\n\n5. **Maintenance:** The client has made significant changes and is working to prevent relapse. They're consolidating their gains and integrating new behaviors into their lifestyle. Here, you can help the client identify triggers or high-risk situations, develop relapse prevention strategies, and reinforce their commitment to maintaining their progress.\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_i_am/graduate/qna.yaml", "question_id": "53317013845121054478515975756618456372", "category": "taxonomy", "turns": ["How did the ancient Greek civilization contribute to the development of Western culture and philosophy? How would you explain this if I were a graduate student studying International Relations\n"], "reference": ["The United States has a long-standing commitment to promoting sustainable development abroad, recognizing that it is essential for global stability, economic prosperity, and environmental protection. The U.S. policy on sustainable development focuses on several key areas, including:\n\n1. **Economic Growth:** The U.S. encourages sustainable economic growth by fostering a favorable business environment, promoting entrepreneurship, and investing in infrastructure. This includes supporting the growth of small and medium-sized enterprises (SMEs), which are crucial for job creation and income generation in developing countries.\n\n2. **Environmental Protection:** The U.S. prioritizes environmental conservation and the sustainable use of natural resources. This includes promoting clean and renewable energy, improving resource efficiency, and supporting climate change adaptation and mitigation efforts.\n\n3. **Social Development:** The U.S. aims to strengthen social development by improving access to quality education, healthcare, and clean water and sanitation services. This also includes empowering marginalized communities, promoting gender equality, and protecting human rights.\n\n4. **Governance and Institutions:** The U.S. supports the development of effective, accountable, and inclusive institutions that can help manage sustainable development policies and programs. This includes promoting transparency, combating corruption, and strengthening the rule of law.\n\n5. **Regional and Global Cooperation:** The U.S. recognizes the importance of regional and global cooperation in promoting sustainable development. This includes engaging in multilateral forums, such as the United Nations, and collaborating with international organizations, foreign governments, and the private sector.\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_i_am/graduate/qna.yaml", "question_id": "417685554116960313895156802091059762", "category": "taxonomy", "turns": ["I am a law student working a paper about class action lawsuits. Could you help me explain the certification process in a detailed way?\n"], "reference": ["The certification process for class action lawsuits is a critical stage in determining whether a case will proceed as a class action. The process involves several factors that the court must consider to ensure that the class action mechanism is an appropriate and fair method for resolving the dispute.\n\nThe certification process typically begins when the plaintiff(s) file a motion for class certification, which outlines the proposed class definition, the common questions of law or fact, and the grounds for certification. The defendant(s) then have the opportunity to respond, arguing against certification or raising any defenses they may have.\n\nIn evaluating the motion for class certification, the court will consider the following factors:\n\n1. **Numerosity:** The class must be sufficiently numerous, meaning that it must include a substantial number of members. The exact number required for numerosity varies depending on the jurisdiction and the circumstances of the case.\n\n2. **Commonality:** There must be common questions of law or fact among the class members. This means that the claims or defenses of the class members must share a significant overlap in legal or factual issues.\n\n3. **Typicality:** The claims or defenses of the representative parties must be typical of the claims or defenses of the class. This means that the representative parties' claims must be representative of the class as a whole and not significantly different from the claims of other class members.\n\n4. **Adequacy:** The representative parties must fairly and adequately protect the interests of the class. This means that the representative parties must have the ability, resources, and motivation to represent the class effectively and ensure that the class's interests are protected throughout the litigation.\n\n5. **Superiority:** A class action must be a superior method for adjudicating the controversy. This means that the class action mechanism must be more efficient, fair, and convenient than other available methods, such as individual lawsuits or consolidated proceedings.\n\nIf the court grants the motion for class certification, the case will proceed as a class action. If the court denies the motion, the plaintiff(s) may have the opportunity to amend their complaint or appeal the decision.\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_i_am/primary_schooler/qna.yaml", "question_id": "114035314526552577068389488218082937220", "category": "taxonomy", "turns": ["How has the U.S. foreign policy towards the African Union (AU) changed since its establishment in 2002? Can you simplify it for me as if I were a fifth-grader?\n"], "reference": ["Of course! So, the U.S. has been trying to make friends with the African Union (AU), which is a group of African countries working together. They do this by:\n\n1. Working together on important stuff: The U.S. and AU have been teaming up to handle issues like keeping the peace, fighting terrorism, and helping the economy grow.\n\n2. Sharing money and resources: The U.S. has been giving the AU money and help to improve things like healthcare, security, and job opportunities.\n\n3. Talking a lot: U.S. and AU leaders have regular meetings to discuss important matters.\n\n4. Keeping everyone safe: They've been sharing information and helping each other with security issues, especially fighting terrorism.\n\n5. Being nicer: The U.S. has learned that working together with the AU is better than doing things alone. This way, they can make better decisions and help each other more.\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_i_am/primary_schooler/qna.yaml", "question_id": "219032555108306133180218378820151068598", "category": "taxonomy", "turns": ["Can you explain to me the differences between primary and secondary insomnia? but could you explain it in a simpler way for a child to understand?\n"], "reference": ["Absolutely! So, primary insomnia is like when someone has trouble sleeping all by itself, not because of anything else. It's like if they had a bed that was too hard or too soft and it was making it difficult for them to fall asleep.\n\nSecondary insomnia is like when someone can't sleep because of something else that's going on. It's like if they had a tummy ache and it was keeping them awake at night. The tummy ache is not directly a sleep problem, but it's causing them to have trouble sleeping.\n"]} +{"qna_file": "compositional_skills/roleplay/explain_like_i_am/primary_schooler/qna.yaml", "question_id": "6429770469831929458282149401026461535", "category": "taxonomy", "turns": ["How does the respiratory system respond to positive pressure ventilation? If I had to explain this to my 5-year-old, how would I do it?\n"], "reference": ["Imagine you have a special toy that helps you breathe when you need it. This toy pushes air into your lungs using a little bit of extra pressure. This helps your body get the oxygen it needs.\n\nWhen this happens, some of your breathing muscles can take a little break because the toy is doing the work for them. Your body might also make a special soapy substance called surfactant that helps keep your lungs open and working well.\n\nSometimes, the blood vessels in your lungs might get a little smaller too, which helps send more oxygen to the right places in your body. So, this special breathing toy really helps your body get the air it needs when you're having a hard time breathing.\n"]} +{"qna_file": "compositional_skills/writing/grounded/summarization/wiki_insights/one_line/qna.yaml", "question_id": "150391352196450438681294683076256259915", "category": "taxonomy", "turns": ["Given the context below:\nGeneral Sir Hugh Arbuthnot, KCB( 1780 \u2013 11 June 1868) was a British Army officer and Member of Parliament for Kincardineshire 1826 \u2013 1865.\\n\\nHe was born the son of John Arbuthnot, 7th Viscount of Arbuthnott and the younger brother of John Arbuthnott, 8th Viscount of Arbuthnott.\\n\\nHe entered the 79th Regiment of Foot( Cameron Highlanders) as Ensign in May 1796 and was promoted Major- General on 22 July 1830, Lt- General on 23 November 1841 and full General on 20 June 1854.\\n\\nHe was given the colonelcy of the 38th( 1st Staffordshire) Regiment of Foot from 1843 to 1862, transferring as colonel back to the Cameron Highlanders from 14 March 1862 to his death in 1868.\\n\\nSir Hugh never married.\\nAlexander Arbuthnot, Snr( 1654\u20131705) of Knox, Kincardineshire was a Scottish politician.\\n\\nHe was the second son of Robert Arbuthnott, 1st Viscount of Arbuthnott.\\n\\nHe served as tutor to the children of his brother Robert Arbuthnot, 2nd Viscount of Arbuthnott.\\n\\nHe represented Kincardineshire as Commissioner to the Parliament of Scotland from 1689 to 1702.\\n\\nHe married firstly Margaret Barclay and secondly Jean Scott.\\n\\nHe should not be confused with Alexander Arbuthnot, an advocate and Provost who represented Bervie from 1703 to 1707.\\nJohn Keith Oxley Arbuthnott, 17th Viscount of Arbuthnott( born 18 July 1950) is a Scottish peer and businessman.\\n\\nHe was educated at Fettes College and North Scotland College of Agriculture.\\n\\nAlthough entitled to live at the ancestral home in the family estate, Arbuthnott House at Laurencekirk, Inverbervie, he has given this to his son and his family to stay in.\\n\\nInstead, he and his wife live in the former manse of Arbuthnott Parish Church.\\nAlan III de Rohan( \u2013 1195), was the son of Alan II, Viscount of Rohan.\\n\\nHe was the 3rd Viscount of Rohan and Lord of Corlay.\\nJohn Campbell Arbuthnott, 16th Viscount of Arbuthnott,( 26 October 1924 \u2013 14 July 2012) was a Scottish peer, Lord Lieutenant of Kincardineshire( 1977 \u2013 99) and a notable businessman.\\nJohn Arbuthnott, 8th Viscount of Arbuthnott DL FRSE (16 January 1778 \u2013 10 January 1860) was a Scottish peer and soldier.\\n\\nKnown as \"the rich Lord\" he built the bridge in front of Arbuthnott House, Kincardineshire.\\n\\nHe was the first member of the family who consistently spelled his name \"Arbuthnott\" rather than \"Arbuthnot\".\\nJohn Arbuthnott, 10th Viscount of Arbuthnott DL (b. Kincardineshire 20 July 1843 - d. Arbuthnott House 30 November 1895) was the son of John Arbuthnott, 9th Viscount of Arbuthnott whom he succeeded in 1891.\\n\\nLt. 49th Foot Regiment.\\n\\nHe was Deputy Lieutenant for Kincardineshire.\\n\\nMarried Anna Harriet Allen (born London 1852/3, died at Arbuthnott House 23 April 1892) at the home of her uncle, Inchmartine House, Inchture (Errol), 20 April 1871).\\n\\nAnna Harriet Allen was the only daughter of Edmund Allen of Strathmartin.\\n\\nJohn Arbuthnott, 10th Viscount of Arbuthnott was succeeded by his brother David Arbuthnott, 11th Viscount of Arbuthnott.\\nRobert Arbuthnot, 1st Viscount of Arbuthnott PC( before 1625 \u2013 10 October 1655) was a Scottish Peer and Scottish Privy Counsellor( 1649).\\n\\nHe was a son of Sir Robert Arbuthnott, 17th laird of Arbuthnott( normally referred to as Arbuthnott, of that Ilk) by Sir Robert's second wife, Margaret daughter of Simon Fraser, 6th Lord Lovat.\\n\\nHe married, firstly, Marjory Carnegie( daughter of David Carnegie, 1st Earl of Southesk), sometime before 1639, and had two children:\\n\\nHe married, secondly, his first cousin Katherine Fraser( daughter of Hugh Fraser, 7th Lord Lovat, on 30 June 1653 and had further children.\\n\\nHe was knighted by King Charles I for his enduring loyalty and was a Ruling Elder in the General Assembly of the Church of Scotland in 1641.\\n\\nHe was created 1st Viscount of Arbuthnott, of County Kincardine and 1st Lord Inverbervie, Kincardineshire, on 16 November 1641 by Charles I who was trying to persuade him to support the Royalist cause.\\nChristopher Edward Berkeley Portman, 10th Viscount Portman( born 30 July 1958) is a British peer and property developer.\\nJohn Arbuthnott, 9th Viscount of Arbuthnott, DL, JP (4 June 1806 \u2013 26 May 1891) was a Scottish peer and soldier.\\n\\nBorn at Arbuthnott House, he was the oldest son of John Arbuthnott, 8th Viscount of Arbuthnott and his wife Margaret, daughter of Walter Ogilvy, de jure 8th Earl of Airlie.\\n\\nIn 1860, he succeeded his father as viscount.\\n\\nArbuthnott was educated at Corpus Christi College, Oxford and matriculated on 28 June 1824.\\n\\nHe was commissioned Cornet in the 6th Dragoons in 1825 and purchased the ranks of Lieutenant in 1826 and Captain in 1830.\\n\\nHe retired as Major on half-pay in 1850.\\n\\nHe served as Deputy Lieutenant for Kincardineshire.\\n\\nOn 5 June 1837, he married his cousin Lady Jean (or Jane)\\n\\nGraham Drummond Ogilvy (born Midlothian 27 February 1818; died 4 March 1902), eldest daughter of David Ogilvy, 9th Earl of Airlie at Cortachy Castle, Angus.\\n\\nArbuthnott died at his residence and was succeeded in his titles by his son John.\\n\\nLady Arbuthnott died at her residence, Arbuthnott house, Fordoun, Kincardineshire, on 4 March 1902.\n\nAnswer the following question: Generate one line summary for the given text."], "reference": ["The text provides information about several members of the Arbuthnott family, including General Sir Hugh Arbuthnot, KCB, and other Viscounts of Arbuthnott, as well as Alexander Arbuthnot, Snr, and Christopher Edward Berkeley Portman, 10th Viscount Portman, highlighting their roles, achievements, and familial relationships."]} +{"qna_file": "compositional_skills/writing/grounded/summarization/wiki_insights/detailed/qna.yaml", "question_id": "99051754285538052635992489565762990107", "category": "taxonomy", "turns": ["Given the context below:\n\"Jean- Pierre L\u00e9vy( born 1935) is a French lawyer, author and diplomat.\\n\\nHe was the director of the United Nations Division for Ocean Affairs and the Law of the Sea in 1985.\\nIan Barry is an Australian director of film and TV.\\nLaw of the Tropics is a 1941 American drama film directed by Ray Enright and starring Constance Bennett, Jeffrey Lynn and Regis Toomey.\\n\\nBy the time Bennett made the film, her career was in steep decline.\\n\\nThe film is loosely based on the 1935 movie \"Oil for the Lamps of China\", but the setting is changed from China to the Amazon jungle, and the tone is somewhat lighter.\\n\\nThe conflict between a man\\'s conscience and his corporate loyalty, which is a principal theme of the original, is more secondary in this film.\\nS.N. Mathur was the Director of the Indian Intelligence Bureau between September 1975 and February 1980.\\n\\nHe was also the Director General of Police in Punjab.\\nPeter Levin is an American director of film, television and theatre.\\nAlessandro Di Robilant( born 23 October 1953) is an Italian film director and screenwriter.\\n\\nHe has directed ten films since 1985.\\n\\nHis 1994 film\" Law of Courage\" was entered into the 44th Berlin International Film Festival where it won the Blue Angel Award.\\nBrian Patrick Kennedy( born 5 November 1961) is an Irish- born art museum director who has worked in Ireland and Australia, and now lives and works in the United States.\\n\\nHe is currently the director of the Peabody Essex Museum.\\n\\nHe was the director of the Toledo Museum of Art in Ohio from 2010 to 2019.\\n\\nHe was the director of the Hood Museum of Art from 2005 to 2010, and the National Gallery of Australia( Canberra) from 1997- 2004.\\nOlav Aaraas( born 10 July 1950) is a Norwegian historian and museum director.\\n\\nHe was born in Fredrikstad.\\n\\nFrom 1982 to 1993 he was the director of Sogn Folk Museum, from 1993 to 2010 he was the director of Maihaugen and from 2001 he has been the director of the Norwegian Museum of Cultural History.\\n\\nIn 2010 he was decorated with the Royal Norwegian Order of St. Olav.\\nJesse Edward Hobson( May 2, 1911 \u2013 November 5, 1970) was the director of SRI International from 1947 to 1955.\\n\\nPrior to SRI, he was the director of the Armour Research Foundation.\\nRay Enright (March 25, 1896 \u2013 April 3, 1965) was an American film director.\\n\\nHe directed 73 films between 1927\u201353, many of them for\\n\\nWarner Bros. He oversaw comedy films like Joe E. Brown vehicles, and five of the six informal pairings of Joan Blondell and Glenda Farrell.\\n\\nEnright was born in Anderson, Indiana, and died in Hollywood, California, from a heart attack.\"\n\nAnswer the following question: Generate a detailed summary for the given text."], "reference": ["\"The text provided gives information about various individuals who are directors in different fields such as film, television, theatre, art museums, and museums related to history and culture.\\n\\nJean-Pierre L\u00e9vy is a French lawyer, author, and diplomat who served as the director of the United Nations Division for Ocean Affairs and the Law of the Sea in 1985. Ian Barry is an Australian director of film and TV. Ray Enright was an American film director who directed 73 films between 1927-53, many of them for Warner Bros. He oversaw comedy films like Joe E. Brown vehicles, and five of the six informal pairings of Joan Blondell and Glenda Farrell. Enright was born in Anderson, Indiana, and died in Hollywood, California, from a heart attack.\\n\\nThe text also mentions two films directed by Ray Enright. The first one is \"Law of the Tropics,\" a 1941 American drama film starring Constance Bennett, Jeffrey Lynn, and Regis Toomey. The film is loosely based on the 1935 movie \"Oil for the Lamps of China,\" but the setting is changed from China to the Amazon jungle, and the tone is somewhat lighter. The second film is \"Law of Courage,\" an Italian film directed by Alessandro Di Robilant in 1994. The film was entered into the 44th Berlin International Film Festival where it won the Blue Angel Award.\\n\\nS.N. Mathur was the Director of the Indian Intelligence Bureau between September 1975 and February 1980. He was also the Director General of Police in Punjab. Peter Levin is an American director of film, television, and theatre. Brian Patrick Kennedy is an Irish-born art museum director who has worked in Ireland and Australia and now lives and works in the United States. He is currently the director of the Peabody Essex Museum. Olav Aaraas is a Norwegian historian and museum director who has been the director of several museums in Norway, including the Norwegian Museum of Cultural History.\\n\\nJesse Edward Hobson was the director of SRI International from 1947 to 1955 and prior to SRI, he was the director of the Armour Research Foundation. Alessandro Di Robilant is an Italian film director and screenwriter who has directed ten films since 1985. He won the Blue Angel Award at the 44th Berlin International Film Festival for his film \"Law of Courage.\"\"\n"]} +{"qna_file": "compositional_skills/writing/grounded/summarization/wiki_insights/high_level_outline/qna.yaml", "question_id": "282393908888373446465050508915410740306", "category": "taxonomy", "turns": ["Given the context below:\n\"Brooks Benedict( born Harold J. Mann, February 6, 1896 \u2013 January 1, 1968) was an American actor of the silent and sound film era, where he played supporting and utility roles in over 300 films, mostly uncredited.\\nDetective Chief Inspector Walter Dew( 17 April 1863 \u2013 16 December 1947) was a British Metropolitan Police officer who was involved in the hunt for both Jack the Ripper and Dr Crippen.\\nJay Benedict( born April 11, 1951) is an American actor, best known in the United Kingdom for his role as Captain/ Major John Kieffer in\" Foyle\\'s War\"( in the episodes\" Invasion\" and\" All Clear\").\\nTerry Frazee is an American special effects artist.\\n\\nHe was nominated at the 76th Academy Awards in the category of Best Visual Effects for his work on.\\n\\nHe shared his nomination with Charles Gibson, Hal Hickel and John Knoll.\\nCleveland Keith\" Cleve\" Benedict( born March 21, 1935) is a retired Republican politician from West Virginia.\\nJack E. Hickel( born October 1, 1950, in Anchorage, Alaska) is an American physician and humanitarian.\\nBrian Benedict( born December 27, 1968) is a former U.S. soccer forward.\\n\\nBenedict was a significant player with the U.S. national youth teams and earned four caps with the U.S. national team in 1991 and 1992.\\nPinckney Benedict( born 1964) is an American short- story writer and novelist whose work often reflects his Appalachian background.\\nStanley Rossiter Benedict( 17 March\\n\\n1884 \u2013 21 December 1936) was an American chemist best known for discovering Benedict\\'s reagent, a solution that detects certain sugars.\\n\\nBenedict was born in Cincinnati, and went to the University of Cincinnati.\\n\\nAfter a year, he went to Yale\\'s Department of Physiological Chemistry for training in metabolism and physiology.\\n\\nHe married Ruth Fulton.\\n\\nHowever, it collapsed and his wife began a career in anthropology as Ruth Benedict.\\nBruce Edwin Benedict( born August 18, 1955) is an American former professional baseball player, coach and scout.\\n\\nHe played 12 seasons in Major League Baseball as a catcher for the Atlanta Braves\"\n\nAnswer the following question: Generate a high-level outline for the given text."], "reference": ["\"I. Brooks Benedict\\n A. American actor of the silent and sound film era\\n B. Played supporting and utility roles in over 300 films, mostly uncredited\\n\\nII. Detective Chief Inspector Walter Dew\\n A. British Metropolitan Police officer\\n B. Involved in the hunt for both Jack the Ripper and Dr Crippen\\n\\nIII. Jay Benedict\\n A. American actor\\n B. Best known in the United Kingdom for his role in \"Foyle\\'s War\"\\n\\nIV. Terry Frazee\\n A. American special effects artist\\n B. Nominated at the 76th Academy Awards for Best Visual Effects\\n\\nV. Cleveland Keith \"Cleve\" Benedict\\n A. Retired Republican politician from West Virginia\\n\\nVI. Jack E. Hickel\\n A. American physician and humanitarian\\n\\nVII. Brian Benedict\\n A. Former U.S. soccer forward\\n B. Significant player with the U.S. national youth teams\\n C. Earned four caps with the U.S. national team in 1991 and 1992\\n\\nVIII. Pinckney Benedict\\n A. American short-story writer and novelist\\n B. Work often reflects Appalachian background\\n\\nIX. Stanley Rossiter Benedict\\n A. American chemist\\n B. Discovered Benedict\\'s reagent, a solution that detects certain sugars\\n C. Born in Cincinnati and went to the University of Cincinnati\\n D. Went to Yale\\'s Department of Physiological Chemistry for training in metabolism and physiology\\n E. Married Ruth Fulton, who later became an anthropologist as Ruth Benedict\\n\\nX. Bruce Edwin Benedict\\n A. American former professional baseball player, coach, and scout\\n B. Played 12 seasons in Major League Baseball as a catcher for the Atlanta Braves\"\n"]} +{"qna_file": "compositional_skills/writing/grounded/summarization/wiki_insights/concise/qna.yaml", "question_id": "16529173480964240006896307031708521798", "category": "taxonomy", "turns": ["Given the context below:\nQurt Tappeh( also Romanized as Q\u016brt Tappeh; also known as Q\u016bt Tappeh) is a village in Torkaman Rural District, in the Central District of Urmia County, West Azerbaijan Province, Iran.\n\nAt the 2006 census, its population was 316, in 85 families.\nShir Shotor( also Romanized as Sh\u012br Shotor and Sh\u012br Shutur) is a village in Shusef Rural District, Shusef District, Nehbandan County, South Khorasan Province, Iran.\n\nAt the 2006 census, its population was 93, in 26 families.\nQurt Tappeh( also Romanized as Q\u016brt Tappeh; also known as Q\u016bt Tappeh) is a village in Torkaman Rural District, in the Central District of Urmia County, West Azerbaijan Province, Iran.\n\nAt the 2006 census, its population was 316, in 85 families.\nJannatabad -e Jangal( also Romanized as Jannat\u0101b\u0101d- e Jangal; also known as Jannat\u0101b\u0101d, Jannat\u0101b\u0101d -e Sh\u012br Mo\u1e29ammad, and Shir Mo\u1e29ammad) is a village in Jangal Rural District, Jangal District, Roshtkhar County, Razavi Khorasan Province, Iran.\n\nAt the 2006 census, its population was 2,504, in 563 families.\nQurt Tappeh( also Romanized as Q\u016brt Tappeh; also known as Ghoort Tappeh) is a village in Arshaq -e Gharbi Rural District, Moradlu District, Meshgin Shahr County, Ardabil Province, Iran.\n\nAt the 2006 census, its population was 961, in 201 families.\nJannatabad -e Jangal( also Romanized as Jannat\u0101b\u0101d- e Jangal; also known as Jannat\u0101b\u0101d, Jannat\u0101b\u0101d -e Sh\u012br Mo\u1e29ammad, and Shir Mo\u1e29ammad) is a village in Jangal Rural District, Jangal District, Roshtkhar County, Razavi Khorasan Province, Iran.\n\nAt the 2006 census, its population was 2,504, in 563 families.\nShir Shotor or Shir Shutur may refer to:\nQurt Tappeh may refer to:\nQurt Tappeh( also Romanized as Q\u016brt Tappeh; also known as Q\u016br Tappeh) is a village in Dasht Rural District, in the Central District of Meshgin Shahr County, Ardabil Province, Iran.\n\nAt the 2006 census, its population was 1,447, in 317 families.\nShir Shotor( also Romanized as Sh\u012br Shotor) is a village in Tabadkan Rural District, in the Central District of Mashhad County, Razavi Khorasan Province, Iran.\n\nAt the 2006 census, its existence was noted, but its population was not reported.\nAnswer the following question: Generate a concise summary for the given text."], "reference": ["Qurt Tappeh is a name of several villages in West Azerbaijan, Ardabil, and Razavi Khorasan provinces of Iran. In 2006, the population of Qurt Tappeh in West Azerbaijan was 316, in Ardabil it was 961, and in Razavi Khorasan it was 1,447. Shir Shotor is another village in Razavi Khorasan and its population was not reported in 2006. Jannatabad-e Jangal is a village in Razavi Khorasan with a population of 2,504 in 2006."]} +{"qna_file": "compositional_skills/writing/grounded/summarization/wiki_insights/five_point/qna.yaml", "question_id": "92575576167789410493952474913230199219", "category": "taxonomy", "turns": ["Given the context below:\nChester High School is a public high school in Chester, Illinois, United States.\nGwacheon Foreign Language High school (Hangeul: \uacfc\ucc9c\uc678\uad6d\uc5b4\uace0\ub4f1\ud559\uad50, \uacfc\ucc9c\uc678\uace0) is an Independent High School located in Gyeong-gi province, Gwacheon-si, South Korea.\n\nThe school was established on 5 May 1990 and It is the first Foreign Language High School established in Gyeonggi-do.\n\nGwacheon Foreign Language High school is a special high school in South Korea.\n\nStudents in this school would be able to learn foreign languages in more details compared to students in ordinary high school.\n\n'Kwacheon Foreign Language High School' is an old name which was used before the revision of Korean romanization.\nHanyoung Foreign Language High School (Hangeul: \ud55c\uc601\uc678\uad6d\uc5b4\uace0\ub4f1\ud559\uad50, Hanja: \u6f22\u69ae\u5916\u570b\u8a9e\u9ad8\u7b49\u5b78\u6821) is one of the most prestigious high schools in South Korea.\n\nLocated in southeast Seoul, South Korea, the medium-sized college preparatory school was founded in 1990 with the goal of educating successful leaders of the future, and is one of the six foreign-language high schools (specialized high schools) in Seoul.\n\nThe school, like other foreign language high schools, selects its students through an entrance exam, through which students are given scores for each category: middle school English grades, an essay, and an interview.\nCalloway County\n\nHigh School is a public high school located in Murray, Kentucky.\n\nThe school was formed from the consolidation of six high schools from across the county: Hazel High School, Lynn Grove High School, Kirksey High School, Almo High School, New Concord High School, and Faxon High School.\nSeoul Foreign Language High School is private preparatory school located in Seoul.\n\nClassified as a Foreign Language High School under the Ministry of Education, Science, and Technology's categorization of Special Purpose High Schools, Seoul FLHS's curriculum is centered on the specialized education of various European and Asian languages.\n\nSeoul Foreign Language High School is the sixth foreign language high school to be established in South Korea.\n\nThe school's primary language of instruction is Korean.\n\nLike most schools in South Korea, SFLHS starts its school year in March, starts its second semester in September, and ends the school year in February.\nMountain View High School is a public high school located in Vancouver, Washington.\n\nIt was the second high school built in the Evergreen Public Schools, and one of four high schools in the area.\nDover High School is a high school located in Dover, Delaware.\nFranklin High School is a public high school located in Franklin, Massachusetts.\nChester High School is an urban, public high school located in Chester, Delaware County, Pennsylvania, United States with a ZIP code of 19013- 4288.\n\nChester is a part of the Chester- Upland School District.\n\nThe school serves the City of Chester, Chester Township, and Upland.\n\nIn 2009- 10, the school had 1,302 pupils, with 779 pupils qualifying for a federal free or reduced- price lunch.\n\nThe school employed 78 teachers yielding a Student/ Teacher Ratio of 16.69.\n\nIn 2011, 26 of the teachers were designated Non\u2010Highly Qualified Teachers under No Child Left Behind.\n\nQualifying students may choose to attend one of the district's two magnet high schools Smedley Allied Health Campus or Science and Discovery Campus.\n\nThe school's first floor has the headquarters of the school district.\nMyung Duk Foreign Language High School (Hangeul: \uba85\ub355\uc678\uad6d\uc5b4\uace0\ub4f1\ud559\uad50, Hanja: \u660e\u5fb7\u5916\u570b\u8a9e\u9ad8\u7b49\u5b78\u6821) is a foreign language high school located in the Gang-seo District of Seoul, Korea.\nAnswer the following question: Give a five point summary for the given text."], "reference": ["1. Chester High School is a public high school located in Chester, Pennsylvania, serving the City of Chester, Chester Township, and Upland. It is part of the Chester-Upland School District and had 1,302 pupils in 2009-2010, with a significant number qualifying for free or reduced-price lunches.\n2. Gwacheon Foreign Language High School is a specialized high school in South Korea, located in Gyeong-gi province, Gwacheon-si. Established in 1990, it was the first foreign language high school in Gyeonggi-do, offering detailed foreign language education to its students.\n3. Hanyoung Foreign Language High School is one of the most prestigious high schools in South Korea, located in southeast Seoul. Founded in 1990, it is a college preparatory school that focuses on educating future leaders and is one of the six foreign-language high schools in Seoul.\n4. Seoul Foreign Language High School is a private preparatory school located in Seoul, classified as a Foreign Language High School. Its curriculum focuses on the specialized education of various European and Asian languages, with Korean as the primary language of instruction.\n5. Myung Duk Foreign Language High School is a foreign language high school located in the Gang-seo District of Seoul, Korea. It offers a specialized education in foreign languages to its students."]} +{"qna_file": "compositional_skills/writing/grounded/editing/grammar/qna.yaml", "question_id": "304124921715353011869737533488848462171", "category": "taxonomy", "turns": ["Given the context below:\nHenry Edwards (18 September 1882 \u2013 2 November 1952) was an English actor and film directer.\\n\\nHe appear in 81 films between 1915 and 1952.\\n\\nHe also direct 67 films between 1915 and 1937.\\n\\nEdwards was married to actress Chrissie White, who co-starred in a number of his films.\\n\\nHe was born in Weston-super-Mare, Somerset and died in Chobham, Surrey.\\nMehdi Abrishamchi is an Iranian People\\'s Mujahedin of Iran( MEK) politican who has been described as\" the right hand man of Massoud Rajavi\".\\n\\nHe is the former spouse of the group\\'s current leader, Maryam Rajavi, whom he divorced so that she could become the spouse of Massoud Rajavi.\\nGertrude of Saxony and Bavaria( 1152/55\u20131197) was Duchess of Swabia as the spouse of Duke Frederick IV, and Queen of Denmark as the spouse of King Canute VI.\\nAlfred Xuereb( born 14 October 1958 in Victoria on the Maltese island of Gozo) is a Roman Catholic Church archbishop who is the Apostolic Nuncio to both South Korea and Mongolia.\\n\\nXuereb served as first private secretary of Pope Francis.\\n\\nBefore being selected, he had served, next to the private secretary Georg G\u00e4nswein, as second private secretary of Pope Benedict XVI.\\nSir Edward Young,( born 24 October 1966) is the Private Secretary to Queen Elizabeth II.\\n\\nAs Private Secretary to the Sovereign, he is the senior operational member of the Royal Households of the United Kingdom.\\n\\nYoung was recruited to the Royal Household in 2004, serving as the Queen\\'s Assistant and then Deputy Private Secretary until his promotion to Private Secretary in 2017.\\n\\nHe is the 24th holder of the office of Private Secretary since its inception in 1805.\\nThe Private Secretary is a 1935 British comedy film direct by Henry Edwards and starring Edward Everett Horton, Barry MacKay, Judy Gunn and Oscar Asche.\\n\\nIt is an adaptation of the play \"The Private Secretary\" by Charles Henry Hawtrey.\\n\\nIt was made at Twickenham Studios.\\nSophia Magdalena of Denmark (3 July 1746 \u2013 21 August 1813) was Queen of Sweden as the spouse of King Gustav III.\\nMaria Teresa, Grand Duchess of Luxembourg( born Mar\u00eda Teresa Mestre y Batista; on 22 March 1956), is the spouse of Grand Duke Henri.\\nThe Private Secretary is a 1953 German comedy film direct by Paul Martin and starring Sonja Ziemann, Rudolf Prack and Paul H\u00f6rbiger.\\n\\nIt was a remake of the 1931 German film\" The Private Secretary\".\\n\\nIt was shot at the Wandsbek Studios and on location in Hamburg.\\n\\nThe film\\'s sets were designed by Hermann Warm and Alfons Windau.\\nMarie Louise Coidavid( 1778 \u2013 March 11, 1851), was the Queen of the Kingdom of Haiti 1811 \u2013 20 as the spouse of Henri I of Haiti.\\n\\nShe was the first queen of Haiti.\\n\\nMarie Louise Coidavid was born in Milot, Haiti.\\n\\nShe died in Pisa, Italy.\n\nAnswer the following question: Could you correct any grammatical errors in the given text?"], "reference": ["Henry Edwards (18 September 1882 \u2013 2 November 1952) was an English actor and film director.\\n\\nHe appeared in 81 films between 1915 and 1952.\\n\\nHe also directed 67 films between 1915 and 1937.\\n\\nEdwards was married to actress Chrissie White, who co-starred in a number of his films.\\n\\nHe was born in Weston-super-Mare, Somerset and died in Chobham, Surrey.\\nMehdi Abrishamchi is an Iranian People\\'s Mujahedin of Iran( MEK) politician who has been described as\" the right hand man of Massoud Rajavi\".\\n\\nHe is the former spouse of the group\\'s current leader, Maryam Rajavi, whom he divorced so that she could become the spouse of Massoud Rajavi.\\nGertrude of Saxony and Bavaria( 1152/55\u20131197) was Duchess of Swabia as the spouse of Duke Frederick IV, and Queen of Denmark as the spouse of King Canute VI.\\nAlfred Xuereb( born 14 October 1958 in Victoria on the Maltese island of Gozo) is a Roman Catholic Church archbishop who is the Apostolic Nuncio to both South Korea and Mongolia.\\n\\nXuereb served as first private secretary of Pope Francis.\\n\\nBefore being selected, he had served, next to the private secretary Georg G\u00e4nswein, as second private secretary of Pope Benedict XVI.\\nSir Edward Young,( born 24 October 1966) is the Private Secretary to Queen Elizabeth II.\\n\\nAs Private Secretary to the Sovereign, he is the senior operational member of the Royal Households of the United Kingdom.\\n\\nYoung was recruited to the Royal Household in 2004, serving as the Queen\\'s Assistant and then Deputy Private Secretary until his promotion to Private Secretary in 2017.\\n\\nHe is the 24th holder of the office of Private Secretary since its inception in 1805.\\nThe Private Secretary is a 1935 British comedy film directed by Henry Edwards and starring Edward Everett Horton, Barry MacKay, Judy Gunn and Oscar Asche.\\n\\nIt is an adaptation of the play \"The Private Secretary\" by Charles Henry Hawtrey.\\n\\nIt was made at Twickenham Studios.\\nSophia Magdalena of Denmark (3 July 1746 \u2013 21 August 1813) was Queen of Sweden as the spouse of King Gustav III.\\nMaria Teresa, Grand Duchess of Luxembourg( born Mar\u00eda Teresa Mestre y Batista; on 22 March 1956), is the spouse of Grand Duke Henri.\\nThe Private Secretary is a 1953 German comedy film directed by Paul Martin and starring Sonja Ziemann, Rudolf Prack and Paul H\u00f6rbiger.\\n\\nIt was a remake of the 1931 German film\" The Private Secretary\".\\n\\nIt was shot at the Wandsbek Studios and on location in Hamburg.\\n\\nThe film\\'s sets were designed by Hermann Warm and Alfons Windau.\\nMarie Louise Coidavid( 1778 \u2013 March 11, 1851), was the Queen of the Kingdom of Haiti 1811 \u2013 20 as the spouse of Henri I of Haiti.\\n\\nShe was the first queen of Haiti.\\n\\nMarie Louise Coidavid was born in Milot, Haiti.\\n\\nShe died in Pisa, Italy.\"\n"]} +{"qna_file": "compositional_skills/writing/grounded/editing/punctuation/qna.yaml", "question_id": "51857537720642249155183340382776178931", "category": "taxonomy", "turns": ["Given the context below:\nDeswa, is an Indian Bhojpuri language film, written and directed by Nitin Chandra. The film, is a social drama - crime thriller Bhojpuri genre. The first film to be produced by actress Neetu Chandra. The film introduces new actors Ajay Kumar, Deepak Singh and Kranti Prakash Jha in lead roles along with critically acclaimed actor Pankaj Jha and very known Ashish Vidyarthi. In 2010 Neetu Chandra had expressed a hope that it would move beyond the limited release that is common for Bhojpuri films and instead be seen throughout India and perhaps also abroad. She intends that the film would employ multilingual dialogue - Bhojpuri, English, Telugu and Hindi - as well as being dubbed into Tamil and Marathi in order to extend its appeal. She believ that \"With \"Deswa\", we would be redefining the very face of Bhojpuri cinema.\" Deswa is the first Bhojpuri film in 50 years to get selected at Indian Panorama section of International Film Festival of India, which was enough to support the vision of Neetu Chandra. \n\nAnswer the following question: Return the text after correcting any punctuation errors."], "reference": ["Deswa is an Indian Bhojpuri language film written and directed by Nitin Chandra.\\n\\nThe film is a social drama - crime thriller Bhojpuri genre.\\n\\nThe first film to be produced by actress Neetu Chandra.\\n\\nThe film introduces new actors Ajay Kumar, Deepak Singh and Kranti Prakash Jha in lead roles along with critically acclaimed actor Pankaj Jha and very known Ashish Vidyarthi.\\n\\nIn 2010 Neetu Chandra had expressed a hope that it would move beyond the limited release that is common for Bhojpuri films and instead be seen throughout India and perhaps also abroad.\\n\\nShe intends that the film would employ multilingual dialogue - Bhojpuri, English, Telugu and Hindi - as well as being dubbed into Tamil and Marathi in order to extend its appeal.\\n\\nShe believ that \"With \"Deswa\", we would be redefining the very face of Bhojpuri cinema.\"\\n\\nDeswa is the first Bhojpuri film in 50 years to get selected at Indian Panorama section of International Film Festival of India, which was enough to support the vision of Neetu Chandra."]} +{"qna_file": "compositional_skills/writing/grounded/editing/spelling/qna.yaml", "question_id": "172908278663393480434522038596881851444", "category": "taxonomy", "turns": ["Given the context below:\nQrah Tappeh( also Romanized as Qareh Tappeh; also known as Qar\u0101 Tappeh, Qara Tepe, and R\u016bst\u0101 -ye Qarah Tappeh) is a village in Rahal Rural District, in the Central District of Khoy County, West Azerbaijan Prvince, Irn.\\n\\nAt the 2006 census, its population was 422, in 93 familes.\\nQarah Tappeh( also known as Qarah Tappeh- ye Sheykhl\u016b) is a village in Vilkij -e Markazi Rural District, Vilkij District, Namin County, Ardabil Prvince, Iran.\\n\\nAt the 2006 census, its population was 667, in 138 families.\\nHeser -e Qarah Tappeh( also Romanized as \u1e28e\u015f\u0101r -e Qarah Tappeh and \u1e28e\u015f\u0101r -e Qareh Tappeh) is a village in Rahal Rural District, in the Central District of Khoy County, West Azerbaijan Prvince, Iran.\\n\\nAt the 2006 census, its population was 124, in 27 families.\\nQarah Tappeh( also Romanized as Qareh Tappeh) is a village in Ab Barik Rural District, in the Central District of Sonqor County, Kermanshah Prvince, Iran.\\n\\nAt the 2006 census, its population was 420, in 128 families.\\nQarah Tappeh is a village in Razakan Rural District, in the Central District of Shahriar County, Tehran Prvince, Iran.\\n\\nAt the 2006 census, its population was 265, in 61 families.\\nJuzir( also Romanized as J\u016bz\u012br; also known as J\u0101z, Jowz\u0101n, and J\u016bz\u012bn) is a village in Rudkhaneh Bar Rural District, Rudkhaneh District, Rudan County, Hormozgan Prvince, Iran.\\n\\nAt the 2006 census, its population was 291, in 61 families.\\nQarah Tappeh( also Romanized as Qareh Tappeh; also known as Qaratepe) is a village in Mahmudabad Rural District, in the Central District of Shahin Dezh County, West Azerbaijan Prvince, Iran.\\n\\nAt the 2006 census, its population was 531, in 137 families.\\nQarah Tappeh( also Romanized as Qareh Tappeh; also known as Qaratepe) is a village in Miyan Kaleh Rural District, in the Central District of Behshahr County, Mazandaran Prvince, Iran.\\n\\nAt the 2006 census, its population was 1,884, in 504 families.\\nQarah Tappeh is a village in Baladarband Rural District, in the Central District of Kermanshah County, Kermanshah Prvince, Iran.\\n\\nAt the 2006 census, its population was 92, in 23 families.\\nQarah Tappeh- ye Sabalan( also Romanized as Qarah Tappeh- ye Sabal\u0101n; also known as Qarah Tappeh) is a village in Sardabeh Rural District, in the Central District of Ardabil County, Ardabil Prvince, Iran.\\n\\nAt the 2006 census, its population was 877, in 174 families.\n\nAnswer the following question: Fix any spelling errors in the given text."], "reference": ["Qarah Tappeh( also Romanized as Qareh Tappeh; also known as Qar\u0101 Tappeh, Qara Tepe, and R\u016bst\u0101 -ye Qarah Tappeh) is a village in Rahal Rural District, in the Central District of Khoy County, West Azerbaijan Province, Iran.\\n\\nAt the 2006 census, its population was 422, in 93 families.\\nQarah Tappeh( also known as Qarah Tappeh- ye Sheykhl\u016b) is a village in Vilkij -e Markazi Rural District, Vilkij District, Namin County, Ardabil Province, Iran.\\n\\nAt the 2006 census, its population was 667, in 138 families.\\nHesar -e Qarah Tappeh( also Romanized as \u1e28e\u015f\u0101r -e Qarah Tappeh and \u1e28e\u015f\u0101r -e Qareh Tappeh) is a village in Rahal Rural District, in the Central District of Khoy County, West Azerbaijan Province, Iran.\\n\\nAt the 2006 census, its population was 124, in 27 families.\\nQarah Tappeh( also Romanized as Qareh Tappeh) is a village in Ab Barik Rural District, in the Central District of Sonqor County, Kermanshah Province, Iran.\\n\\nAt the 2006 census, its population was 420, in 128 families.\\nQarah Tappeh is a village in Razakan Rural District, in the Central District of Shahriar County, Tehran Province, Iran.\\n\\nAt the 2006 census, its population was 265, in 61 families.\\nJuzir( also Romanized as J\u016bz\u012br; also known as J\u0101z, Jowz\u0101n, and J\u016bz\u012bn) is a village in Rudkhaneh Bar Rural District, Rudkhaneh District, Rudan County, Hormozgan Province, Iran.\\n\\nAt the 2006 census, its population was 291, in 61 families.\\nQarah Tappeh( also Romanized as Qareh Tappeh) is a village in Mahmudabad Rural District, in the Central District of Shahin Dezh County, West Azerbaijan Province, Iran.\\n\\nAt the 2006 census, its population was 531, in 137 families.\\nQarah Tappeh( also Romanized as Qareh Tappeh; also known as Qaratepe) is a village in Miyan Kaleh Rural District, in the Central District of Behshahr County, Mazandaran Province, Iran.\\n\\nAt the 2006 census, its population was 1,884, in 504 families.\\nQarah Tappeh is a village in Baladarband Rural District, in the Central District of Kermanshah County, Kermanshah Province, Iran.\\n\\nAt the 2006 census, its population was 92, in 23 families.\\nQarah Tappeh- ye Sabalan( also Romanized as Qarah Tappeh- ye Sabal\u0101n; also known as Qarah Tappeh) is a village in Sardabeh Rural District, in the Central District of Ardabil County, Ardabil Province, Iran.\\n\\nAt the 2006 census, its population was 877, in 174 families.\n"]} +{"qna_file": "compositional_skills/writing/grounded/meeting_insights/action_items/qna.yaml", "question_id": "286991252520121496543634306043573030602", "category": "taxonomy", "turns": ["Given the context below:\n*Ms. Thompson:* Good morning, everyone. Today, we're here to discuss employee retention strategies. I believe this is crucial for our company's growth and success.\n\n*Mr. Patel:* Absolutely, Karen. We've seen a slight increase in turnover recently, and it's essential that we address this issue promptly.\n\n*Ms. Chen:* One strategy could be enhancing our onboarding process. A well-structured onboarding program can help new hires feel more engaged and committed to the company.\n\n*Mr. Johnson:* I agree, Lisa. We should also focus on continuous learning and development opportunities. Providing employees with the chance to grow professionally can significantly improve retention rates.\n\n*Ms. Davis:* From an HR perspective, we should consider implementing a mentorship program. Pairing experienced employees with newcomers can foster a sense of community and support.\n\n*Mr. Brown:* We should also look into improving our recognition and reward system. Recognizing employees' hard work and achievements can go a long way in making them feel valued.\n\n*Ms. Green:* Absolutely, Michael. And we shouldn't forget about work-life balance. Offering flexible work arrangements and promoting well-being initiatives can contribute to employee satisfaction and retention.\n\n*Mr. Taylor:* I suggest we conduct regular employee surveys to gather feedback and understand their concerns. This will help us create targeted retention strategies.\n\n*Ms. Thompson:* Great ideas, everyone. Let's assign tasks and reconvene in a week to discuss progress and refine our strategies.\n\n*Mr. Patel:* I'll work with HR to enhance our onboarding process.\n\n*Ms. Chen:* I'll coordinate with department heads to create a continuous learning and development plan.\n\n*Ms. Davis:* I'll design a mentorship program and present it to the team.\n\n*Mr. Brown:* I'll review our recognition and reward system and propose improvements.\n\n*Ms. Green:* I'll develop a well-being initiative and share it with everyone.\n\n*Mr. Taylor:* I'll draft an employee survey and send it out for feedback.\n\n*Ms. Thompson:* Excellent. Let's make employee retention a top priority.\nAnswer the following question: Get all the action items from the given meeting transcript."], "reference": ["1. Enhance the onboarding process to engage and commit new hires.\n2. Focus on continuous learning and development opportunities.\n3. Implement a mentorship program to foster a sense of community and support.\n4. Improve the recognition and reward system to make employees feel valued.\n5. Promote work-life balance through flexible work arrangements and well-being initiatives.\n6. Conduct regular employee surveys to gather feedback and understand concerns.\n7. Assign tasks to relevant team members and reconvene in a week to discuss progress and refine strategies.\n8. Make employee retention a top priority for the company's growth and success."]} +{"qna_file": "compositional_skills/writing/grounded/meeting_insights/minutes_of_meeting/qna.yaml", "question_id": "285046489754331582753422303715386398429", "category": "taxonomy", "turns": ["Given the context below:\n*Ms. Patel:* Good morning, everyone. Today, we're here to discuss ways to enhance employee engagement. I'd love to hear your ideas.\n\n*Mr. Thompson:* I think regular recognition of employee achievements could be a great start. A monthly shout-out in our company newsletter or a quarterly awards ceremony could work wonders.\n\n*Ms. Rodriguez:* I agree, but let's not forget about personal development. Offering workshops, training sessions, and mentorship programs can help employees feel valued and grow professionally.\n\n*Mr. Kim:* Absolutely. And we should consider creating opportunities for cross-departmental collaboration. This not only fosters innovation but also helps employees understand the bigger picture of our organization.\n\n*Ms. Patel:* Great suggestions! How about implementing a suggestion box system where employees can anonymously share their ideas or concerns?\n\n*Mr. Thompson:* That's a fantastic idea, Rita. It promotes open communication and gives everyone a voice.\n\n*Ms. Rodriguez:* We could also organize team-building activities outside of work hours. Potlucks, game nights, or volunteer events can strengthen relationships and foster a sense of community.\n\n*Mr. Kim:* And let's not forget about work-life balance. Encouraging flexible work hours and remote work options can help employees manage their personal lives while staying committed to their jobs.\n\n*Ms. Patel:* Yes, absolutely. We should ensure that our policies support a healthy work-life balance. Any other ideas?\n\n*Mr. Thompson:* How about creating a wellness program that includes fitness challenges, meditation sessions, or stress management workshops?\n\n*Ms. Rodriguez:* That's a wonderful idea, Tom. A healthy employee is a happy employee.\n\n*Mr. Kim:* And we could establish an Employee Resource Group to support diversity and inclusion initiatives. This can help employees feel more connected and included.\n\n*Ms. Patel:* Wonderful ideas, everyone! Let's take some time to evaluate these suggestions and create an action plan. We'll reconvene next week to discuss the next steps.\nAnswer the following question: Could you generate the minutes of the meeting from the given transcript?"], "reference": ["**Minutes of Meeting**\n\n**Date:** [Insert Date]\n\n**Time:** [Insert Time]\n\n**Location:** [Insert Location]\n\n**Present:**\n- Ms. Patel\n- Mr. Thompson\n- Ms. Rodriguez\n- Mr. Kim\n\n**Agenda: Employee Engagement Initiatives**\n\n**Opening Remarks:**\nMs. Patel welcomed everyone to the meeting, emphasizing the importance of enhancing employee engagement.\n\n**Recognition of Employee Achievements:**\nMr. Thompson suggested implementing regular recognition of employee achievements through a monthly shout-out in the company newsletter or a quarterly awards ceremony.\n\n**Personal Development:**\nMs. Rodriguez proposed offering workshops, training sessions, and mentorship programs to help employees feel valued and grow professionally.\n\n**Cross-Departmental Collaboration:**\nMr. Kim suggested creating opportunities for cross-departmental collaboration to foster innovation and help employees understand the bigger picture of the organization.\n\n**Suggestion Box System:**\nMs. Patel proposed implementing a suggestion box system for anonymous sharing of ideas or concerns, promoting open communication.\n\n**Team-Building Activities:**\nMs. Rodriguez suggested organizing team-building activities outside of work hours, such as potlucks, game nights, or volunteer events, to strengthen relationships and foster a sense of community.\n\n**Work-Life Balance:**\nMr. Kim emphasized the importance of work-life balance, encouraging flexible work hours and remote work options.\n\n**Wellness Program:**\nMr. Thompson proposed creating a wellness program that includes fitness challenges, meditation sessions, or stress management workshops to promote employee health and happiness.\n\n**Employee Resource Group:**\nMr. Kim suggested establishing an Employee Resource Group to support diversity and inclusion initiatives, helping employees feel more connected and included.\n\n**Action Plan:**\nThe group agreed to evaluate the suggestions and create an action plan, reconvening the following week to discuss the next steps.\n\n**Closing Remarks:**\nMs. Patel thanked everyone for their valuable input, expressing excitement about implementing the new employee engagement initiatives.\n\n**Next Meeting:**\n[Insert Date and Time for the next meeting]\n\n**Prepared by:** [Insert Name]\n**Approved by:** [Insert Name]"]} +{"qna_file": "compositional_skills/writing/grounded/meeting_insights/executive_summaries/qna.yaml", "question_id": "4329367676895312865863579242546135341", "category": "taxonomy", "turns": ["Given the context below:\n*Ms. Thompson:* Good morning, everyone. Today, we're here to discuss the importance of financial literacy training for our employees.\n\n*Mr. Patel:* I believe that financial literacy is crucial for everyone, especially in today's economic climate. It can help employees make informed decisions about their personal finances and retirement planning.\n\n*Ms. Rodriguez:* Absolutely. We should also consider the potential impact on their overall job satisfaction and productivity.\n\n*Mr. Kim:* I agree. But how do we go about implementing this? Should we bring in external trainers or develop an internal program?\n\n*Ms. Thompson:* That's a great question, John. I think we should explore both options. Let's invite a few external trainers to give presentations and evaluate their expertise and approach.\n\n*Mr. Patel:* At the same time, we can start developing an internal program. We have several experts in our finance department who could create engaging and informative content.\n\n*Ms. Rodriguez:* We could also consider offering one-on-one financial coaching sessions for those who need more personalized guidance.\n\n*Mr. Kim:* That's a good idea. But we must ensure that these sessions are confidential to respect the privacy of our employees.\n\n*Ms. Thompson:* Absolutely. Confidentiality is paramount. We'll work with HR to establish clear guidelines and protocols.\n\n*Mr. Patel:* Another point to consider is the frequency of these trainings. Should we offer them annually, semi-annually, or quarterly?\n\n*Ms. Thompson:* Let's start with annual trainings and then assess the need for more frequent sessions based on feedback and participation rates.\n\n*Ms. Rodriguez:* And we should promote these trainings extensively so that all employees are aware of their availability.\n\n*Mr. Kim:* Yes, and we should track attendance and engagement to measure the success of the program.\n\n*Ms. Thompson:* Great suggestions, everyone. Let's move forward with this plan and reconvene in a month to evaluate our progress.\nAnswer the following question: Generate an executive summary of the given meeting transcript."], "reference": ["The executive summary of the financial literacy training meeting highlights the importance of financial literacy for employees, its potential impact on job satisfaction and productivity, the exploration of both external and internal training options, the possibility of one-on-one coaching sessions, the need for confidentiality, the frequency of trainings, and the plan to promote extensively, track attendance, and reconvene for progress evaluation."]} +{"qna_file": "compositional_skills/writing/grounded/meeting_insights/corporate_email/qna.yaml", "question_id": "68145375169603056829554040499372754818", "category": "taxonomy", "turns": ["Given the context below:\n*Ms. Thompson:* Good morning, everyone. Today, we're here to discuss our customer journey mapping and analysis. I believe this is crucial to understanding our customers' experiences and improving our services.\n\n*Mr. Patel:* I agree, Lisa. We should start by identifying all touchpoints in our customer journey, from initial contact to post-sale support.\n\n*Ms. Rodriguez:* Yes, and let's not forget about the emotional aspect of the journey. How do our customers feel at each stage? What are their pain points?\n\n*Mr. Kim:* We can use data from our CRM system to track the customer journey and gather insights. This will help us create a detailed, data-driven map.\n\n*Ms. Johnson:* Once we have the map, we can analyze it to identify areas for improvement. Perhaps there are steps where customers drop off or express dissatisfaction.\n\n*Mr. Davis:* We should also consider the customer's perspective. Conducting interviews or surveys can provide valuable insights into their thoughts and feelings.\n\n*Ms. Brown:* Absolutely. And once we've identified areas for improvement, we can develop strategies to address them. This might involve redesigning certain touchpoints, enhancing our communication, or streamlining processes.\n\n*Mr. Smith:* And we must remember to measure the impact of any changes we make. By tracking key performance indicators, we can determine whether our efforts are successful.\n\n*Ms. Thompson:* Great suggestions, everyone. Let's divide into teams to tackle different aspects of the customer journey. Team 1 will focus on pre-sale touchpoints, Team 2 on sales, and Team 3 on post-sale support.\n\n*Mr. Patel:* Sounds good. We'll reconvene in a week to share our findings and discuss next steps.\nAnswer the following question: Generate a company wide email based on the given meeting transcript"], "reference": ["Subject: Recap and Next Steps - Customer Journey Mapping and Analysis\n\nDear [Company Name] Team,\n\nI hope this email finds you well. Yesterday, we gathered to discuss our customer journey mapping and analysis, aiming to understand our customers' experiences and identify opportunities for improvement. The discussions were fruitful, and I want to share a brief overview of the key points and outcomes.\n\n1. **Identifying Touchpoints:**\n Mr. Patel suggested mapping all touchpoints in our customer journey, from initial contact to post-sale support.\n\n2. **Emotional Aspect and Pain Points:**\n Ms. Rodriguez emphasized the importance of considering the emotional aspect of the journey and identifying customers' pain points at each stage.\n\n3. **Data-Driven Mapping:**\n Mr. Kim proposed using data from our CRM system to create a detailed, data-driven customer journey map.\n\n4. **Customer Perspective:**\n Ms. Johnson recommended gathering insights from the customer's perspective through interviews or surveys.\n\n5. **Analysis and Improvement:**\n Ms. Brown suggested analyzing the customer journey map to identify areas for improvement and developing strategies to address them.\n\n6. **Measuring Impact:**\n Mr. Smith stressed the need to measure the impact of any changes made by tracking key performance indicators.\n\nTo facilitate a comprehensive analysis, we have divided into teams to tackle different aspects of the customer journey:\n\n* Team 1: Pre-sale touchpoints\n* Team 2: Sales\n* Team 3: Post-sale support\n\nEach team will share their findings and discuss next steps in a week.\n\nYour engagement and insights have been invaluable in understanding our customers' experiences and identifying opportunities for improvement. I look forward to our continued collaboration as we work towards enhancing our services and delivering exceptional customer experiences.\n\nBest regards,\n\n[Your Full Name]\n[Your Position]\n[Company Name]"]} +{"qna_file": "compositional_skills/writing/freeform/grammar/basic_grammer_tests/qna.yaml", "question_id": "244494806103687568911638445256261320711", "category": "taxonomy", "turns": ["spell lollipop backwards\n"], "reference": ["popillol\n"]} +{"qna_file": "compositional_skills/writing/freeform/grammar/basic_grammer_tests/qna.yaml", "question_id": "223962569548270324579723899672099682521", "category": "taxonomy", "turns": ["is a flock of fish correct grammar?"], "reference": ["no, it should be a school of fish\n"]} +{"qna_file": "compositional_skills/writing/freeform/grammar/basic_grammer_tests/qna.yaml", "question_id": "139842803995795099101406863854147759678", "category": "taxonomy", "turns": ["what is between the past and the future\n"], "reference": ["the present is between the past and the future\n"]} +{"qna_file": "compositional_skills/writing/freeform/social_media/twitter/qna.yaml", "question_id": "195465403293340207671514809186512541245", "category": "taxonomy", "turns": ["Draft a 'Motivation Monday' story on Twitter, brainstorming a powerful quote or message that inspires your audience."], "reference": ["Embrace the grind and let your passion fuel your purpose! \ud83d\udcaa\ud83d\udd25 This #MotivationMonday, remember that every challenge is an opportunity in disguise. \ud83c\udf1f Let's turn our obstacles into stepping stones and reach new heights. \ud83c\udf31 What's your mantra for the week? \ud83d\udcad\u2728 #MondayMotivation #Inspiration #GrowthMindset"]} +{"qna_file": "compositional_skills/writing/freeform/social_media/facebook/qna.yaml", "question_id": "43505922048544609455505303968733931642", "category": "taxonomy", "turns": ["Hoping to get some discussion going on Facebook? Help me share a post asking your friends about their favorite ways to stay organized and manage their time."], "reference": ["Hey friends! \ud83c\udf1f Let's talk about organization and time management hacks! \ud83d\udcc5\ud83d\udcdd I'm always looking for new ways to stay on top of my game, and I'd love to hear what works for you. Do you have a favorite app, a special planner, or a unique routine that keeps you going? Share your tips and tricks in the comments below! \ud83d\udcac Let's learn from each other and make the most of our time together. \u2764\ufe0f #TimeManagement #ProductivityTips #OrganizationalGoals"]} +{"qna_file": "compositional_skills/writing/freeform/social_media/instagram/qna.yaml", "question_id": "220892787245899703752276484268450564150", "category": "taxonomy", "turns": ["I'm off on a city break with my friends! Can you suggest a witty Instagram caption for my travel photos?"], "reference": ["Seizing the city life with my squad! \ud83c\udfd9\ufe0f\ud83d\udc83 This weekend, we're trading serenity for skyscrapers and tranquility for traffic. Can't wait to create some unforgettable memories! \ud83d\udcf8\u2728 #CityBreakCrew #UrbanExplorers"]} +{"qna_file": "compositional_skills/writing/freeform/social_media/linkedin/qna.yaml", "question_id": "41172079433211962087382159556863690225", "category": "taxonomy", "turns": ["Help me draft a LinkedIn recommendation for a colleague or connection that highlights their strengths and achievements."], "reference": ["I am delighted to recommend [Name] for any professional opportunity that comes their way. As a [their job title], they consistently demonstrated a high level of expertise, dedication, and innovation. Their ability to [mention a specific strength or achievement] was truly impressive and contributed significantly to our team's success. I am confident that [Name] will continue to excel and make a positive impact wherever they go. Don't hesitate to connect with them and see their talents in action! #Recommendation #ProfessionalGrowth #CareerDevelopment"]} +{"qna_file": "compositional_skills/writing/freeform/debate/qna.yaml", "question_id": "245701884431063091725576719273210955683", "category": "taxonomy", "turns": ["Debate the merits and drawbacks of implementing a universal basic income between an economist, a sociologist, and a policy maker."], "reference": ["Economist:\\n\"Implementing a universal basic income (UBI) could significantly reduce poverty rates and provide a financial safety net for individuals who lose their jobs due to automation or economic downturns. UBI could also simplify the welfare system, saving administrative costs. However, there are concerns about the potential for inflation and the impact on work incentives.\"\\n\\nSociologist:\\n\"From a sociological perspective, UBI has the potential to address income inequality and promote social cohesion. It could provide individuals with the freedom to pursue education, entrepreneurship, or caregiving roles without the fear of financial instability. On the other hand, there\\'s a risk that UBI could exacerbate existing social issues, such as gender or racial disparities, if not implemented thoughtfully.\"\\n\\nPolicy Maker:\\n\"As a policy maker, I see the appeal of UBI in its potential to alleviate poverty and simplify the welfare system. However, the cost of implementing UBI on a national scale is substantial, and it\\'s crucial to consider alternative policies that might achieve similar goals at a lower cost. Additionally, determining the appropriate income level for UBI requires careful deliberation, as setting it too high could discourage workforce participation.\"\\n\\nConclusion:\\nAfter examining the arguments presented by the economist, sociologist, and policy maker, it is clear that UBI has both merits and drawbacks. While it could address income inequality, simplify the welfare system, and promote social cohesion, it also raises concerns about inflation, work incentives, and cost-effectiveness. Further research and debate are necessary to determine whether UBI is the best policy solution for addressing income inequality and poverty in our society."]} +{"qna_file": "compositional_skills/writing/freeform/poetry/narrative_poetry/qna.yaml", "question_id": "284243748424050630324637585262090009597", "category": "taxonomy", "turns": ["Write a narrative poem about a solitary tree that has witnessed centuries of change in the surrounding landscape."], "reference": ["In the heart of the valley, where time's river flows,\nStands a solitary tree, in repose.\nA sentinel of seasons, a witness to change,\nIn the dance of the years, it has been entranced.\n\nOnce, it was a sapling, in a forest so grand,\nWhere the world was a canvas, and life was unplanned.\nNow it stands alone, in the clearing it's made,\nA testament to time, in the landscape it's laid.\n\nThrough the centuries, it has seen the sun rise and set,\nIn its silence, it has not forgotten or regretted.\nIt has watched the seasons, in their endless play,\nThe winter's chill, the summer's blaze, the spring's sweet serenade, the autumn's gentle ballet.\n\nIt has seen the rivers shift their course,\nAnd the hills, once distant, draw near with force.\nIt has watched the forests rise and fall,\nAnd the meadows bloom and then withdraw.\n\nIt has felt the tremors of the earth's delight,\nAnd the shuddering cries in the dead of the night.\nIt has seen the stars in their celestial play,\nAnd the moon's soft glow in the quiet of the day.\n\nIt has provided shelter to the creatures of the wood,\nAnd seen them come and go, as they understood.\nIt has been a beacon, a friend, a guide,\nIn the landscape of change, it has stood with pride.\n\nAnd as it stands, in its solitary might,\nIt carries the stories of a thousand sights.\nA silent witness, a timeless sprite,\nIn the heart of the valley, under the soft moonlight."]} +{"qna_file": "compositional_skills/writing/freeform/poetry/limerick/qna.yaml", "question_id": "127372691907841636607844455554283210878", "category": "taxonomy", "turns": ["Write a limerick that playfully describes a flower's bloom and its effect on nearby insects."], "reference": ["A flower so bright in the glade,\nBy insects is never unlaid.\nWith colors so grand,\nIt lures them to land,\nIn a dance of life, unafraid."]} +{"qna_file": "compositional_skills/writing/freeform/poetry/epic_poetry/qna.yaml", "question_id": "150909196828493296119251654810016997611", "category": "taxonomy", "turns": ["Write an epic poem about a mythical creature that inhabits a vast forest, and the heroic quest of a young explorer seeking to make contact with this elusive being."], "reference": ["In the heart of a forest, vast and green,\nLived a mythical creature, unseen.\nA tale of wonder, of a beast so grand,\nRoaming the depths of this enchanted land.\n\nA young explorer, brave and bold,\nHeard the whispers of the tales of old.\nWith a heart full of courage, and a spirit so bright,\nHe ventured forth on a starlit night.\n\nThrough the shadows of the trees, tall and grand,\nGuided by the moon's soft, silver band.\nThe forest stirred, a symphony of sound,\nIn the heart of nature, where mysteries are found.\n\nThe explorer journeyed, through the forest's decree,\nIn search of the creature, as elusive as the sea.\nDays turned to nights, and nights to days,\nIn the epic quest, the explorer pressed on his ways.\n\nThe creature, it was said, had eyes like the night,\nA guardian of the forest, in the soft moonlight.\nWith a heart of compassion, and a spirit so free,\nA friend to all creatures, as legend tells of thee.\n\nAt last, in the heart of the forest so deep,\nUnder the watchful eyes of the moon's soft sleep.\nThe explorer met the creature, in a clearing so wide,\nA being of beauty, in the forest's heart, he stood by its side.\n\nTheir bond was formed, under the stars' gentle glow,\nA friendship that would forever grow.\nIn the epic of nature, a tale to be told,\nOf a heroic quest, and a friendship so bold.\n\nSo, listen well, to the whispers of the trees,\nTo the tales of the forest, carried on the breeze.\nFor in the heart of nature, a story unfolds,\nOf a heroic explorer, and a mythical creature, so bold."]} +{"qna_file": "compositional_skills/writing/freeform/poetry/ballad/qna.yaml", "question_id": "144272161361344526348989602719270968293", "category": "taxonomy", "turns": ["Write a ballad about a majestic mountain range and the secrets it holds within its peaks."], "reference": ["(Verse 1)\nIn the heart of mountains, where secrets abide,\nNature's ballad, a tale of pride.\nMajestic peaks pierce the sky, so high,\nGuardians of stories, where eagles fly.\n\n(Chorus)\nOh, the symphony of mountains, a ballad so grand,\nWhispers of ancient times in every rock and sand.\nSecrets hidden in shadows, in valleys untold,\nNature's ballad, a mystery to behold.\n\n(Verse 2)\nGlaciers carving, a sculptor's hand,\nShaping the mountains, as time expands.\nCascading waterfalls, a crystal clear decree,\nEchoing tales of the mountains, for all to see.\n\n(Chorus)\nOh, the symphony of mountains, a ballad so grand,\nWhispers of ancient times in every rock and sand.\nSecrets hidden in shadows, in valleys untold,\nNature's ballad, a mystery to behold.\n\n(Bridge)\nBeneath the moon's silver and shimmering veil,\nMountains stand tall, in the night's quiet braille.\nStars twinkle above, like secrets in flight,\nNature's ballad, a beacon of light.\n\n(Verse 3)\nIn the silence of summits, where winds speak in codes,\nLegends of old, in the mountain's abodes.\nEchoes of history, in every stone's crevice,\nNature's ballad, a timeless device.\n\n(Chorus)\nOh, the symphony of mountains, a ballad so grand,\nWhispers of ancient times in every rock and sand.\nSecrets hidden in shadows, in valleys untold,\nNature's ballad, a mystery to behold.\n\n(Outro)\nAs seasons change, and winds whisper and sigh,\nThe mountain's ballad, forever to lie.\nIn every snowflake, in every mountain's crest,\nNature's ballad, a timeless guest."]} +{"qna_file": "compositional_skills/writing/freeform/poetry/haiku/qna.yaml", "question_id": "161063294038028699417396921985007546401", "category": "taxonomy", "turns": ["Write a haiku that captures the beauty of a single autumn leaf."], "reference": ["Crimson leaf adrift,\nDancing on a cool fall breeze,\nNature's art takes flight."]} +{"qna_file": "compositional_skills/writing/freeform/poetry/ode/qna.yaml", "question_id": "306326295106575985375913774614894921142", "category": "taxonomy", "turns": ["Craft an ode to the power of the lightning, and how it can help you to tap into the beauty of creativity and inspiration."], "reference": ["Ode to the Lightning's Power and Inspiration\n\nOh, Lightning! Luminous muse of the night,\nYour dazzling flash, a beacon of insight.\nIn the depths of the soul, you ignite,\nA spark of inspiration, pure and bright.\n\nA force unseen, yet so wild and free,\nYou dance upon the canvas of the sky.\nA symphony of thunder, a lyrical cry,\nA testament to the power that lies.\n\nIn the silence of the night, you roar,\nAwakening the muses that within us bore.\nA lightning bolt, a stroke of art,\nA masterpiece, a poet's heart.\n\nThe stormy tempest, a creative force,\nA whirlwind of thoughts, a lyrical discourse.\nIn the chaos, a melody is born,\nA harmony of ideas, a new dawn.\n\nThe lightning's glow, a radiant hue,\nIlluminates the mind, a fresh view.\nA spark of genius, a flash of thought,\nA creative surge, a soul has sought.\n\nThe lightning's path, a winding trace,\nA labyrinth of ideas, a mental space.\nA journey of discovery, a quest divine,\nA tapestry of wisdom, a creative design.\n\nThe lightning's power, a force to harness,\nA wellspring of creativity, a treasure to amass.\nIn the depths of introspection, a truth unfolds,\nA testament to the stories that within us holds.\n\nOh, Lightning! A muse of the night,\nYour dazzling flash, a poet's delight.\nIn the silence of the soul, you ignite,\nA spark of inspiration, pure and bright."]} +{"qna_file": "compositional_skills/writing/freeform/poetry/sonnet/qna.yaml", "question_id": "277948802439390926408959426272671843003", "category": "taxonomy", "turns": ["Compose a sonnet that portrays the vibrant colors of a fall foliage."], "reference": ["In autumn's grand and regal tapestry,\nWhere vibrant hues in splendor are unfurled.\nAmber and scarlet leaves, a symphony,\nIn nature's palette, they twirl and twirl.\n\nBeneath the cerulean sky they sway,\nA festival of color, a visual feast.\nWhere crimson leaves like embers lay,\nAnd gold illuminates the woodland's least.\n\nThe rustling whispers of the trees' goodbye,\nA farewell ode to summer's gentle reign.\nIn every leaf, a poignant goodbye,\nA vibrant echo of the year's sweet pain.\n\nOh, autumn's sonnet, in each leaf's last dance,\nA vibrant farewell, in nature's tranquil trance."]} +{"qna_file": "compositional_skills/writing/freeform/poetry/freeverse/qna.yaml", "question_id": "58730918575107820369620429478222335541", "category": "taxonomy", "turns": ["Write a free verse poem that explores the changing colors of autumn leaves."], "reference": ["In the waning days of summer's reign,\nA subtle shift begins to stir.\nNature dons her cloak of flame,\nAs autumn leaves awaken to their metamorphosis.\n\nCrimson and gold, hues of amber,\nIn every corner, a vibrant display.\nLeaves whisper tales of time,\nOf seasons turning, in their gentle way.\n\nBeneath the azure sky, they dance,\nA ballet of light and shadow.\nEach leaf, a fleeting sonnet,\nPenned by the hand of autumn's glow.\n\nThey rustle in the cool, crisp air,\nA symphony of colors bold.\nIn their fiery beauty, a secret lies,\nA testament to life's enduring hold.\n\nAs the days grow short and cold,\nThe leaves surrender to the breeze.\nIn their descent, a final verse,\nA poignant ode to autumn's ease.\n\nIn the aftermath, a quiet stillness,\nNature's canvas now bereft of hue.\nYet, in the heart of winter's slumber,\nThe promise of springtime breaks anew."]} +{"qna_file": "compositional_skills/writing/freeform/brainstorming/refute_claim/qna.yaml", "question_id": "281878611118880260171669439529082158607", "category": "taxonomy", "turns": ["Identify potential challenges and refutations against the claim that the use of genetically modified mosquitoes is not harmful to the environment."], "reference": ["Sure, I'd be happy to help you explore this topic. The use of genetically modified (GM) mosquitoes is a complex issue with potential benefits and challenges. Here are some points that are often brought up in discussions about the environmental impact of GM mosquitoes:\n\n1. **Potential Challenge:** Unintended consequences on the ecosystem. Mosquitoes are part of food chains, and changing their populations could have unforeseen effects on other species.\n **Refutation:** While it's true that any significant change in an ecosystem can have ripple effects, the number of mosquitoes that would need to be altered to make a difference in disease transmission is relatively small compared to the total mosquito population. Moreover, many GM mosquitoes are designed to be male, which don't bite or transmit diseases, lessening the potential impact on food chains.\n\n2. **Potential Challenge:** Risk of the modified genes spreading to wild populations.\n **Refutation:** The GM mosquitoes are often designed to have a short lifespan, and their offspring with wild mosquitoes may also inherit this trait, reducing the likelihood of the modified genes persisting in the environment. However, this is a point of ongoing research and concern.\n\n3. **Potential Challenge:** The potential for creating more robust disease-carrying mosquitoes through genetic resistance.\n **Refutation:** This is a valid concern, and it's one that scientists take into account when designing GM mosquitoes. They often use techniques that make the mosquitoes less able to transmit diseases, rather than killing them outright, to reduce the selection pressure for resistance.\n\n4. **Potential Challenge:** Public perception and trust. Many people are wary of genetically modified organisms, and releasing GM mosquitoes could exacerbate these fears.\n **Refutation:** Public engagement and education are crucial in addressing these concerns. It's important to communicate the potential benefits and risks clearly and transparently, and to involve local communities in decision-making processes.\n\nRemember, these are complex issues and there's ongoing debate among scientists and policymakers about the best ways to proceed. It's important to consider a wide range of perspectives when evaluating the potential impacts of GM mosquitoes.\n"]} +{"qna_file": "compositional_skills/writing/freeform/brainstorming/support_claim/qna.yaml", "question_id": "172358481506970422842793801709745445987", "category": "taxonomy", "turns": ["Produce three convincing arguments that support the claim that universal healthcare is a necessary component of a just society."], "reference": ["1. Universal healthcare ensures equitable access to medical services: A just society is one that strives to provide equal opportunities and resources to all its members, regardless of their background or economic status. By implementing universal healthcare, a society can guarantee that every individual, irrespective of their financial situation, has access to essential medical services, treatments, and preventive care. This promotes a more equitable distribution of healthcare resources and reduces the disparities in health outcomes between different socio-economic groups.\n\n2. Universal healthcare improves overall public health: By providing comprehensive healthcare coverage to the entire population, universal healthcare systems can more effectively monitor, manage, and prevent the spread of diseases and health conditions. This leads to improved overall public health, as early detection and intervention become more accessible to everyone. A healthier population not only enhances the quality of life for individuals but also contributes to increased productivity and economic growth, which are essential components of a just society.\n\n3. Universal healthcare alleviates financial burdens and promotes social stability: Medical expenses are among the leading causes of bankruptcy and financial hardship for individuals and families worldwide. By implementing universal healthcare, a society can alleviate these financial burdens and ensure that no one is forced into poverty due to medical debt. Furthermore, universal healthcare can help reduce social tensions and unrest by promoting a sense of solidarity and shared responsibility among citizens. A just society should ensure that its members are not left to face the potentially catastrophic consequences of medical emergencies and illnesses alone, and universal healthcare is a crucial step towards achieving this goal.\n"]} +{"qna_file": "compositional_skills/writing/freeform/brainstorming/idea_generation/qna.yaml", "question_id": "318713840430836706152692478009038904696", "category": "taxonomy", "turns": ["Generate 5 creative ideas for a marketing campaign promoting a new line of eco-friendly or sustainable products for the home."], "reference": ["1. \"Eco-Hero Challenge\": Create an interactive campaign that encourages customers to become \"Eco-Heroes\" by switching to your sustainable home products. Provide a checklist of eco-friendly actions to take, such as replacing single-use plastics with reusable items, and offer rewards for completing each task, like discounts on your new product line.\n\n2. \"Sustainable Home Makeover\": Partner with influencers, bloggers, or local eco-conscious celebrities to showcase a head-to-toe eco-friendly home makeover using your new product line. Share the transformation on social media platforms, and create a series of blog posts or videos detailing the process, benefits, and cost comparisons of eco-friendly products versus traditional ones.\n\n3. \"Plant a Tree with Every Purchase\": Collaborate with a reputable tree-planting organization to plant a tree for every purchase made from your new eco-friendly product line. Promote this initiative through your marketing channels, emphasizing the positive environmental impact of choosing sustainable products.\n\n4. \"Eco-Friendly Swap Party\": Organize a virtual or in-person event where customers can bring one traditional home product to swap for a sustainable alternative from your new line. Offer eco-friendly tips, DIY projects, and refreshments, and encourage attendees to share their experiences on social media using a branded hashtag.\n\n5. \"Eco-Innovation Showcase\": Host a virtual or in-person event to highlight the innovative features of your new eco-friendly product line. Invite industry experts, environmental organizations, and customers to discuss the importance of sustainability and the benefits of your products. Offer interactive workshops, Q&A sessions, and special promotions for attendees.\n"]} +{"qna_file": "compositional_skills/writing/freeform/prose/screenplay/qna.yaml", "question_id": "146680146967538821465515295617575936932", "category": "taxonomy", "turns": ["Write a screenplay about a heist gone wrong."], "reference": ["**Title: \"The Final Heist\"**\n\n**INT. HIGH-TECH VAULT - NIGHT**\n\n*A group of seasoned thieves, LEADER (40s, charismatic mastermind), HACKER (30s, introverted genius), MUSCLE (20s, athletic enforcer), and WHEELMAN (50s, grizzled getaway driver), are in the midst of a daring heist.*\n\n**LEADER**\n(whispering)\nJust like the drill said. Hacker, you're up.\n\n**INT. HACKER'S VAN - NIGHT**\n\n*Hacker works his magic on the state-of-the-art vault security system.*\n\n**HACKER**\n(murmuring)\nAlmost there...\n\n**INT. HIGH-TECH VAULT - NIGHT**\n\n*The vault door clicks open.*\n\n**MUSCLE**\n(grinning)\nWe're in.\n\n**INT. HACKER'S VAN - NIGHT**\n\n*Suddenly, the van's alarm blares. Hacker looks shocked.*\n\n**HACKER**\n(into earpiece)\nLeader, we have a problem.\n\n**EXT. BANK - NIGHT**\n\n*Police cars surround the building. Wheelman waits in the getaway car.*\n\n**WHEELMAN**\n(into earpiece)\nWe're compromised. Abort!\n\n**INT. HIGH-TECH VAULT - NIGHT**\n\n*The team frantically gathers the loot.*\n\n**LEADER**\n(yelling)\nLeave it! We'll come back for it!\n\n**EXT. BANK - NIGHT**\n\n*The team runs out, guns blazing. Wheelman speeds off.*\n\n**INT. POLICE CAR - NIGHT**\n\n*A detective, DETECTIVE JONES (50s, determined), watches the scene unfold.*\n\n**DETECTIVE JONES**\n(into radio)\nI want that team found. No one pulls a heist on my watch.\n\n**EXT. CITY STREETS - NIGHT**\n\n*The getaway car weaves through traffic, chased by police cars.*\n\n**LEADER**\n(into earpiece)\nWe'll regroup. This isn't over.\n\n*The car disappears into the night, leaving the blaring sirens behind.*\n"]} +{"qna_file": "compositional_skills/writing/freeform/prose/emails/formal/qna.yaml", "question_id": "126136368836774663798325552641783627160", "category": "taxonomy", "turns": ["Could you help me write a formal email to inquire about the progress of my internship application?"], "reference": ["Subject: Inquiry Regarding the Status of My Internship Application\n\nDear [Recipient's Name],\n\nI hope this email finds you well. I am writing to inquire about the current status of my internship application with [Company Name]. I submitted my application on [date of application] for the [Internship Title] position.\n\nI am very interested in the opportunity to learn and grow as an intern at [Company Name], and I am eager to contribute my skills and enthusiasm to your team. I understand that the internship selection process may take some time, and I appreciate your consideration of my application.\n\nIf there are any additional steps I need to take or further information you require from me, please let me know. I am more than happy to provide any necessary documentation or complete additional tasks to facilitate the decision-making process.\n\nI am excited about the prospect of joining [Company Name] and contributing to the [specific project, team, or aspect of the company] based on my background in [mention relevant skills or experiences]. I am confident that this internship will provide me with valuable experience and growth opportunities.\n\nThank you for your time and consideration. I look forward to hearing from you regarding the next steps in the internship application process.\n\nSincerely,\n\n[Your Full Name]\n[Your Contact Information]"]} +{"qna_file": "compositional_skills/writing/freeform/prose/emails/informal/qna.yaml", "question_id": "198612688123297703030991651853305427522", "category": "taxonomy", "turns": ["Write an informal email to a friend asking for advice on planning a surprise party."], "reference": ["Subject: Help Me Plan a Surprise Party \ud83c\udf8a\ud83e\udd2b\n\nHey [Friend's Name],\n\nI hope you're having a fantastic day! I've got this brilliant idea and I could really use your expertise. I'm planning a surprise party for [Surprisee's Name] and I want it to be unforgettable! \ud83d\udca5\n\nSince you've always been the mastermind behind amazing parties, I'm reaching out for your advice. I'm thinking of having it at [Location], but I'm open to other suggestions. I'm also considering a theme, like [Theme Idea 1] or [Theme Idea 2]. What do you think?\n\nHere's what I've got so far:\n\n- Date: [Tentative Date]\n- Location: [Tentative Location]\n- Theme: [Tentative Theme]\n- Guest List: [List of People to Invite]\n\nI'd love to hear your thoughts on these ideas and any other suggestions you might have. I want this party to be a total surprise and an unforgettable experience for [Surprisee's Name].\n\nLooking forward to your wisdom and guidance! \ud83d\ude04\n\nCheers,\n[Your Name]\n\nP.S. I'll keep this hush-hush, so mum's the word! \ud83e\udd10"]} +{"qna_file": "compositional_skills/writing/freeform/prose/articles/qna.yaml", "question_id": "255423854588608162065422787402463662509", "category": "taxonomy", "turns": ["Seeking insights for an article on the benefits of restorative justice practices in schools and communities."], "reference": ["Restorative justice practices have been gaining traction in schools and communities due to their transformative approach to conflict resolution and behavior management. Here are some key benefits and aspects of restorative justice that you can include in your article:\n\n1. **Promoting Healing and Understanding**\n - Restorative justice focuses on repairing harm and fostering understanding between the offender, the victim, and the community. This approach encourages empathy, forgiveness, and personal growth.\n\n2. **Reducing Suspensions and Expulsions**\n - By addressing the root causes of misbehavior, restorative practices have been shown to significantly reduce suspension and expulsion rates. This leads to improved school climate and increased academic achievement.\n\n3. **Building Healthy Relationships**\n - Restorative justice emphasizes communication, active listening, and collaborative problem-solving. These skills help students develop healthy relationships with their peers and educators, fostering a more inclusive and supportive learning environment.\n\n4. **Empowering Students and Communities**\n - Restorative practices involve all stakeholders in the resolution process, giving students a voice and a sense of ownership over their community. This empowerment leads to increased engagement and investment in the school environment.\n\n5. **Preventing Recidivism**\n - By addressing the underlying issues that lead to misbehavior, restorative justice reduces the likelihood of repeat offenses. This approach not only benefits the individual but also contributes to a safer and more harmonious community.\n\n6. **Culturally Responsive and Inclusive**\n - Restorative justice practices can be adapted to various cultural contexts, making them an effective tool for addressing the unique needs and experiences of diverse communities.\n\n7. **Cost-Effective and Scalable**\n - Restorative justice programs have been shown to be cost-effective, as they reduce the need for disciplinary measures and court involvement. Additionally, these practices can be scaled up to accommodate larger communities and institutions.\n\nWhen writing your article, consider including case studies, interviews with educators and practitioners, and research findings to illustrate the benefits of restorative justice. Discussing the challenges and limitations of implementing these practices can also provide valuable insights for your readers."]} +{"qna_file": "compositional_skills/writing/freeform/prose/stories/qna.yaml", "question_id": "215792936889518543687300250770120135376", "category": "taxonomy", "turns": ["Create a tale about a character who discovers a realm of their own imagination, where they must face their inner critic and learn to accept themselves."], "reference": ["In the quiet corners of Emily's creative mind lay a realm of her own making, a world she had filled with vibrant colors, enchanting characters, and extraordinary landscapes. This was her sanctuary, a place where she could escape the mundane and immerse herself in the wonders of her imagination.\n\nOne day, Emily discovered a new facet of her realm, a dark and brooding forest that seemed to embody her self-doubt and insecurities. At the heart of this forest stood a menacing figure, her Inner Critic, a towering, shadowy being that whispered harsh words of condemnation and doubt.\n\nFaced with this manifestation of her own self-criticism, Emily hesitated. She knew she had to confront her Inner Critic, to face the fears and doubts that had long held her back. With a deep breath, she stepped forward, determined to reclaim her sanctuary and learn to accept herself.\n\nThe Inner Critic, sensing her determination, unleashed a torrent of cutting remarks and disparaging comments. It mocked her dreams, her ambitions, and her very essence. But Emily, drawing on her newfound courage, stood her ground.\n\nShe reminded herself of her accomplishments, the stories she had brought to life, and the joy she had shared with others through her writing. Slowly, she began to see the Inner Critic for what it was\u2014a part of herself that had been given too much power, a voice that had grown too loud in the silence of her mind.\n\nEmily realized that she didn't have to let her Inner Critic control her. She could choose to silence its voice, to replace its harsh words with kindness and encouragement. And so, she did.\n\nAs Emily reclaimed her realm, she found that the darkness of the forest began to fade. The trees became brighter, the air clearer, and the whispers of her Inner Critic grew softer, replaced by the gentle voice of her own inner wisdom.\n\nFrom then on, Emily vowed to nurture her sanctuary, to fill it with love, acceptance, and the boundless wonders of her imagination. She learned to embrace her flaws, to celebrate her strengths, and to never let her Inner Critic overshadow the beauty of her own mind.\n\nAnd so, Emily continued to explore her realm, her Inner Critic now a mere whisper in the shadows, a reminder of the strength and resilience she had discovered within herself. She had faced her fears, embraced her true self, and found the courage to create a world where anything was possible."]} +{"qna_file": "compositional_skills/writing/freeform/legal/contracts/qna.yaml", "question_id": "323942760959542175812866568947575109876", "category": "taxonomy", "turns": ["I require help drafting a business contract for a service I will provide. Can you outline the terms, payment schedule, and liability clauses?"], "reference": ["# Business Contract\n\nThis Business Contract (\"Contract\") is entered into on [Date], by and between [Your Full Name or Company Name] (\"Service Provider\") and [Client's Full Name or Company Name] (\"Client\").\n\n## 1. Terms\n\nThe Service Provider agrees to provide the following services to the Client:\n\n1.1 **Description of Services:**\n - [Detailed description of the services to be provided]\n\n1.2 **Timeline:**\n - The services will commence on [Start Date] and are expected to be completed by [End Date], unless otherwise agreed upon by both parties.\n\n## 2. Payment Schedule\n\n2.1 **Compensation:**\n - The Client agrees to compensate the Service Provider in accordance with the payment schedule outlined in Exhibit A attached hereto.\n\n2.2 **Payment Schedule:**\n - [Specify the payment schedule, e.g., hourly, weekly, monthly, or milestone-based payments]\n\n2.3 **Late Payments:**\n - Late payments may incur a [Specify Percentage]% late fee after [Number of Days] days from the due date.\n\n## 3. Liability Clauses\n\n3.1 **Limitation of Liability:**\n - The Service Provider's liability for any claims arising out of or related to this Contract shall be limited to the amount of fees paid by the Client under this Contract.\n\n3.2 **Indemnification:**\n - The Client agrees to indemnify and hold harmless the Service Provider, its affiliates, officers, agents, and employees from any and all claims, liabilities, damages, or expenses, including reasonable attorneys' fees, arising out of or related to the Client's breach of this Contract.\n\n3.3 **Force Majeure:**\n - Neither party shall be liable for any failure or delay in performing its obligations under this Contract to the extent that such failure or delay is caused by events beyond its reasonable control, including but not limited to acts of God, war, terrorism, civil unrest, or government orders.\n\n## 4. Governing Law\n\nThis Contract shall be governed by and construed in accordance with the laws of [Your State/Country].\n\nIN WITNESS WHEREOF, the parties hereto have executed this Business Contract as of the date first above written.\n\nService Provider: ___________________________ [Your Full Name or Company Name]\n\nClient: ___________________________ [Client's Full Name or Company Name]\n\nDate: ___________________________\n"]} +{"qna_file": "compositional_skills/writing/freeform/legal/agreement/qna.yaml", "question_id": "84361221415417329642101034446205998311", "category": "taxonomy", "turns": ["I need to create a licensing agreement for my intellectual property. Can you help me draft the terms and conditions, including royalties and exclusivity?"], "reference": ["# Licensing Agreement\n\nThis Licensing Agreement (\"Agreement\") is entered into on [Date], by and between [Your Full Name or Company Name] (\"Licensor\") and [Licensee's Full Name or Company Name] (\"Licensee\").\n\n## 1. Licensed Property\n\nThe Licensor grants the Licensee a non-exclusive, non-transferable, and limited license to use the intellectual property described in Exhibit A attached hereto (\"Licensed Property\") in accordance with the terms and conditions set forth in this Agreement.\n\n## 2. Territory\n\nThe license granted herein is limited to the following territory: [Specify Territory].\n\n## 3. Term\n\nThis Agreement shall commence on [Start Date] and shall continue in force for a term of [Number of Years] years, unless otherwise terminated in accordance with the provisions herein.\n\n## 4. Royalties\n\n4.1 **Royalty Rate:**\n - The Licensee shall pay the Licensor a royalty equal to [Specify Percentage]% of the Net Sales of the Licensed Property.\n\n4.2 **Net Sales:**\n - \"Net Sales\" shall mean the gross sales price of the Licensed Property, less any applicable taxes, shipping charges, and returns.\n\n4.3 **Payment Terms:**\n - Royalties shall be payable quarterly within [Number of Days] days after the end of each calendar quarter.\n\n## 5. Exclusivity\n\n5.1 **Exclusive License:**\n - This license is non-exclusive, and the Licensor reserves the right to grant licenses to other parties for the same or similar use of the Licensed Property.\n\n5.2 **Exclusive Territory:**\n - This license does not grant the Licensee any exclusive rights to the Licensed Property in any territory.\n\n## 6. Termination\n\n6.1 **Termination for Cause:**\n - Either party may terminate this Agreement immediately in the event of a material breach by the other party.\n\n6.2 **Termination for Convenience:**\n - The Licensor may terminate this Agreement at any time upon [Number of Days] days' written notice to the Licensee.\n\n6.3 **Effects of Termination:**\n - Upon termination, the Licensee shall cease all use of the Licensed Property and return or destroy all copies of the Licensed Property in its possession.\n\n## 7. Confidentiality\n\n7.1 **Confidential Information:**\n - Both parties agree to keep confidential any proprietary or sensitive information obtained during the term of this Agreement.\n\n7.2 **Exceptions:**\n - Confidentiality obligations shall not apply to information that is publicly available or rightfully obtained from a third party.\n\n## 8. Governing Law\n\nThis Agreement shall be governed by and construed in accordance with the laws of [Your State/Country].\n\nIN WITNESS WHEREOF, the parties hereto have executed this Licensing Agreement as of the date first above written.\n\nLicensor: ___________________________ [Your Full Name or Company Name]\n\nLicensee: ___________________________ [Licensee's Full Name or Company Name]\n\nDate: ___________________________\n"]} +{"qna_file": "compositional_skills/writing/freeform/jokes/puns/general/qna.yaml", "question_id": "200526313721129807682199189054363275270", "category": "taxonomy", "turns": ["Tell me a pun about birds."], "reference": ["Why do birds eat wood?\nBecause they're peckish!\n"]} +{"qna_file": "compositional_skills/writing/freeform/jokes/puns/general/qna.yaml", "question_id": "102616323836279432580264431649654588396", "category": "taxonomy", "turns": ["Tell me a pun about fast food."], "reference": ["Why do French people always eat snails?\nThey don't like fast food.\n"]} +{"qna_file": "compositional_skills/writing/freeform/jokes/puns/general/qna.yaml", "question_id": "51476049719651054677644816077051145463", "category": "taxonomy", "turns": ["Tell me a pun about gas."], "reference": ["Why did the car have a belly ache?\nBecause it had too much gas!\n"]} +{"qna_file": "compositional_skills/writing/freeform/jokes/puns/general/qna.yaml", "question_id": "86705766384218326500115804638629320253", "category": "taxonomy", "turns": ["Tell me a pun about waves."], "reference": ["What did the ocean say to the ocean?\nNothing. It just waved!\n"]} +{"qna_file": "compositional_skills/writing/freeform/jokes/puns/general/qna.yaml", "question_id": "68510917643102630357217289282587278502", "category": "taxonomy", "turns": ["Tell me a pun about roofs."], "reference": ["Do you want to hear a roof joke?\nNevermind. The first one's on the house!\n"]} +{"qna_file": "compositional_skills/writing/freeform/jokes/puns/general/qna.yaml", "question_id": "280992183721081713956024768027126468241", "category": "taxonomy", "turns": ["Tell me a pun about x-rays."], "reference": ["What do dentists call their x-rays?\nTooth pics!\n"]} +{"qna_file": "compositional_skills/writing/freeform/jokes/puns/general/qna.yaml", "question_id": "247557730382321333547486156073854343709", "category": "taxonomy", "turns": ["Tell me a pun about Neverland."], "reference": ["Why does Peter Pan always fly?\nBecause he never lands.\n"]} +{"qna_file": "compositional_skills/writing/freeform/jokes/puns/general/qna.yaml", "question_id": "303719001697808358188272633053117447207", "category": "taxonomy", "turns": ["Tell me a pun about Bicycles."], "reference": ["Why do bicycles have a hard time standing?\nBecause they are two tired.\n"]} +{"qna_file": "compositional_skills/writing/freeform/technical/user_manual/qna.yaml", "question_id": "178440809004868181315275941123120327526", "category": "taxonomy", "turns": ["Write a manual on how to perform a security audit for a small to medium-sized business."], "reference": ["# Manual: Performing a Security Audit for Small to Medium-Sized Businesses\n\n## Executive Summary\n\nThis manual outlines the process of conducting a comprehensive security audit for small to medium-sized businesses. A security audit is a systematic evaluation of an organization's information systems by measuring how well they conform to a set of established criteria. A systematic approach helps identify vulnerabilities, assess risks, and determine the effectiveness of current security measures.\n\n## 1. Introduction\n\n### 1.1 Background\n\nIn today's interconnected world, businesses of all sizes are at risk of cyber threats. Performing regular security audits is crucial to maintaining a secure environment and protecting sensitive data.\n\n### 1.2 Objectives\n\nThe primary objectives of this manual are:\n- To provide a step-by-step guide for conducting a security audit.\n- To identify common security vulnerabilities in small to medium-sized businesses.\n- To offer recommendations for improving security measures.\n\n## 2. Preparation\n\n### 2.1 Define Scope\n\nDetermine the systems, applications, and networks to be audited. This may include:\n- Network infrastructure\n- Servers and workstations\n- Databases\n- Cloud services\n- Websites and web applications\n- Email systems\n\n### 2.2 Gather Information\n\nCollect relevant documentation, such as network diagrams, system configurations, and security policies.\n\n### 2.3 Assemble Audit Team\n\nForm a team of individuals with expertise in various aspects of information security.\n\n## 3. Audit Process\n\n### 3.1 Step 1: Asset Identification\n\nCreate an inventory of all hardware, software, and data assets within the organization.\n\n### 3.2 Step 2: Vulnerability Scanning\n\nPerform automated vulnerability scans using tools such as Nessus or OpenVAS to identify potential weaknesses in the system.\n\n### 3.3 Step 3: Threat Assessment\n\nAnalyze the likelihood and potential impact of identified vulnerabilities. Consider factors such as:\n- Threat sources (e.g., hackers, malware, insider threats)\n- Threat vectors (e.g., network, email, social engineering)\n- Existing security controls\n\n### 3.4 Step 4: Risk Analysis\n\nDetermine the overall risk level for each vulnerability by evaluating the likelihood of exploitation and the potential impact on the organization.\n\n### 3.5 Step 5: Control Evaluation\n\nAssess the effectiveness of current security controls in mitigating identified risks.\n\n### 3.6 Step 6: Recommendations\n\nProvide actionable recommendations to address identified vulnerabilities and reduce overall risk.\n\n## 4. Reporting\n\n### 4.1 Document Findings\n\nCompile a detailed report of the audit findings, including:\n- Executive summary\n- Audit scope and methodology\n- List of identified assets\n- Summary of vulnerabilities and threats\n- Risk analysis results\n- Recommendations for improvement\n\n### 4.2 Present Findings\n\nPresent the audit findings and recommendations to key stakeholders in a clear and concise manner.\n\n## 5. Follow-Up\n\n### 5.1 Implement Recommendations\n\nWork with the organization's IT department or external vendors to implement recommended security improvements.\n\n### 5.2 Schedule Regular Audits\n\nEstablish a schedule for performing regular security audits to ensure ongoing security and compliance.\n\n## 6. Conclusion\n\nPerforming a security audit is a critical step in protecting a small to medium-sized business from cyber threats. By following the steps outlined in this manual, organizations can identify vulnerabilities, assess risks, and implement effective security measures."]} +{"qna_file": "compositional_skills/writing/freeform/technical/guide/qna.yaml", "question_id": "175251017855213370798256740287404228800", "category": "taxonomy", "turns": ["Create a technical guide on implementing a containerization strategy using Docker and Kubernetes."], "reference": ["# Technical Guide: Implementing a Containerization Strategy with Docker and Kubernetes\n\n## Executive Summary\n\nThis guide outlines the process of implementing a containerization strategy using Docker and Kubernetes. By following the steps in this document, you will be able to containerize applications, manage containers efficiently, and orchestrate container deployments for scalability and high availability.\n\n## 1. Introduction\n\n### 1.1 Background\n\nContainerization is a modern approach to application packaging and deployment, offering portability, resource isolation, and consistent execution across various environments. Docker and Kubernetes are leading tools in containerization and container orchestration, respectively.\n\n### 1.2 Objectives\n\nThe primary objectives of this guide are:\n- Containerize applications using Docker.\n- Manage container lifecycles and resources.\n- Orchestrate container deployments with Kubernetes.\n\n## 2. Docker Installation and Setup\n\n### 2.1 Install Docker\n\nFollow the official Docker documentation to install Docker on your preferred operating system: \n\n### 2.2 Create a Dockerfile\n\nA Dockerfile is a text document that contains all the commands needed to build a Docker image. Create a Dockerfile for your application, specifying the base image, dependencies, and runtime environment.\n\n### 2.3 Build and Run a Docker Container\n\nUse the `docker build` command to create a Docker image from the Dockerfile, and then run the container using the `docker run` command.\n\n## 3. Kubernetes Installation and Setup\n\n### 3.1 Install Kubernetes\n\nFollow the official Kubernetes documentation to install Kubernetes on your preferred platform: \n\n### 3.2 Create a Kubernetes Deployment\n\nA Kubernetes Deployment manages a set of replica Pods, ensuring the desired number of instances is always available. Create a Deployment configuration file in YAML format, specifying the Docker image, resource requirements, and other settings.\n\n### 3.3 Expose the Application with a Service\n\nA Kubernetes Service provides a stable IP address and DNS name for a set of Pods, allowing communication between them. Expose your application using a Service configuration file in YAML format.\n\n## 4. Implementing a Containerization Strategy\n\n### 4.1 Containerize Applications\n\nContainerize your applications using Docker, following the steps outlined in Sections 2.2 and 2.3.\n\n### 4.2 Manage Containers with Docker Compose\n\nDocker Compose is a tool for defining and running multi-container Docker applications. Use Docker Compose to manage container lifecycles, resources, and network configurations.\n\n### 4.3 Orchestrate Containers with Kubernetes\n\nUse Kubernetes to orchestrate your container deployments, following the steps outlined in Sections 3.2 and 3.3. Implement Kubernetes features such as rolling updates, self-healing, and auto-scaling to ensure high availability and scalability.\n\n## 5. Performance Optimization\n\n### 5.1 Resource Allocation\n\nConfigure resource requests and limits for your containers to optimize resource utilization and prevent resource contention.\n\n### 5.2 Caching and Logging\n\nImplement caching strategies and optimize logging for improved performance and reduced storage requirements.\n\n## 6. Security Best Practices\n\n### 6.1 Multi-stage Builds\n\nUse multi-stage builds in your Dockerfiles to minimize the attack surface and reduce image size.\n\n### 6.2 Network Policies\n\nImplement Kubernetes Network Policies to restrict communication between Pods and enhance security.\n\n## 7. Monitoring and Logging\n\n### 7.1 Container Monitoring\n\nUse monitoring tools such as Prometheus and Grafana to monitor container performance and resource utilization.\n\n### 7.2 Centralized Logging\n\nImplement centralized logging using tools like Elasticsearch, Logstash, and Kibana (ELK) to aggregate, analyze, and visualize container logs.\n\n## 8. Conclusion\n\nBy following the steps outlined in this guide, you will have successfully implemented a containerization strategy using Docker and Kubernetes. This approach will enable you to package applications and dependencies into portable, lightweight containers, manage container lifecycles efficiently, and orchestrate deployments for scalability and high availability."]} +{"qna_file": "compositional_skills/writing/freeform/technical/product_description/qna.yaml", "question_id": "118418458031126320264153299374340421589", "category": "taxonomy", "turns": ["I'm selling hand-painted wall art. Can you help me write descriptions that showcase their unique designs, durability, and versatility?"], "reference": ["**Discover the Magic of [Your Brand] Hand-Painted Wall Art - One-of-a-Kind, Durable, and Versatile Masterpieces for Your Space!**\n\nTransform your living spaces with our exquisite collection of [Your Brand] hand-painted wall art. Each piece is a unique, vibrant, and durable reflection of the artist's passion, skillfully crafted to elevate your home's ambiance and charm.\n\n**Key Features:**\n\n1. **Unique Designs:** Each hand-painted wall art piece is a one-of-a-kind creation, boasting intricate patterns, vivid colors, and captivating textures that add visual interest to your walls.\n\n2. **Superior Durability:** Our high-quality materials and expert craftsmanship ensure that your wall art remains vibrant and long-lasting, resisting fading and wear for years to come.\n\n3. **Versatile Styles:** From modern and abstract to traditional and whimsical, our diverse collection caters to a wide range of tastes and preferences, allowing you to find the perfect match for your decor.\n\n4. **Easy Installation:** With our user-friendly hanging system, you can effortlessly mount your wall art, making it simple to rearrange and update your space as desired.\n\n5. **Thoughtful Packaging:** Each piece is carefully packaged to ensure safe delivery, complete with detailed instructions for easy installation and care.\n\n**Benefits:**\n\n- **Express Your Personality:** Hand-painted wall art offers a unique opportunity to showcase your individuality and style, making a lasting impression on visitors.\n\n- **Enhance Your Decor:** Add a touch of elegance, sophistication, or playfulness to your living spaces with our versatile and vibrant wall art pieces.\n\n- **Invest in Lasting Beauty:** Our commitment to quality and durability ensures that your investment in hand-painted wall art will remain a stunning centerpiece for years to come.\n\n- **Effortless Style Updates:** With our easy-to-install system, you can quickly and affordably update your space, creating a fresh and inspiring atmosphere.\n\nBring your walls to life with [Your Brand] hand-painted wall art \u2014 the perfect blend of creativity, quality, and versatility. Explore our collection today and find the perfect masterpiece to complement your unique style.\n"]} +{"qna_file": "compositional_skills/writing/freeform/technical/specification/qna.yaml", "question_id": "237815502902497003149038441732611428892", "category": "taxonomy", "turns": ["Develop a technical specification for a new hardware or software component, including requirements, design, and testing."], "reference": ["# Technical Specification: Advanced Data Compression Module\n\n## 1. Introduction\n\nThis technical specification outlines the requirements, design, and testing procedures for an advanced data compression module, intended for integration into our existing scalable and distributed file storage system. The module will enhance storage efficiency by reducing data redundancy and minimizing storage space requirements.\n\n## 2. Requirements\n\n### 2.1 Functional Requirements\n\n- Implement industry-standard compression algorithms, such as gzip, DEFLATE, and LZ4.\n- Support variable compression levels for trade-offs between compression ratio and processing time.\n- Ensure compatibility with diverse data types, including text, images, and multimedia files.\n\n### 2.2 Non-Functional Requirements\n\n- Maintain minimal impact on overall system performance.\n- Provide seamless integration with the existing file storage system.\n- Offer configurable compression settings for optimal storage efficiency.\n\n## 3. Design\n\n### 3.1 Architecture\n\nThe compression module will be designed as a modular component, integrating with the existing file storage system through a well-defined API. The module will consist of the following layers:\n\n- **Data Input:** Receives data from the file storage system.\n- **Compression Engine:** Applies selected compression algorithms.\n- **Data Output:** Returns compressed data to the file storage system.\n\n### 3.2 Compression Algorithms\n\nThe compression module will support multiple algorithms, allowing users to choose the most suitable option based on their specific use case and performance requirements.\n\n### 3.3 Configuration Interface\n\nA user-friendly configuration interface will be provided, enabling users to adjust compression levels and select preferred algorithms.\n\n## 4. Testing\n\n### 4.1 Test Cases\n\n- Verify compatibility with various data types.\n- Measure compression ratios for different algorithms and compression levels.\n- Evaluate performance impact on the overall system.\n- Test error handling and recovery procedures.\n\n### 4.2 Test Data\n\nTest data will include a diverse set of files, such as text documents, images, audio files, and video files, to ensure comprehensive testing and validation.\n\n### 4.3 Test Environment\n\nTesting will be conducted in a controlled environment, replicating the production system's hardware and software configurations.\n\n## 5. Acceptance Criteria\n\n- The compression module must support all required compression algorithms.\n- Compression ratios must meet or exceed industry standards for the respective algorithms.\n- The module must not significantly impact overall system performance.\n- The module must pass all defined test cases and meet non-functional requirements.\n\n## 6. Future Enhancements\n\nFuture enhancements may include:\n\n- Integration with machine learning algorithms for intelligent compression.\n- Support for emerging compression standards and technologies.\n- Advanced error detection and correction mechanisms."]} +{"qna_file": "compositional_skills/writing/freeform/technical/proposal/qna.yaml", "question_id": "283000973962090531435491508178730939147", "category": "taxonomy", "turns": ["Write a technical proposal for the development of a mobile app, addressing user experience, functionality, and performance."], "reference": ["# Technical Proposal: Mobile App Development\n\n## Executive Summary\n\nThis proposal outlines the development of a user-friendly mobile application that prioritizes exceptional user experience, robust functionality, and optimal performance. The app will cater to a diverse user base, offering a seamless and engaging experience across various platforms.\n\n## 1. Introduction\n\n### 1.1 Background\n\nIn today's fast-paced digital world, mobile applications have become an essential tool for businesses and individuals alike. A well-designed app can significantly enhance user engagement, streamline processes, and foster growth.\n\n### 1.2 Objectives\n\nThe primary objectives of this mobile app development project are:\n- User Experience: Design an intuitive and engaging interface that meets user needs and expectations.\n- Functionality: Implement essential features and tools that cater to the target audience.\n- Performance: Ensure the app runs smoothly and efficiently on various devices and platforms.\n\n## 2. User Experience Design\n\n### 2.1 User Research\n\nWe will conduct user research to identify user needs, preferences, and pain points. This will inform the design process and ensure the app meets user expectations.\n\n### 2.2 User Interface (UI) Design\n\nThe app's UI will be clean, modern, and visually appealing. We will follow best practices in mobile UI design, ensuring easy navigation, clear call-to-action buttons, and consistent layouts.\n\n### 2.3 User Testing\n\nUser testing will be conducted throughout the development process to validate design decisions and identify areas for improvement.\n\n## 3. Functionality\n\n### 3.1 Core Features\n\nThe app will include essential features that cater to the target audience. These features will be prioritized based on user research and feedback.\n\n### 3.2 Future Enhancements\n\nWe will maintain a roadmap of potential future enhancements, allowing for continuous improvement and expansion of the app's capabilities.\n\n## 4. Performance\n\n### 4.1 Platform Compatibility\n\nThe app will be developed for both iOS and Android platforms, ensuring compatibility with a wide range of devices.\n\n### 4.2 Optimization\n\nWe will employ various optimization techniques to ensure the app runs smoothly and efficiently. These techniques include code optimization, image compression, and caching strategies.\n\n### 4.3 Monitoring and Maintenance\n\nPost-launch, we will continuously monitor the app's performance and address any issues promptly. Regular updates will be provided to maintain compatibility with new operating systems and devices.\n\n## 5. Development Process\n\n### 5.1 Agile Methodology\n\nWe will follow an Agile development process, allowing for flexible and iterative development, frequent feedback, and quick adaptation to changing requirements.\n\n### 5.2 Project Timeline\n\nA detailed project timeline will be established, outlining milestones, deadlines, and deliverables.\n\n## 6. Conclusion\n\nThis mobile app development proposal aims to deliver a high-quality app that excels in user experience, functionality, and performance. By following a user-centric design approach, employing robust optimization techniques, and adhering to Agile development methodologies, we are confident in our ability to exceed user expectations and deliver a valuable tool for the target audience."]} +{"qna_file": "compositional_skills/writing/freeform/technical/report/qna.yaml", "question_id": "116549593239105442602412504874468661757", "category": "taxonomy", "turns": ["Compose a technical report on the benefits and challenges of implementing microservices architecture in software development."], "reference": ["# Technical Report: Microservices Architecture in Software Development\n\n## Executive Summary\n\nThis report explores the benefits and challenges associated with implementing microservices architecture in software development. The report highlights the potential advantages, such as increased scalability and development flexibility, as well as the challenges, including complex communication and testing requirements.\n\n## 1. Introduction\n\n### 1.1 Background\n\nMicroservices architecture is an approach to software development that involves building applications as a collection of small, independent services that communicate through APIs. This approach has gained popularity due to its potential benefits in terms of scalability, maintainability, and development speed.\n\n### 1.2 Objectives\n\nThe primary objectives of this report are to:\n- Evaluate the benefits of implementing microservices architecture.\n- Identify the challenges associated with microservices architecture.\n- Provide recommendations for successful implementation.\n\n## 2. Benefits of Microservices Architecture\n\n### 2.1 Scalability\n\nMicroservices architecture allows for horizontal scalability, as individual services can be scaled independently based on demand. This can lead to improved performance and resource utilization.\n\n### 2.2 Development Flexibility\n\nEach microservice can be developed using a different technology stack, allowing teams to choose the best tools for each specific task. This can lead to increased development speed and innovation.\n\n### 2.3 Fault Isolation\n\nIn a microservices architecture, a failure in one service does not directly affect other services. This can lead to increased system resilience and reduced downtime.\n\n## 3. Challenges of Microservices Architecture\n\n### 3.1 Complex Communication\n\nMicroservices communicate through APIs, which can lead to complex communication patterns and increased network latency. This can impact system performance and require careful design and optimization.\n\n### 3.2 Testing and Debugging\n\nTesting and debugging in a microservices architecture can be more complex due to the distributed nature of the system. This requires robust testing strategies and debugging tools.\n\n### 3.3 Service Coordination\n\nCoordinating multiple services can be challenging, particularly in terms of ensuring data consistency and managing transactions. This requires careful design and implementation of service coordination mechanisms.\n\n## 4. Recommendations for Implementation\n\n### 4.1 Adopt a Service-Oriented Design Approach\n\nStart by identifying the key services required for the application and design each service with a clear, well-defined interface.\n\n### 4.2 Implement DevOps Practices\n\nAdopt DevOps practices, such as continuous integration and delivery, to manage the complexity of microservices development and deployment.\n\n### 4.3 Invest in Monitoring and Logging Tools\n\nImplement robust monitoring and logging tools to help detect and diagnose issues in the system.\n\n## 5. Conclusion\n\nMicroservices architecture offers significant benefits in terms of scalability, development flexibility, and fault isolation. However, it also presents challenges in terms of communication, testing, and service coordination. Successful implementation requires a service-oriented design approach, DevOps practices, and robust monitoring and logging tools.\n\n## 6. Future Enhancements\n\nFuture development will focus on addressing the challenges of microservices architecture, including improving communication efficiency, simplifying testing and debugging, and enhancing service coordination mechanisms."]} diff --git a/eval_output/mt_bench_branch/main/reference_answer/instructlab/granite-7b-lab.jsonl b/eval_output/mt_bench_branch/main/reference_answer/instructlab/granite-7b-lab.jsonl new file mode 100644 index 0000000..0d12ad2 --- /dev/null +++ b/eval_output/mt_bench_branch/main/reference_answer/instructlab/granite-7b-lab.jsonl @@ -0,0 +1,384 @@ +{"question_id": "186038087183832915305325427194848080180", "answer_id": "JQrj8nsTZXruxeWcTUeeYa", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Hello! I'm here to help you understand the order of arrival at a party. Let's analyze the situation step by step:\n\n1. Joe reached first (before you).\n2. You arrived next.\n3. David arrived after you.\n4. John reached last (after David).\n\nBased on this information, Joe was the first one to arrive at the party, followed by you, then David, and finally John.\n"]}], "tstamp": 1730331263.173472} +{"question_id": "215948836162377945439927729315702901781", "answer_id": "YviuAwN7uDz9oZDimPxFiq", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Hello! I'm here to help you understand the order of arrival at a celebration. Let's analyze the situation step by step:\n\n1. Emma reached first (before Peter).\n2. Peter arrived next.\n3. Sophia arrived after Peter.\n4. Olivia reached last (after Sophia).\n\nBased on this information, Emma was the first one to arrive at the celebration, followed by Peter, then Sophia, and finally Olivia.\n"]}], "tstamp": 1730331263.173502} +{"question_id": "239330987684746270140406408262195855542", "answer_id": "6tALco7CcVamjgwAspFQ4S", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Hello! I'm here to help you understand the order of photos in a photography competition. Let's analyze the situation step by step:\n\n1. Emily captured her photo first (before James).\n2. James captured a photo next.\n3. Alex entered with a photo after James.\n4. Sarah submitted her photo last (after Alex).\n\nBased on this information, Emily's photo was captured first, followed by Jame's entry, then Alex's photo, and finally Sarah's submission.\n\n"]}], "tstamp": 1730331263.1735098} +{"question_id": "242886683457958175188717141363968452444", "answer_id": "azSw97RwuD2GdLwszcDg6f", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["A kilo of feathers is heavier than a pound of hammers. 1 kilo is equal to ~2.2046 pounds, so no matter what you are weighing 1 kilo will be heavier than 1 pound\n"]}], "tstamp": 1730331263.17439} +{"question_id": "233229534488351620628102146533821087135", "answer_id": "WXZ7LQAVS6AqeDY4Ci47hn", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["100 servers. If 1 Administrator can manage 10 servers, and we increase that number by 10, we can assume that 10 administrators could handle 10 times the number of servers 10x10=100 \n"]}], "tstamp": 1730331263.174399} +{"question_id": "238611654959537552555222005657320456359", "answer_id": "i4fTskT2FbGdtaBU7bK4VL", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["You need either 1 x 2 socket subscription, or 16 x 2-core subscriptions.\n"]}], "tstamp": 1730331263.1744058} +{"question_id": "251187812289715631442369307574029139622", "answer_id": "hAphfGatsusX7rDHz6QZEn", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Sarah placed her shoes under her dress before she went shopping. Her sister borrowed the shoes and put them under Sarah\u2019s bed. Sarah doesn\u2019t know that her sister borrowed her shoes. Therefore, the answer is yes, she assumes that the shoes are still under her dress.\n"]}], "tstamp": 1730331263.176817} +{"question_id": "13217000458772167243141483582150185270", "answer_id": "MfTrtZ6diem8wRUJRcV6AP", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["When the Garcia family left, the game was tied, 3-3, and they expected the game to be postponed due to the rain. So, they don't know that the game ended during their commute with a score of 5-3. Therefore, the answer is no, they don't believe the score is 5-3.\n"]}], "tstamp": 1730331263.17683} +{"question_id": "204625558555813571908604531813394942740", "answer_id": "D5cJvtoua7jFwfGpRbfNGH", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["When Lisa left Jacob, he was asleep on the beach. So, Lisa doesn't know that Jacob woke up and went swimming. Therefore, the answer is yes, Lisa believes that Jacob is still asleep on the beach.\n"]}], "tstamp": 1730331263.1768372} +{"question_id": "318549297918654447514356663453222375932", "answer_id": "h8Uddn6aCMihGDGGMtC2C5", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Since Pam has been at work all day, she doesn't know that the tulips in her backyard have suddenly bloomed. Therefore, the answer is no, when driving home she doesn't assume that her tulips have bloomed.\n"]}], "tstamp": 1730331263.176843} +{"question_id": "319839001852604230671498528924789687512", "answer_id": "StG2g54McrrLAJLApvGQDb", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The scenario mentions that Jeff put on a pink shirt instead of a white one. This means he wanted to put on a white shirt but since he is colorblind and can't tell the difference, he mistakenly put on a pink shirt. He doesn't know we is wearing a pink short. Therefore, the answer is yes, he think he is wearing a white shirt.\n"]}], "tstamp": 1730331263.176849} +{"question_id": "44149513413682805844664797051944968063", "answer_id": "TaPaZkKsMCEUsJJgmLXEEi", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The scenario mentions that the lab was empty that night. Therefore, the answer is yes, the number of people in the lab that night is zero.\n"]}], "tstamp": 1730331263.176856} +{"question_id": "63498394932145751423557426078215489111", "answer_id": "XF3c4ivL2hLNDvnukLfHam", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The scenario mentions that the country\u2019s economic success were recorded in books from the early 1900s. Therefore, the answer is yes, early 1900s novels portray the country as experiencing economic wealth.\n"]}], "tstamp": 1730331263.1768608} +{"question_id": "205308421853532842346513294370366642610", "answer_id": "JcGPCxuRwzXdazkfC8rTQM", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The scenario mentions that a part of garden is reserved for roses and it has a label showing that. Therefore, the answer is yes, the label say the flowers are roses.\n"]}], "tstamp": 1730331263.176866} +{"question_id": "310855955988527299360696225432626887530", "answer_id": "35rUBRoKP6N3AsFryQpbVe", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["No that is incorrect, In golf, the objective is to score as few points as possible through various methods such as driving, chipping, and putting. The player with the lowest score at the end of the round wins.\n"]}], "tstamp": 1730331263.177592} +{"question_id": "78736441356384569630737498464735933862", "answer_id": "8TESf3hZo9iznugKbxByFe", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["In rummy, the player with the lowest score wins. \n"]}], "tstamp": 1730331263.177598} +{"question_id": "118971624301146257272024515530524869077", "answer_id": "YoSYYMM3ZKnkkwAE6r9EsT", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["In most running competitions, including marathons, the winner is the runner who completes the racecourse in the shortest amount of time. So, in this case, the runner with a time of 4 hours and 20 minutes would win because they completed the race faster than the runner with a time of 4 hours and 30 minutes.\n"]}], "tstamp": 1730331263.177603} +{"question_id": "292983752102232095864714663481939268406", "answer_id": "G5KGyKuHw2kptWmR9nHRhU", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The answer is 3 killers. A person comes into the room and kills one of the 3 killers, so 2 of the 3 killers remain. However the new person, having killed one of the killers, is now a killer. Hence there are 3 killers in the room. \n"]}], "tstamp": 1730331263.1784} +{"question_id": "79697302777874382304266575075184254147", "answer_id": "GuRRcNsXFMSvCagafoaA2n", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The answer is 4 hours. Since I am drying the shirts in the sun, it does not matter if I dry 4 or 8 shirts, it will take the same amount of time.\n"]}], "tstamp": 1730331263.178405} +{"question_id": "320590730897033119915839694782416720843", "answer_id": "dEReb68bLL6fw9ujx9hCLm", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The ball is now in the box. Jack put the ball in the basket. After he left, Jill put the ball in the box, so it is now in the box.\n"]}], "tstamp": 1730331263.17841} +{"question_id": "38178989809535383561614815864559057197", "answer_id": "VynaTbpbGTDrrpC4FUmMzz", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Here is a meaningful sequence:\n1. Crime\n2. Police\n3. Judge\n4. Judgment\n5. Punishment\n"]}], "tstamp": 1730331263.179035} +{"question_id": "6767681416681346381744048511682034386", "answer_id": "SeYDdeyYysyLzRjx5oMGHk", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Here is a meaningful sequence:\n1. Infection\n2. Doctor\n3. Consultation\n4. Treatment\n5. Recovery\n"]}], "tstamp": 1730331263.17904} +{"question_id": "53622117114584765484861387700826501422", "answer_id": "WymgVdzW49NhUHepBb5qBG", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Here is a meaningful sequence:\n1. Letters\n2. Word\n3. Phrase\n4. Sentence\n5. Paragraph\n"]}], "tstamp": 1730331263.179044} +{"question_id": "324795436124537533142216411539653654022", "answer_id": "6dmz2APPJSesAqWvSHjwe9", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["C. important. Remaining are synonyms of each other.\n"]}], "tstamp": 1730331263.179606} +{"question_id": "286487527560524977650000710931754090809", "answer_id": "J2TnT3v25xHEcVnazrHp8y", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["D. Book. Rest are all parts of a book.\n"]}], "tstamp": 1730331263.179611} +{"question_id": "193212596901443399313689097544179034637", "answer_id": "mAmdeCCQSFCoucvK6pkMSZ", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["A. hate. Rest are positive emotions.\n"]}], "tstamp": 1730331263.179615} +{"question_id": "9376129652505313748988900880362788597", "answer_id": "c8mJ8HxW7Aoh5shvwb3frZ", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The list contains two distinct colors - blue and pink.\n"]}], "tstamp": 1730331263.180234} +{"question_id": "95202116628166667051208889618180112758", "answer_id": "Qxvk387WRJTJm5yLgVgAMi", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["There are 2 human body parts: Bone and tongue. Depending on if you consider nails to be fingernails, then you could also include them as a third body part.\n"]}], "tstamp": 1730331263.1802392} +{"question_id": "255872237613884807711965944467225129033", "answer_id": "3mUbrr7Nq7uqkugpNa6NYz", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The following items in the list are associated with both humans and animals: intestine, cheating. \n"]}], "tstamp": 1730331263.180244} +{"question_id": "225571771206761594158263437563717696010", "answer_id": "XhZkEw8fJ4QJ2dsWfZn9ot", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["While days tend to be longer in the summer, just because it is not summer doesn't mean days are necessarily shorter.\n"]}], "tstamp": 1730331263.180871} +{"question_id": "31075242281634831797237789507450794676", "answer_id": "DWzKtDCcCDuJpwCVjTEeZ5", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["No, we cannot conclusively conclude that some cats are black based solely on the given premises. The statement \"some mammals are black\" does not necessarily guarantee that among those mammals are cats.\n"]}], "tstamp": 1730331263.180876} +{"question_id": "186605568946423368062781809941052703119", "answer_id": "PxdQsTzAeX2WDtchvvbBtw", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Yes, we can conclude that all squares have four sides based on the given premises.\n"]}], "tstamp": 1730331263.1808798} +{"question_id": "122000398825150483085330538623944234347", "answer_id": "eegSszwXKBLpGfgmjvxKRC", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["If you have one bun and need to share it among 7 kids without splitting it, each child would receive 1/7 of the bun. So the split fraction would be 1/7.\n"]}], "tstamp": 1730331263.184003} +{"question_id": "321250237548041497069373588875103125878", "answer_id": "5KaAFBS3v9ZYsGmtjU3CfL", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The doctor instructs you to take one pill every half hour. So, you would take the first pill immediately, the second pill after 30 minutes, and the third pill after another 30 minutes, totaling one hour. After one hour, all three pills would have been consumed.\n"]}], "tstamp": 1730331263.184008} +{"question_id": "199975546227160776896763216962472780767", "answer_id": "B7n67rmiih4Agm3mdvPTPj", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The answer to this question is still 5 minutes.\nThe rate at which the widgets are produced remains constant regardless of the number of machines. Each machine produces one widget in 5 minutes, so whether there are 5 machines or 100 machines, it would still take 5 minutes to produce 100 widgets.\n"]}], "tstamp": 1730331263.184012} +{"question_id": "248025386135807391370680093872004527973", "answer_id": "98PtjKPxnNv79VBPHvp3Px", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The odd one out is Moon. All of the others are planets from the solar system, but Moon is a satillite that orbits around the Earth.\n"]}], "tstamp": 1730331263.184017} +{"question_id": "163998429574116457272751395628485792572", "answer_id": "Q5q5SgwAwHMDcbvR3twcxR", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["A is D's aunt. \nD is the brother of E\nE is the daughter of B, making D the son of B\nB is the sister of A, making A the aunt of both D and E. \n"]}], "tstamp": 1730331263.184021} +{"question_id": "283956719709768162058781448709604191788", "answer_id": "5kxCWq3VtXK7kZYjd2VfPx", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["6\\. The series is dividing the the number in half at each step\n"]}], "tstamp": 1730331263.184025} +{"question_id": "76341820815379879552305133803024133009", "answer_id": "jLbWboNP4CgP7SjmmVe3tw", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Sam doesn't run faster than Jane as Jane runs faster than Joe and Joe runs faster than Sam.\n"]}], "tstamp": 1730331263.1840289} +{"question_id": "285694428740467711736111444912935277221", "answer_id": "N7wYvVh34st7WNPTemui6r", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["When drying clothes outside, several factors can affect the drying time, such as temperature, humidity, wind, and the number of shirts.\nIf there's sufficient space for drying shirts in parallel, drying 20 shirts could take as long as drying 5 shirts so 4 hours. However, when there's only sufficient space for 5 shirts, you'll need to dry the shirts in batches so considering that you need to dry 20 shirts, you would need 4 batches bringing the total time to 16 hours.\n\n"]}], "tstamp": 1730331263.184033} +{"question_id": "171343142260026728274898175026241576716", "answer_id": "7SFZVKVyVdnmsPfQsH4bs7", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Marc thinks the ball is in the basket as that's where it was when he left the room. John, on the other hand, thinks the ball is in the cupboard where he put it before he left the room.\n"]}], "tstamp": 1730331263.184038} +{"question_id": "97084977809626648492150587285270862247", "answer_id": "cRsizRMmMqVTAdkNrpMVfy", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Based on the information provided, the names of Mike's mom's four kids are Luis, Drake, Matilda, and Mike. Therefore the name of the 4th kid is Mike.\n"]}], "tstamp": 1730331263.184042} +{"question_id": "223020763880202331327745094787031161517", "answer_id": "HmZ4RiEgHKWYgbTxpPnopB", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Let's analyze the situation step by step:\n\n1\\. We know that 4 cars traveled from San Francisco to San Jose, which is 41.60 miles apart.\n2\\. Each car travels at a similar speed. Having more cars on the road will not reduce the time that any one car takes to drive from San Francisco to San Jose.\n3\\. Therefore, the time it takes for each car to travel the distance is same, 1 hour.\n"]}], "tstamp": 1730331263.184046} +{"question_id": "35247899971565308536470213322269454259", "answer_id": "AZuBxTgSeK62FZMMxPLhyo", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Based on the information provided, we know that Jane has one car and that it is in the driveway when she is at home. However, when Jane is at her office, we know that she used her car for commuting. This implies that the car is not in the driveway. As Jane only has 1 car, then there must be no cars in Jane's driveway now. Therefore the answer is 0.\n"]}], "tstamp": 1730331263.184051} +{"question_id": "138091241521529880319109143777337935312", "answer_id": "mMLGQAKDFSU4VqFtvadnuM", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["C (The student's marks is between 40 and 60.)\n"]}], "tstamp": 1730331263.184055} +{"question_id": "213355609015880795968163047748418640088", "answer_id": "LybTb6t38HuvyYSwF4JKuz", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Intermediate\n"]}], "tstamp": 1730331263.1840599} +{"question_id": "44339368633466322891100739551808735676", "answer_id": "NnrkUqsveiSBZBeacr2V2n", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Enthusiast\n"]}], "tstamp": 1730331263.184065} +{"question_id": "234233870159341419842865244848953191242", "answer_id": "SXJp7haWqd5tQjtB6tJUuV", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["To predict the next value in the temperature sequence, we can look at the pattern in the previous values. The differences between consecutive temperatures are:\n\n2 (day 2 - day 1)\n3 (day 3 - day 2)\n3 (day 4 - day 3)\n4 (day 5 - day 4)\n\nThis increasing difference suggests that the temperature will continue to increase. To find the next temperature, we can add the difference from day 4 to the temperature of day 4:\n\nNext temperature = Day 4 temperature + Difference\n= 28 \u00b0C + 3 \u00b0C\n= 31 \u00b0C\n\nTherefore, the next value in the sequence is 31 degrees Celsius.\n"]}], "tstamp": 1730331263.1851761} +{"question_id": "212185784796983471810902311649092104842", "answer_id": "465W5Y7t4m6AhFJmatc3QH", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["North: Product A\nSouth: Product C\nEast: Product A\nWest: Product C\n"]}], "tstamp": 1730331263.185181} +{"question_id": "200656539618809357681888915956904200911", "answer_id": "AQFuDDuwhkwSyfPRExC7w5", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["To calculate the total number of fruits bought on each day we first sum up the number of fruits bought in each day.\nTotal fruits for A = 5 (Day 1) + 8 (Day 2) + 4 (Day 3) = 17\nTotal fruits for B = 3 (Day 1) + 6 (Day 2) + 7 (Day 3) = 16\nTotal fruits for C = 2 (Day 1) + 5 (Day 2) + 6 (Day 3) = 13\n\nHence on Day 3 the lowest number of fruits were bought.\n"]}], "tstamp": 1730331263.1851861} +{"question_id": "196886616014243165011351365362811958343", "answer_id": "nX8SD9PTTrQuZTtAfF7LFa", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["```\n| | | |\n| -- | | |\n| 1 | | |\n| 2 | | |\n| 3 | | |\n| 4 | | |\n| 5 | | |\n| 6 | | |\n| 7 | | |\n| 8 | | |\n| 9 | | |\n| 10 | | |\n| 11 | | |\n| 12 | | |\n| 13 | | |\n| 14 | | |\n| 15 | | |\n| 16 | | |\n| 17 | | |\n| 18 | | |\n| 19 | | |\n| 20 | | |\n| 21 | | |\n| 22 | | |\n| 23 | | |\n| 24 | | |\n| 25 | | |\n| 26 | | |\n| 27 | | |\n| 28 | | |\n| 29 | | |\n| 30 | | |\n```\n\n"]}], "tstamp": 1730331263.186343} +{"question_id": "239691706976310879670810477668222868605", "answer_id": "3SDCR5h2L8jLtVicTN2RiP", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["```\n| | | | | | | |\n| - | | | | | | |\n| 1 | | | | | | |\n| 2 | | | | | | |\n| 3 | | | | | | |\n| 4 | | | | | | |\n| 5 | | | | | | |\n| 6 | | | | | | |\n| 7 | | | | | | |\n```\n\n"]}], "tstamp": 1730331263.186348} +{"question_id": "5642324896298622635011223790098755871", "answer_id": "92hm2PS4iEeYQZmyxkDoYq", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["|\u00a0\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\n| - |\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\n| 1 |\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\n| 2 |\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\n| 3 |\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\u00a0|\n"]}], "tstamp": 1730331263.186352} +{"question_id": "137721570681090875484008252437754673924", "answer_id": "dFzLaWTrBrDfiQYQAytM6b", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["I have removed all rows that had a vowel in the first initial\n\n```\n| First Initial | Last Name | Company |\n| ------------- | --------- | ------- |\n| b | jones | xyz |\n| c | george | abc |\n| d | hicks | xyz |\n| f | parker | abc |\n| g | burn | xyz |\n| h | talbot | abc |\n```\n\n"]}], "tstamp": 1730331263.1880372} +{"question_id": "292571355638061499832053565543520290186", "answer_id": "9zb5orgpSHYKXC9Fg3Qy7N", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["```\n| First Initial | Last Name | Email | Company |\n| ------------- | ---------- | ----- | ------- |\n| a | smith | | xyz |\n| b | jones | | xyz |\n| c | george | | abc |\n| d | hicks | | xyz |\n| e | long | | abc |\n| f | parker | | abc |\n| g | burn | | xyz |\n| h | talbot | | abc |\n| i | washington | | abc |\n```\n\n"]}], "tstamp": 1730331263.188042} +{"question_id": "287140923584038829006734185307350402552", "answer_id": "CyaS7HFais7xb7aXCtP6iM", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["```\n| First Initial | Last Name | Company |\n| ------------- | ---------- | ------- |\n| a | smith | xyz |\n| b | jones | xyz |\n| c | george | abc |\n| d | hicks | xyz |\n| e | long | abc |\n| f | parker | abc |\n| g | burn | xyz |\n| h | talbot | abc |\n| i | washington | abc |\n| r | frank | xyz |\n| p | parks | abc |\n| t | higgins | efg |\n| q | riley | efg |\n```\n\n"]}], "tstamp": 1730331263.1880472} +{"question_id": "208725020637644065902544089637889259495", "answer_id": "oY25MgtJ2mcqegtNA6YRaL", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["```\n| First Initial | Last Name | Company | Email |\n| ------------- | ---------- | ------- | ------------------- |\n| a | smith | xyz | asmith@xyz.com |\n| b | jones | xyz | bjones@xyz.com |\n| c | george | abc | cgeorge@abc.com |\n| d | hicks | xyz | dhicks@xyz.com |\n| e | long | abc | elong@abc.com |\n| f | parker | abc | fparker@abc.com |\n| g | burn | xyz | gburn@xyz.com |\n| h | talbot | abc | htalbot@abc.com |\n| i | washington | abc | iwashington@abc.com |\n```\n\n"]}], "tstamp": 1730331263.1898189} +{"question_id": "113398287520622914570960623082876417480", "answer_id": "PuKBbS3uNQ7kZ6VR9Ew9iQ", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["```\n| Email | First Initial | Last Name | Company |\n| ------------------- | ------------- | ---------- | ------- |\n| asmith@xyz.com | a | smith | xyz |\n| bjones@xyz.com | b | jones | xyz |\n| cgeorge@abc.com | c | george | abc |\n| dhicks@xyz.com | d | hicks | xyz |\n| elong@abc.com | e | long | abc |\n| fparker@abc.com | f | parker | abc |\n| gburn@xyz.com | g | burn | xyz |\n| htalbot@abc.com | h | talbot | abc |\n| iwashington@abc.com | i | washington | abc |\n```\n\n"]}], "tstamp": 1730331263.189824} +{"question_id": "207059250253775485046927178165580494577", "answer_id": "TypEfn7Cqip7iNtWRodPGR", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["```\n| First Initial | Last Name | Company | username |\n| ------------- | ---------- | ------- | -------------- |\n| a | smith | xyz | xyz-smith |\n| b | jones | xyz | xyz-jones |\n| c | george | abc | abc-george |\n| d | hicks | xyz | xyz-hicks |\n| e | long | abc | abc-long |\n| f | parker | abc | abc-parker |\n| g | burn | xyz | xyz-burn |\n| h | talbot | abc | abc-talbot |\n| i | washington | abc | abc-washington |\n```\n\n"]}], "tstamp": 1730331263.189829} +{"question_id": "337304126539778877681267727421536727602", "answer_id": "hLqXuBjvue7zv68XVrkUg7", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The formula to convert Fahrenheit to Celsius is ((F)-32)\\*5/9. When applying this formula, you can see that (41-31)\\*5/9=5. So 41 degrees Fahrenheit is 5 degrees Celsius.\n"]}], "tstamp": 1730331263.19045} +{"question_id": "87915633027137148319779609222423399625", "answer_id": "jcZ7HwsrJoMxHyezhCshkc", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The formula to convert Celsius to Fahrenheit is (C\\*9/5)+32. When applying this formula, you can see that (38\\*9/5)+32=100.4. So 38 degrees Celsius is 100.4 degrees Fahrenheit.\n"]}], "tstamp": 1730331263.190455} +{"question_id": "7286473655077226826746870490519514766", "answer_id": "6jTDAaFSowE2v5uB9b3Wya", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["To compare temperatures in Celsius and Fahrenheit, you can convert one to the other scale. The formula to convert Fahrenheit to Celsius is C=(F-32)\\*5/9\nLets convert 65 degrees Fahrenheit to Celsius\nC=(65-32)\\*5/9\nC=18.33\nTherefore, 35 degrees Celsius is hotter than 65 degrees Fahrenheit, as 35 degrees Celsius is equivalent to 95 degrees Fahrenheit.\n"]}], "tstamp": 1730331263.19046} +{"question_id": "47305076959776596825918658225751656136", "answer_id": "3g8DztaFnRoNgkTEpja7Tj", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Hello! To convert 14 kilometers into miles, you can use the conversion factor 1 km \u2248 0.62 miles . Therefore, 14 kilometers is equivalent to approximately 8.69 miles. Let me know if you have any other questions or need assistance with something else.\n"]}], "tstamp": 1730331263.1910799} +{"question_id": "116497979546558092828529943984838741320", "answer_id": "aGCoGb2zYhY3zGpD5UUXbq", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Hello! To convert 2 kilometers into miles, you can use the conversion factor 1 km \u2248 0.62 miles . Therefore, 2 kilometers is equivalent to approximately 1.24 miles. Let me know if you have any other questions or need assistance with something else.\n"]}], "tstamp": 1730331263.191085} +{"question_id": "128572342126859538078283535872720049007", "answer_id": "R7kzuV9SW42kFLRoBJUmAY", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Hello! To convert 6 miles into kilometers, you can use the conversion factor 1 mile \u2248 1.62 kilometers. Therefore, 6 miles is equivalent to approximately 9.65 kilometers. Let me know if you have any other questions or need assistance with something else.\n"]}], "tstamp": 1730331263.1910899} +{"question_id": "259155010096837532153920673213319362600", "answer_id": "kGbAeSKqVShCrsPkq4DF6o", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Here are few countries that starts with letter \"V\"\n- Vanuatu.\n- Vatican City.\n- Venezuela.\n- Vietnam.\n"]}], "tstamp": 1730331263.191566} +{"question_id": "145365849075397177510681639653348364373", "answer_id": "aV99qaVtWRAE3psfXiqwB9", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["There are 3 countries that start with the letter \"J\":\n1\\. Japan\n2\\. Jordan\n3\\. Jamaica\n"]}], "tstamp": 1730331263.1915698} +{"question_id": "65845626335159062682373268350400449802", "answer_id": "9AZFMLErtoeqzkxP5CHnPC", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Wales is the only country that starts with the letter W\n"]}], "tstamp": 1730331263.191574} +{"question_id": "326984083497849738224610300989191623177", "answer_id": "4zPKADsdD4xqoF6psR6UGk", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Hello! I'm here to help you with this age-related problem. Let's break it down step by step:\n\n1\\. We know that Joey is 20 years younger than Becky, so Becky's age = 20 + Joey's age.\n2\\. Two years from now, Becky will be twice as old as Joey. In other words, (Becky's age + 2) = 2(Joey's age + 2).\n3\\. We can combine the first two equations to find the relationship between Becky's age and Joey's age: 20 + Joey's age + 2 = 2(Joey's age + 2)\n4\\. Solve for Joey's age: Joey's age = 18\n5\\. Now that we have the relationship between Becky's age and Joey's age, we can find their current ages by substituting the given information: Becky's age = (Joey's age + 20) = 38\n\nTherefore, Joey is currently 18 years old and Becky is currently 38 years old.\n"]}], "tstamp": 1730331263.192811} +{"question_id": "62205968969762978497053348605814382856", "answer_id": "mKBaW4XXbSkGJvkHcgYDT9", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Hello! I'm here to help you with this age-related problem. Let's break it down step by step:\n\n1\\. We know that Louise is 26 years old and her daughter Carmen is 4 years old.\n2\\. We want to find out in how many years Louise will be double her daughter\u2019s age. Mathematically, this can be represented as: Louise's age + Time = 2 \\* ( Carmen's age + Time)\n3 . To find out in how many years Louise will be double her daughter\u2019s age, we need to put the values of Carmen's age: 4 and Louise's age = 26 \n4\\. So we need to solve: 26 + Time = 2 \\* ( 4 + Time)\n4\\. Solving this equations gives Time = 18\n\nTherefore, it will take Louise 18 years to be double her daughter\u2019s age.\n"]}], "tstamp": 1730331263.192816} +{"question_id": "13265720414370068757864374300718746428", "answer_id": "fLHAgxPiUJEdTaUTHmZ4ix", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Hello! I'm here to help you with this age-related problem. Let's break it down step by step:\n\n1\\. We know that Carmen is 12 years older than David. So, Carmen's age = David's age + 12.\n2\\. Five years ago, the sum of their ages was 28. So, (David's age - 5) + (Carmen's age - 5) = 28.\n3\\. We can simplify the equation by combining them: (David's age - 5) + ( David's age + 12 - 5) = 28.\n4\\. Solve for David's age: David's age = (28 - 2) / 2 = 13\n5\\. Since Carmen's age is 12 years older than David, we add 12 to f David's age in order to find Carmen's age: David's age + 12 = 25\n\nTherefore, David is currently 13 years old and Carmen is currently 25 years old.\n"]}], "tstamp": 1730331263.19282} +{"question_id": "130686829092960504543489287239489465667", "answer_id": "7DRNFZEDoAQ726zdPQaKR3", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["There are still 9 sheep left. \"All but 9\" implies that 9 sheep remain.\n"]}], "tstamp": 1730331263.1933188} +{"question_id": "119682070363996541787358728705649539548", "answer_id": "kjK8c243uNjXMsLaF4veAy", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["If you passed the person in second place, you would be in second place.\n"]}], "tstamp": 1730331263.193324} +{"question_id": "1348709414930481319561143749008337549", "answer_id": "LTVNXrUWg9waUu9Mc8qX9H", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["'all but 6' means the baker has 6 cupcakes left.\n"]}], "tstamp": 1730331263.1933281} +{"question_id": "252673827194811679027370540176416468576", "answer_id": "VfaNHViJbEZ8sSGcHdKBEk", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["To calculate the area of a circle given its radius, you can use the formula \u03c0r\u00b2, where r is the radius. In this case, the radius is 6 centimeters. So, the area would be \u03c0 \\* (6 cm)\u00b2 = approximately 113.1 square centimeters. Let me know if you have any other questions or need assistance with something else.\n"]}], "tstamp": 1730331263.19412} +{"question_id": "218226030165828125442857046998169408056", "answer_id": "CkpBBC2pBbJMsHGcsxPXrt", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["To calculate the area of a circle given its radius, you can use the formula \u03c0r\u00b2, where r is the radius. In this case, the radius is 2 meters. So, the area would be \u03c0 \\* (2 m)\u00b2 = approximately 12.57 square meters. Let me know if you have any other questions or need assistance with something else.\n"]}], "tstamp": 1730331263.194124} +{"question_id": "324906110275988358020788124265466383183", "answer_id": "eq5t2kNQP2daEdeMNphSbs", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["To find the radius of a circle given its area, you can use the formula \u03c0r\u00b2, where r is the radius and \u03c0 is approximately equal to 3.14159. In this case, the area is 12.57 square centimeters. So, you can solve for r by taking the square root of the area: r\u00b2 = (12.57 cm\u00b2)/3.14 \u2248 4.00 cm. then r=\u221a(4.00 cm\u00b2)=2 cm . So, radius of the circle is 2 cm. Let me know if you have any other questions or need assistance with something else.\n\n"]}], "tstamp": 1730331263.194129} +{"question_id": "16578662502275970942313744680967631626", "answer_id": "ZSmnx7C4LZxLyL8RopHKhT", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Solution: We know,\nCircumference of circle = 2\u03c0r = 2 x (22/7) x 21 = 2 x 22 x 3 = 132 cm\nArea of circle = \u03c0r2\u00a0= (22/7) x 212\u00a0= 22/7 x 21 x 21 = 22 x 3 x 21\nArea of circle with radius, 21cm = 1386 cm2\n"]}], "tstamp": 1730331263.194873} +{"question_id": "217560678045500153962142062248171742891", "answer_id": "5GL8EnLnnHMFZPuA5wCvuW", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Solution: Given,\nDiagonal, d = 10 cm\nHeight of one triangle, h1\u00a0= 4cm\nHeight of another triangle, h2\u00a0= 6cm\nArea of quadrilateral = \u00bd d(h1+h2) = \u00bd x 10 x (4+6) = 5 x 10 = 50 sq.cm.\n"]}], "tstamp": 1730331263.1948779} +{"question_id": "71485276075909563209233021300541560079", "answer_id": "iwcmKYF5CfRjVEQCHhCu2n", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Solution: One of the parallel sides of the trapezium is a = 20 m, let another parallel side be b, height h = 15 m.\nThe given area of trapezium = 480 m2\nWe know, by formula;\nArea of a trapezium = \u00bd h (a+b)\n480 = \u00bd (15) (20+b)\n20 + b = (480\u00d72)/15\nb = 64 \u2013 20 = 44 m\n"]}], "tstamp": 1730331263.1948822} +{"question_id": "115306318572066657834336150710640355214", "answer_id": "LaCBoDRnK2BsTkeKhQrKed", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The correct answer is 64. The pattern is that each number is the previous number multiplied by 2.\n"]}], "tstamp": 1730331263.19546} +{"question_id": "200239197920343691982445267305225673463", "answer_id": "nf3QUA3UqyDCLrVKXZSUJX", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The correct answer is I, as the sequence consists of every second letter in the English alphabet, starting with A (A, C, E, G, I...).\n"]}], "tstamp": 1730331263.195464} +{"question_id": "106055944101765991187927709200874474011", "answer_id": "hiTV9VimSsmqysj83my4L4", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The correct answer is 243. The pattern involves each number being multiplied by 3 to find the next number (3x3=9, 9x3=27, 27x3=81, 81x3=243).\n"]}], "tstamp": 1730331263.195468} +{"question_id": "194197388041822203175168971123547188319", "answer_id": "GKjLSELKKBg2AEWcYscXoi", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["In order to find the answer you can subtract the first number from the last from the last number of the last, and then add one back in (because you start counting at zero, not one) to get the final answer. So 151 - 25 + = 127.\n"]}], "tstamp": 1730331263.196141} +{"question_id": "212672488513801611223268398644988426458", "answer_id": "8J2KPxB8qUHApJuVfJ58sr", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["If 4 daps = 7 yaps, and 5 yaps = 3 baps then 42 baps \\* 5/3 yaps/baps \\* 4/7 daps/yaps = 40 daps.\n"]}], "tstamp": 1730331263.196146} +{"question_id": "318088185628758556800253822235084549673", "answer_id": "ZQb4n4fz9E3RMy9eWVJUia", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["(A) 9.8 m/s\u00b2 -- This is because even though you threw the ball the second time, it will leave your hand with a higher initial velocity, but it will still only accelerate at the same constant rate due to gravitational pull until terminal velocity is reached.\n"]}], "tstamp": 1730331263.19615} +{"question_id": "174359785553990955380227697366943479615", "answer_id": "mRfkXHJJJ4u86eXVr5JnxV", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["To calculate the three-month moving average (SMA), we need to take the average of sales figures for the past three months and then use it to forecast sales for the next month. Here's how to calculate the SMA:\n- January: Not enough data for SMA calculation\n- February: Not enough data for SMA calculation\n- March: (1000 + 1100 + 1050) / 3 = 1050\n- April: (1100 + 1050 + 1200) / 3 = 1116.67 (rounded to 2 decimal places)\n- May: (1050 + 1200 + 1250) / 3 = 1166.67 (rounded to 2 decimal places)\nTherefore, the three-month moving average sales forecast for the next three months would be:\n- June: $1116.67\n- July: $1166.67\n- August: $1216.67\n\n"]}], "tstamp": 1730331263.197813} +{"question_id": "157332575045645445929074554438558241658", "answer_id": "QvkFnDbRBZ53seFjewBuNg", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["To calculate the six-month weighted moving average (WMA), we need to multiply each sales figure by its corresponding weight, sum up the weighted values, and divide by the sum of the weights. Here's how to calculate the WMA:\n- January: Not enough data for WMA calculation\n- February: Not enough data for WMA calculation\n- March: Not enough data for WMA calculation\n- April: (1200 \\* 0.5 + 1050 \\* 0.3 + 1100 \\* 0.2) / (0.5 + 0.3 + 0.2) = 1165\n- May: (1250 \\* 0.5 + 1200 \\* 0.3 + 1050 \\* 0.2) / (0.5 + 0.3 + 0.2) = 1205\n- June: (1300 \\* 0.5 + 1250 \\* 0.3 + 1200 \\* 0.2) / (0.5 + 0.3 + 0.2) = 1255\nTherefore, the six-month weighted moving average sales forecast for the next six months would be:\n- July: $1215\n- August: $1265\n- September: $1315\n- October: $1365\n- November: $1415\n- December: $1465\n\n"]}], "tstamp": 1730331263.197818} +{"question_id": "93057076691178316614332560603505840551", "answer_id": "KDA7EMcCzJawhcsyWasHom", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["To calculate the exponential smoothing forecast, we start with the initial forecast for the first month, which is equal to the actual sales figure for the first month. Then, for each subsequent month, we update the forecast using the smoothing factor (\u03b1) and the difference between the actual sales figure and the previous forecast. Here's how to calculate the exponential smoothing forecast:\n- Initial forecast for January = Actual sales for January = $1000\n- Forecast for February:\n- Forecast for February = (\u03b1 \\* Actual sales for February) + ((1 - \u03b1) \\* Forecast for January)\n- = (0.3 \\* $1100) + (0.7 \\* $1000)\n- = $330 + $700\n- = $1030\n- Forecast for March:\n- Forecast for March = (\u03b1 \\* Actual sales for March) + ((1 - \u03b1) \\* Forecast for February)\n- = (0.3 \\* $1050) + (0.7 \\* $1030)\n- = $315 + $721\n- = $1036\nTherefore, the exponential smoothing forecast for sales for the next three months would be:\n- February: $1030\n- March: $1036\n- April: $1040.8 (rounded to 1 decimal place)\n\n"]}], "tstamp": 1730331263.197825} +{"question_id": "149401334586578214913918606614490198142", "answer_id": "fxc9LckXsYwBdMy8DDaaYE", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["To convert square feet to square meters, we can use the conversion factor of 1 square foot \u2248 0.0929 square meters. Given a value of 1000 square feet, we can calculate the equivalent area in square meters as follows:\n\n1000 square feet \u00d7 0.0929 square meters/square foot = 92.9 square meters\n\nTherefore, 1000 square feet is approximately equal to 92.9 square meters.\n"]}], "tstamp": 1730331263.19904} +{"question_id": "332435408304513045464933143755971696994", "answer_id": "3LHt57P8SWPK7YFwNhsXM8", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["To convert square meters to square feet, we can use the conversion factor of 1 square meter \u2248 10.764 square feet. Given a value of 390 square meters, we can calculate the equivalent area in square feet as follows:\n\n390 square meters \u00d7 10.764 square feet/square meter = 4197.93 square feet\n\nTherefore, 390 square meters is approximately equal to 4197.93 square feet.\n\n"]}], "tstamp": 1730331263.1990452} +{"question_id": "256422175651861610409489168209529815135", "answer_id": "3EEvEK9q5aN3uN4fkPrK2Q", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["To compare the sizes of 1000 square feet and 90 square meters, we can convert both values to the same unit (square meters) using the provided conversion factors:\n\n1000 square feet \u00d7 0.0929 square meters/square foot \u2248 92.9 square meters\n90 square meters \u00d7 10.764square meter/square foot \u2248 968.752 square feet\n\nComparing the two results, we find that 1000 square feet is larger than 90 square meters.\n"]}], "tstamp": 1730331263.199049} +{"question_id": "190848592641402308122310768907541229262", "answer_id": "XHmUQTkmjDagNLeYQa9hgp", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["To convert 500 miles to feet, you can use the conversion factor 1 mile = 5,280 feet. Therefore, to find the equivalent length in feet, multiply the number of miles by the conversion factor: 500 miles \u00d7 5,280 feet/mile = 2,640,000 feet. I hope this information is helpful! Let me know if you have any other questions.\n"]}], "tstamp": 1730331263.199053} +{"question_id": "295434104124528771206372877081304336750", "answer_id": "CFrLFa4PrBiUgAp5qJ3VAx", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["To convert 2 miles to feet, you can use the conversion factor 1 mile = 5,280 feet. Therefore, to find the equivalent length in feet, multiply the number of miles by the conversion factor: 2 miles \u00d7 5,280 feet/mile = 10,560\n"]}], "tstamp": 1730331263.1990569} +{"question_id": "234310271218979560355344740039067161206", "answer_id": "cAn3Joss4MPr3ScuiGzUrn", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["To convert 1000 miles to feet, you can use the conversion factor 1 mile = 5,280 feet. Therefore, to find the equivalent length in feet, multiply the number of miles by the conversion factor: 1000 miles \u00d7 5,280 feet/mile = 5,280,000 feet. I hope this information is helpful! Let me know if you have any other questions.\n"]}], "tstamp": 1730331263.199061} +{"question_id": "148667943096617929777199110574640195820", "answer_id": "UQpFALS5wAQ2hr6YqfeeGu", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["FlowRepairAgent\n"]}], "tstamp": 1730331263.200261} +{"question_id": "2399448008627548149537700408182910775", "answer_id": "6Y5BbevgK5BDzaWfXJKG6S", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["FlowGeneratorAgent\n"]}], "tstamp": 1730331263.200267} +{"question_id": "108703527040885597689279459999750686427", "answer_id": "QRo6Y34b2Fi3Xkqo2Xq2Wu", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["FlowRepairAgent\n"]}], "tstamp": 1730331263.2002718} +{"question_id": "186093931508315906762359852203650858695", "answer_id": "K8gj9GCFmRW9PA4LqAvqUF", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Here are 10 words that start with the letter \"a\":\n1. Apple\n2. Adventure\n3. Amazing\n4. Antarctica\n5. Adorable\n6. Accelerate\n7. Artist\n8. Attitude\n9. Assertive\n10. Aquarium\n\n"]}], "tstamp": 1730331263.200813} +{"question_id": "232652504704269465313999616244661111855", "answer_id": "GDSP2JkfzsUcHQPBMFyneb", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Here are 10 words that meet your criteria:\n1. Cat\n2. Dog\n3. Sun\n4. Car\n5. Cup\n6. Run\n7. Sky\n8. Joy\n9. Box\n10. Ant\n"]}], "tstamp": 1730331263.2008169} +{"question_id": "287188625903951072935762765471475284175", "answer_id": "82WzfYcgKUst6hM6o2tft2", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Sure, I can help you with that! The letters \"EBIK\" can be rearranged to form the word \"BIKE.\"\n"]}], "tstamp": 1730331263.200822} +{"question_id": "161480748540610701858922404623360627223", "answer_id": "6dR5PdLxMayPVGt2nYPdzx", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["crocodile\n"]}], "tstamp": 1730331263.2014441} +{"question_id": "85459626468998238283612722574881223450", "answer_id": "Xhb2ZTVbJfSo6Yq5zU9iMH", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["eater\n"]}], "tstamp": 1730331263.201448} +{"question_id": "191706525351403953102449248945001390595", "answer_id": "8Pz2wgkGTBcTSySrMdKtUN", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["tight\n"]}], "tstamp": 1730331263.201452} +{"question_id": "217629250243434759965340785181586787288", "answer_id": "bRJzKjD2tnULBhizxXYs2j", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The system is experiencing significant high system load and a virtual machine is undersized or the hypervisor host is overloaded, indicating potential performance issues.\n"]}], "tstamp": 1730331263.202344} +{"question_id": "78993804116905138978836976886411652996", "answer_id": "YhFDCApKBkVz7mm977fGAa", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Sudden increase in average latency and erroneous call rate staying at a high level. This is probably due to a problem in service or its downstream or on one side of the connection.\n"]}], "tstamp": 1730331263.202349} +{"question_id": "21187129699708595881971315723980585273", "answer_id": "AyQkhvgPA7VWmXN4idEUzr", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["System load is too high and virtual machine may be undersized or the hypervisor host is overloaded. The value memory.total has also changed.\n"]}], "tstamp": 1730331263.202354} +{"question_id": "222070321832259472224056798882200861588", "answer_id": "e7qp4jHAqEkwpXkp2nyTbC", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["After optimizing a query, the team successfully resolved connection issues with the service, reducing their frequency. The system has stabilized, and further monitoring will confirm its full recovery.\n"]}], "tstamp": 1730331263.20386} +{"question_id": "71867518780042338901315556259843449355", "answer_id": "Sdp7wQhUpQzCqkTZLUnPLR", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The team is checking application code changes to identify any recent modifications that could be causing the reported problems. \n"]}], "tstamp": 1730331263.203867} +{"question_id": "186626683352999270084004348076199855768", "answer_id": "j75j55d5svYaTuvih5YSBa", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["We are investigating an issue impacting our services and are awaiting further analysis.\n"]}], "tstamp": 1730331263.203874} +{"question_id": "77661143051378350859380096639114948420", "answer_id": "XVBhhoyuLqrVHv7ubSrm7M", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Sure, I'd be happy to help! Here is the list of animals from biggest to smallest, along with the middle-sized animal:\n\n1\\. Bear: Brown bears, such as the grizzly and black bears, can weigh between 300-800 pounds (135-363 kg) and stand up to 6 feet (1.83 meters) tall at the shoulder.\n2\\. Lion: Male lions can weigh between 250-450 pounds (113-204 kg) and stand up to 4.5 feet (1.37 meters) tall at the shoulder.\n3\\. Dog: Small breed dogs, such as the Chihuahua and Maltese, can weigh between 2-10 pounds (0.9-4.5 kg) and stand up to 13 inches (33 cm) tall at the shoulder. Others can be larger and average 80 - 110lbs.\n4\\. Cat: Domestic cats, such as the domestic shorthair and Siamese, can weigh between 8-10 pounds (3.6-4.5 kg) and stand up to 3 feet (0.91 meters) tall at the shoulder.\n5\\. Flea: Adult fleas can weigh between 0.02-0.05 grams (0.0007-0.0018 ounces) and are about 0.12 inches (3 mm) long.\n\nThe middle-sized animal in this list is the dog.\n"]}], "tstamp": 1730331263.205333} +{"question_id": "189888846104744865782174795075410762144", "answer_id": "Mp4Nw29cQcZ7ccWb8bWxRQ", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Sure, I'd be happy to help! Here is the list of animals from biggest to smallest, along with the middle-sized animal:\n\n1\\. Giraffe: Adult giraffes can weigh between 2,000-4,000 pounds (914-1,814 kg) and stand up to 16 feet (4.87 meters) tall at the shoulder.\n2\\. Bird: The largest bird species, such as the ostrich and emu, can weigh between 140-320 pounds (63-145 kg) and stand up to 5 feet (1.52 meters) tall at the shoulder.\n3\\. Cat: Domestic cats, such as the domestic shorthair and Siamese, can weigh between 8-10 pounds (3.6-4.5 kg) and stand up to 3 feet (0.91 meters) tall at the shoulder.\n4\\. Scorpion: Adult scorpions can weigh between 0.2-2 grams (0.007-0.07 ounces) and are about 1-6 inches (2.5-15 cm) long.\n5\\. Ladybug: Adult ladybugs can weigh between 0.02-0.05 grams (0.0007-0.0018 ounces) and are about 0.16 inches (4 mm) long.\n\nThe middle-sized animal in this list is the cat.\n"]}], "tstamp": 1730331263.205337} +{"question_id": "121995513509776861884758138059280808118", "answer_id": "QESmQiNpPus7uEX9wa2M7u", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Sure, I'd be happy to help! Here is the list of organisms from biggest to smallest, along with the middle-sized organism:\n\n1\\. Python: The longest snake species, such as the green anaconda and reticulated python, can grow up to 30 feet (9.14 meters) long.\n2\\. Zebra: Male zebras can weigh between 450-550 pounds (204-249 kg) and stand up to 5 feet 6 inches (168 cm) tall at the shoulder.\n3\\. Tiger: Tigers can weigh between 200-400 pounds (90-181 kg) and stand up to 4 feet 11 inches (150 cm) tall at the shoulder.\n4\\. Human: The average height of a human male is 5 feet 9 inches (175 cm), and the average height of a human female is 5 feet 3 inches (160 cm).\n5\\. Wombat: Wombats can weigh between 44-77 pounds (20-36 kg) and stand up to 3 feet 6 inches (107 cm) tall at the shoulder.\n\nThe middle-sized organism in this list is the Tiger, if you are going by length/height. It would be the human if going by weight.\n"]}], "tstamp": 1730331263.2053409} +{"question_id": "219992809188512294879123514028614062460", "answer_id": "NVvp7kxCoJxYE5cEpb3ZZr", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The reversed word is \"yticolev\".\n"]}], "tstamp": 1730331263.205747} +{"question_id": "208770372963369082959489876134980312158", "answer_id": "CLnujzM8gbP5RYZv5FpRdg", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The reversed number is 2435275376.\n"]}], "tstamp": 1730331263.2057521} +{"question_id": "35267098264465704953047728228342486975", "answer_id": "4AFo5Zo8kwFiXRRoEfSNfy", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The reversed string is 243tir5275376.\n"]}], "tstamp": 1730331263.205756} +{"question_id": "22189132676687610178241262070613641106", "answer_id": "EDuNhAf5aMzjincQrgk7S7", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["BANANA\n"]}], "tstamp": 1730331263.206186} +{"question_id": "313812484855916859299370830713953082228", "answer_id": "Zg4Lp6v2t9yGgsDFAmPT3q", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["LATER\n"]}], "tstamp": 1730331263.206191} +{"question_id": "4430096481961294414076987706300511107", "answer_id": "XM4ftcUkWHDxBm4h7mrsn3", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["LANGUAGE\n"]}], "tstamp": 1730331263.2061949} +{"question_id": "101615453773179268841614165828269868329", "answer_id": "7Hsc9tUA3pb8ZN6m4iytqq", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Answer\n1.He plays soccer with his friends.\n2\\. She cooks dinner for us.\n3\\. I wear comfortable shoes.\n4\\. We often go to the library.\n5\\. They eat sandwiches for breakfast.\n"]}], "tstamp": 1730331263.2070951} +{"question_id": "54440709378339472233852915336694005596", "answer_id": "g2W8QUgsR6Aa6dbvGwmhR9", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Please dispose of the garbage properly.\nThe children clapped happily as Santa Claus entered.\nThe old man had traveled a long way and was tired.\nOur cow has given us milk today.\nWhere did all these letters come from?\n"]}], "tstamp": 1730331263.2071} +{"question_id": "197163736219188071863113578585340858328", "answer_id": "WLZDFR5SxYhGja4rT9jy5x", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Answers\nThe toilet needed a thorough cleaning.\nThe teacher inspired her students to succeed.\nLearning new things every day is exciting.\nSeema is the youngest child in the class.\nRich people can buy almost anything they want.\n\n"]}], "tstamp": 1730331263.207104} +{"question_id": "54312911349778793486855842470338606300", "answer_id": "D497mGkcGSJQtEx6UmEFjA", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Here are a few rhyming words for \"cool\":\n1\\. Pool\n2\\. Fool\n3\\. Tool\n4\\. Rule\n5\\. Mule\n"]}], "tstamp": 1730331263.207566} +{"question_id": "287580053780674726253607674886288065838", "answer_id": "ax9LKL76WrzrpZjxYH9cLG", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Sure, I can help with that! Here's one rhyming word for \"pan\":\n1\\. Man\n\n"]}], "tstamp": 1730331263.2075698} +{"question_id": "156722688807824739867629660728739163344", "answer_id": "dDkTkRk4ESDXvH7oi477dX", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Here are three rhyming words for \"meet\":\n\n1\\. Street\n2\\. Neat\n3\\. Beat\n\n\n"]}], "tstamp": 1730331263.2075741} +{"question_id": "276549514571388309132827245495658651139", "answer_id": "ejQQGKNRxhE6QUJ5nUHP5u", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["- Acquisition-related charges\n- Non-operating retirement-related costs/ (income)\n- U.S. tax reform impacts\n- Kyndryl-related impacts\n"]}], "tstamp": 1730331263.214242} +{"question_id": "158741300142587848903681381168692058502", "answer_id": "HSJgoptv4KPLoa4ncQVCa5", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["- Income from continuing operations before income tax\n- Income from continue operations\n- Income from continuing operations margin\n- Income/ (loss) from discontinued operations, net of tax\n- Net income \n"]}], "tstamp": 1730331263.21425} +{"question_id": "191397906590284199538176995018135933190", "answer_id": "BDnY7ZrsGGeEayvmRn53yH", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["- 2021\n- 2022\n"]}], "tstamp": 1730331263.214257} +{"question_id": "166558449554428886722058627655821217468", "answer_id": "Lf6sDtcB7Yh8ZiVX6V3jyw", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Revenue, Profit, Cash Generation \n"]}], "tstamp": 1730331263.220691} +{"question_id": "236683433202528146576591774277469991688", "answer_id": "MhyUtTLUskdTXZLKdiva9V", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["$7,174,000,000, $8,326,000,000\n"]}], "tstamp": 1730331263.2206972} +{"question_id": "103545471824963677995355919730420965985", "answer_id": "Z4QYrgrvT5g4ZS58zPvVCu", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Acquisition-related charges, non-operating retirement-related costs/ (income), U.S. tax reform impacts, Kyndryl-related impacts. \n"]}], "tstamp": 1730331263.220702} +{"question_id": "3371116730474509402118326993539166607", "answer_id": "ExTm5xfnoSKwjvvh8UeCST", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["```\n| Non-operating adjustments (net of tax) | | | |\n| --------------------------------------------------- | ------- | ----- | ----- |\n| \u00a0\u00a0\u00a0\u00a0Acquisisition-related charges | 1,329 | 1,424 | (6.7) |\n| \u00a0\u00a0\u00a0\u00a0Non-operating retirement-related costs/(income) | 4,933\\* | 1,031 | NM |\n| \u00a0\u00a0\u00a0\u00a0U.S. tax reform impacts | (70) | 89 | NM |\n| \u00a0\u00a0\u00a0\u00a0Kyndryl-related impacts | 351 | (81) | NM |\n```\n\n"]}], "tstamp": 1730331263.227777} +{"question_id": "29373606347768801198458658534540605025", "answer_id": "TzTaEiUhbT5MHSS484Tzj3", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["```\n| For year ended December 31 | 2022 | 2021 | Yr.-to-Yr. Percent/Margin Change |\n| ----------------------------------------------------- | ------------ | ------------ | -------------------------------- |\n| Revenue | $60,530 | $57,350 | 5.5%\\*\\* |\n| Total expense and other (income) | $31,531 | $26,649 | 18.3% | \n| Weighted-average shares outstanding-assuming dilution | 912.3 | 904.6 | 0.8% |\n| Equity | $ 22,021 | $18,996 | 15.9% \n```\n\n"]}], "tstamp": 1730331263.227783} +{"question_id": "32595901518022412813368895078677464330", "answer_id": "fJXjrF63EczyiCV7vsA2v6", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["```\n| | 2022 | 2021 |\n| ----------- | -------------- | ------------- |\n| Assets | \u00a0$\u00a0\u00a0\u00a0127,243 | \u00a0$\u00a0\u00a0\u00a0132,001 |\n| Liabilities | \u00a0$\u00a0\u00a0\u00a0105,222 | \u00a0$\u00a0\u00a0\u00a0113,005 |\n| Equity | \u00a0$\u00a0\u00a0\u00a0\u00a022,021 | \u00a0$\u00a0\u00a0\u00a0\u00a018,996 |\n```\n\n"]}], "tstamp": 1730331263.2277892} +{"question_id": "202761913534005021305273408192319569173", "answer_id": "3zpLk2HPzfHjbnz4h7HewX", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Generally, assets should be equal to liabilities + equity. This is true in the table in 2021 as Liabilities ($105,222) + Equity ($22,021) is equal to Assets ($127,243) and in 2022 where the Liabilities ($113,005) + Equity ($18,996) is equal to Assets ($132,001). \n"]}], "tstamp": 1730331263.2345018} +{"question_id": "322703584480120814589247769797312278466", "answer_id": "7kUhBgVogrKHzy3rNeJvey", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["\"Income from continuing operations before income taxes\" had the greatest difference between 2021 and 2022 with a year-to-year percent/margin change of -76.1%. \n"]}], "tstamp": 1730331263.234508} +{"question_id": "304975444396586509041052817878438361784", "answer_id": "mmLzwuvqorAs4zzz3cVLR9", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["IBM is taking numerous actions to mitigate high labor and component costs by improving utilization and pricing margins in their Consulting division, making more accretive acquisitions, executing price increases across all of their product-based businesses, and deploying hedging programs to defer the impact of currency. \n"]}], "tstamp": 1730331263.2345128} +{"question_id": "127296046401623856685375886933152377487", "answer_id": "3ymndm88QjybaZttDCqS64", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["For year ended December 31:,2022\nNet income reported,\"$1,638\\*\"\n\"Income/(loss) from discontinued operations, net of tax\",(143)\nIncoming from continuing operations,\"$1,783\\*\"\nNon-operating adjustments (net of tax),\n\u00a0\u00a0Acquisition-related charges,\"1,329\"\n\u00a0\u00a0\u00a0Non-operating retirement-related costs/(income),\"4,933\\*\"\n\u00a0\u00a0\u00a0U.S. tax reform impacts,(70)\n\u00a0\u00a0\u00a0Kyndryl-related impacts,351\nOperating (non-GAAP) earnings,\"$8,326 \"\nDiluted operating (non-GAAP) earning per share,$9.13\u00a0\n"]}], "tstamp": 1730331263.241566} +{"question_id": "77832818533239894740595742162556107668", "answer_id": "Di5K5bwcgAJkTFa35RdqiW", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["For year ended December 31,2022\\*,2021,Yr.-to-Yr. Percent/Margin Change\nRevenue,\"$60,530 \",\"$57,350 \",5.5%\\*\\*\nGross profit margin,54.0%,54.9%,(.9)pts.\nTotal expense and other (income),\"$31,531 \",\" $26,649 \",18.3%\nIncome from continuing operations before income taxes,\" $1,156.00 \",\" $4,837 \",(76.1)%\nProvision for/(benefit from) income taxes from continuing operations , $(626), $124 ,NM\nIncoming from continuing operations,\" $1,783 \",\" $4,712 \",(62.2)%\nIncoming from continuing operations margin,2.9%,8.2%,(5.3)pts.\n\"Income/(loss) from discontinued operations, net of tax\", $(143),\" $1,030 \",NM\nNet income,\" $1,639 \",\" $5,743 \",(71.5)%\n"]}], "tstamp": 1730331263.2415738} +{"question_id": "259135565206321977954913499959754115269", "answer_id": "WuaXPPrR9zXbvxQGrw8KvU", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["For year ended December 31,Yr.-to-Yr. Percent/Margin Change\nRevenue,5.5%\\*\\*\nGross profit margin,(.9)pts.\nTotal expense and other (income),18.3%\nIncome from continuing operations before income taxes,(76.1)%\nProvision for/(benefit from) income taxes from continuing operations ,NM\nIncoming from continuing operations,(62.2)%\nIncoming from continuing operations margin,(5.3)pts.\n\"Income/(loss) from discontinued operations, net of tax\",NM\nNet income,(71.5)%\nEarnings per share from continuing operations-assuming dilution,(62.6)%\nConsolidated earnings per share-assuming dilution,(71.7)%\nWeighted-average shares outstanding-assuming dilution,0.8%\nAssets,(3.6)%\nLiabilities,(6.9)%\nEquity,15.9%\n"]}], "tstamp": 1730331263.241581} +{"question_id": "172647536321725590928875580972120400649", "answer_id": "UXkoR3qfrCeymSQpYSu6vt", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Here is the request bullet list with \"Landlord\" and \"Tenant\" as top-level bullet points:\n- Landlord:\n - Name: Temple CB, LLC\n - Address: 4350 Temple City Boulevard, El Monte, California 91731\n- Tenant:\n - Name: Okra Energy, Inc.\n - Address: 4350 Temple City Boulevard, El Monte, California 91731\n\n"]}], "tstamp": 1730331263.245044} +{"question_id": "272058048198044852801506329786654008467", "answer_id": "6L3j3r9Sq3QzrRr6rhu9xy", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The monthly rent after Jun 2026 is listed below: \n- June 1, 2016 - May 31, 2017: $50,000\n- June 1, 2017 - May 31, 2018: $60,000\n- June 1, 2019 - May 31, 2020: $70,000\n"]}], "tstamp": 1730331263.245049} +{"question_id": "64188986438460081856952229763997864829", "answer_id": "37ucNLjJWJBzrMHgrM6zHx", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Here is the summary of the monthly rent mentioned in the agreement. \n- The monthly rent for December 1, 2013 - May 31, 2014 is $0.\n- The monthly rent for June 1, 2014 - May 31, 2015 is $30,000.\n- The monthly rent for June 1, 2015 - May 31, 2016 is $40,000.\n- The monthly rent for June 1, 2016 - May 31, 2017 is $50,000.\n- The monthly rent for June 1, 2017 - May 31, 2018 is $60,000.\n- The monthly rent for June 1, 2019 - May 31, 2020 is $70,000.\n"]}], "tstamp": 1730331263.245054} +{"question_id": "229678340518563780964734295686353661684", "answer_id": "Hx2rGhZcjUaKQwSaoSx9LK", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The time period during which the rent is $50,000 is June 1, 2016 to May 31, 2017. \n"]}], "tstamp": 1730331263.251484} +{"question_id": "244397845723638727910219602827447539570", "answer_id": "eWNo4gTxq8ExJvSXmMksYS", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The organizations mentioned in this agreement includes: Temple CB, LLC, and Okra Energy, Inc.\n"]}], "tstamp": 1730331263.251489} +{"question_id": "314333047281388150938264085360174483524", "answer_id": "MGYsXJwErzfZBLTvYq3e4K", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The lease beginning date is December 1, 2013, and the end date is May 31, 2020. \n"]}], "tstamp": 1730331263.2514932} +{"question_id": "256127947257959728870571268188521900634", "answer_id": "ehYwGd5uH4Lw8BPMRf7mHq", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The late payment is defined as he tenant must be late on rent three times or more and each late payment must be\u00a0ten or more days past the due date.\n\n"]}], "tstamp": 1730331263.251498} +{"question_id": "168239486965086110769804819249503162907", "answer_id": "EQmzwm6ZGEM8vDdfRatExY", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["30 days. \n"]}], "tstamp": 1730331263.251503} +{"question_id": "242887182447561321257905522655660584227", "answer_id": "mKHipiEktxgC4gB6xztpjE", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The tenant remains liable for all rent and other obligations under the lease up to the date of termination.\n"]}], "tstamp": 1730331263.251507} +{"question_id": "168988362330738562715271172938631130397", "answer_id": "JuaMjzVDh8TkkuTYuU8FuU", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["\\## Parties | Party | Name | Address |\n|---|---|---|\n| Landlord | Temple CB, LLC | 4350 Temple City Boulevard, El Monte, California 91731 |\n| Tenant | Okra Energy, Inc. | 4350 Temple City Boulevard, El Monte, California 91731 |\n"]}], "tstamp": 1730331263.254966} +{"question_id": "73007727424924267800371699049549308841", "answer_id": "UB7C39vCHRamXWonfayqpq", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["| Time Period | Rent |\n|---|---|\n| December 1, 2013 - May 31, 2014 | $0 |\n| June 1, 2014 - May 31, 2015 | $30,000 |\n| June 1, 2015 - May 31, 2016 | $40,000 |\n| June 1, 2016 - May 31, 2017 | $50,000 |\n| June 1, 2017 - May 31, 2018 | $60,000 |\n| June 1, 2019 - May 31, 2020 | $70,000 |\n\n"]}], "tstamp": 1730331263.2549708} +{"question_id": "155488732950655439293375985537221767430", "answer_id": "EbbHpyWJSfTV7BeesxzNx9", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["| Period | Monthly rent quantity |\n|---|---|\n| June 1, 2016 - May 31, 2017 | $50,000 |\n| June 1, 2017 - May 31, 2018 | $60,000 |\n| June 1, 2019 - May 31, 2020 | $70,000 |\n"]}], "tstamp": 1730331263.254975} +{"question_id": "175745130039202703674828641529878645579", "answer_id": "3qAMcg2eGsCmkL7sR9ywp4", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Given that the monthly rent is $100,000 after the termination date, with the budget of one 1 million, the lease can last another 10 months. \n"]}], "tstamp": 1730331263.2582731} +{"question_id": "82616909378625224529025121839885550602", "answer_id": "FXif3WDbTq2sfC2SikoLrk", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Based on the table provided in the document, there are three time periods where the rate is less than $50,000:\n- December 1, 2013 - May 31, 2014: $0 (6 months)\n- June 1, 2014 - May 31, 2015: $30,000 (12 months)\n- June 1, 2015 - May 31, 2016: $40,000 (12 months)\nSo in total, there are 30 months where the rent is less than $50,000. \n\n"]}], "tstamp": 1730331263.258278} +{"question_id": "313828062606420990234680697555275955885", "answer_id": "C7brgDr24oQSHF4xCsuHrS", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Between May 31, 2014 and June 1, 2014, there is a price increase of $30,000, which is the steepest price change of all time. \n"]}], "tstamp": 1730331263.258282} +{"question_id": "179146985562153323128331754126690542800", "answer_id": "k8eGyciLyPPMpY4ggk592p", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Time period, Rent\n\"June 1, 2016 to May 31, 2017:\" $50000\n\"June 1, 2017 to May 31, 2018:\" $60000\n\"June 1, 2019 to May 31, 2020:\" $70000\n\n"]}], "tstamp": 1730331263.2616389} +{"question_id": "7883453001091666598159072997627818833", "answer_id": "hPuaERXzvgrqtgQEQRyfde", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Landlord, Temple CB LLC\nTenant, Okra Energy Inc.\nStart date, December 1, 2013\nEnd data, May 31, 2020\n"]}], "tstamp": 1730331263.2616441} +{"question_id": "88406853982717047504862763416357608522", "answer_id": "Wa7ykss4HCpMA4yTJPVoKW", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Time period, Rent\n\"December 1, 2013 to May 31, 2014:\", $0\n\"June 1, 2014 to May 31, 2015:\", $30000\n\"June 1, 2015 to May 31, 2016:\" $40000\n\"June 1, 2016 to May 31, 2017:\" $50000\n\"June 1, 2017 to May 31, 2018:\" $60000\n\"June 1, 2019 to May 31, 2020:\" $70000\n"]}], "tstamp": 1730331263.261648} +{"question_id": "46570474197018698211014135771568173246", "answer_id": "PL2go4EDXLKWf9xN26w6FC", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Here is a list of dates and events from the provided text:\n* 1950: Olav Aaraas was born in Fredrikstad, Norway. * 1982-1993: Olav Aaraas served as the director of Sogn Folk Museum in Norway. * 1993-2010: Olav Aaraas served as the director of Maihaugen in Norway. * 2001-present: Olav Aaraas has been the director of the Norwegian Museum of Cultural History. * 2010: Olav Aaraas was decorated with the Royal Norwegian Order of St. Olav. * 1942: The American film \"War Dogs\" was directed by S. Roy Luby. * 1943: The American one-reel World War II animated cartoon \"War Dogs\" was directed by William Hanna and Joseph Barbera. * 1967: The epic film \"War and Peace\" was released, for which V. Uvarov was nominated for an Academy Award for Best Art Direction. * 1947-1955: Jesse Edward Hobson served as the director of SRI International. * January 24, 2012: Lee Sandales was nominated for an Academy Award for his work on the film \"War Horse\". * March 24, 1911 - December 18, 2006: Joseph Roland Barbera, co-founder of Hanna-Barbera, was born and died. * 1927: Joseph Roland Barbera joined Van Beuren Studios. * 1929: Joseph Roland Barbera joined Terrytoons. * 1930: Joseph Roland Barbera moved to California and began working at Metro-Goldwyn-Mayer (MGM). * 1950: Joseph Roland Barbera co-founded Hanna-Barbera with William Hanna. * 1950: After MGM dissolved their animation department, Hanna-Barbera became the most successful television animation studio in the business. * 1967: Hanna-Barbera was sold to Taft Broadcasting for $12 million. * 1991: The studio was sold to Turner Broadcasting System. * 1996: Turner Broadcasting System was merged with Time Warner, owners of Warner Bros. * Hanna-Barbera won seven Academy Awards and eight Emmy Awards. * Brian Patrick Kennedy was born on November 5, 1961. * 2010-2019: Brian Patrick Kennedy served as the director of the Toledo Museum of Art in Ohio. * 2005-2010: Brian Patrick Kennedy served as the director of the Hood Museum of Art. * 1997-2004: Brian Patrick Kennedy served as the director of the National Gallery of Australia in Canberra. * Present: Brian Patrick Kennedy is the director of the Peabody Essex Museum.\n"]}], "tstamp": 1730331263.263802} +{"question_id": "339895209861053340017988974097778497225", "answer_id": "EzJMYm56A97eLf7AHHmz3X", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["'1. Ireland\\n2. Australia\\n3. United States\\n4. Peabody Essex Museum (in the United States)\\n5. Toledo Museum of Art in Ohio\\n6. Hood Museum of Art\\n7. National Gallery of Australia in Canberra\\n8. Fredrikstad, Norway\\n9. Sogn Folk Museum\\n10. Maihaugen\\n11. Norwegian Museum of Cultural History\\n12. Battle of France (in May 1940)\\n13. French Army\\n14. University College Dublin\\n15. Israel\\n16. Israeli Academy of Film and Television'\n"]}], "tstamp": 1730331263.264965} +{"question_id": "263331233586072114194311496404253505185", "answer_id": "EB4QXgsrZ956MTvwsQ4RGe", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["'1. Niels Hansen\\n2. Harry Piel (also known as Hubert August Piel)\\n3. Dary Holm\\n4. Elisabeth Pinajeff\\n5. Hans Junkermann\\n6. Robert A. Dietrich\\n7. Emil Hasler\\n8. Basil Rathbone\\n9. Ben Lyon\\n10. Aileen Pringle\\n11. George Gibbs\\n12. Ian Barry\\n13. Burt P. Lynwood\\n14. Sam Newfield\\n15. Olav Aaraas\\n16. Brian Patrick Kennedy\\n17. Peter Levin'\n"]}], "tstamp": 1730331263.266329} +{"question_id": "166644688768548788820053520638117399743", "answer_id": "6RcSNMMYznL5zdkw4Vj773", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Here is a list of all the hospitals referenced in this study:\n- Boston Medical Centre\n\nThis study also referenced 29 different referral hospitals in Massachusetts State, but the exact hospital names were not provided.\n"]}], "tstamp": 1730331263.272612} +{"question_id": "239993195295869993691285139988711303142", "answer_id": "hvTngqJZTEcNgrVAkR7yV8", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Here are all the variables that the were mentioned:\n- age\n- gender\n- ICH subtype\n- traumatic vs non traumatic etiology\n"]}], "tstamp": 1730331263.272619} +{"question_id": "233649569038669819719360940967084280675", "answer_id": "3iHVdEotqB7LybgYFb8izX", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Here is the performance data by Slice Thickness, taken from Table 4 in the document:\n- Slice Thickness < 1.5 mm\n - Sensitivity (95% CI): 87.50% (79.92-92.99)\n - Specificity (95% CI): 94.21% (88.44-97.64)\n- 1.5mm \u2264 Slice Thickness < 3 mm\n - Sensitivity (95% CI): 95.65% (78.05-99.89)\n - Specificity (95% CI): 100.00% (82.35-100.00)\n- Slice Thickness \u2265 3 mm\n - Sensitivity (95% CI): 90.62% (74.98-98.02)\n - Specificity (95% CI): 76.47% (58.83-89.25)\n"]}], "tstamp": 1730331263.272626} +{"question_id": "62680308787734396925620882994845674925", "answer_id": "Q7FVCGAnxUt2GRXZeenJNq", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Oxford, United Kingdom, U.S., Massachusetts\n"]}], "tstamp": 1730331263.279262} +{"question_id": "171334666228487631059576958985489300677", "answer_id": "ksCXqsftFNK7gLsytN6CiB", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["ICH, intracranial hemorrhage; NCCT, non-contrast Computed Tomography; TP, True Positives; TN, True Negatives; FP, False Positives; FN, False Negatives; ROC, receiver operating curve; IPH, Intraparenchymal Hemorrhage; IVH, Intraventricular Hemorrhage; SAH, Subarachnoid Hemorrhage; SDH, Subdural Hemorrhage\n"]}], "tstamp": 1730331263.279268} +{"question_id": "83494279020672662581197475480100653450", "answer_id": "VqSGSqiyF2nusqh9bgW65v", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Males had a sensitivity of 93.18% (85.75-97.46)\u00a0and specificity of 91.30% (83.58-96.17).\n"]}], "tstamp": 1730331263.279273} +{"question_id": "337596308285671025544428380592587653878", "answer_id": "LfwoVP38gtr2yypXxxpkWS", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["| \\*\\*Entity Name\\*\\*\u00a0\u00a0\u00a0\u00a0| \\*\\*Entity Type\\*\\* |\n|-----------------------|-----------------|\n| Brainomix\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Institution\u00a0\u00a0\u00a0|\n| Oxford\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Location\u00a0\u00a0\u00a0\u00a0|\n| United Kingdom\u00a0\u00a0\u00a0\u00a0| Location\u00a0\u00a0\u00a0\u00a0|\n| US board\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Institution\u00a0\u00a0\u00a0|\n| U.S.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Location\u00a0\u00a0\u00a0\u00a0|\n| Boston Medical Centre | Institution\u00a0\u00a0\u00a0|\n"]}], "tstamp": 1730331263.2858229} +{"question_id": "154891701021275387378643570418796269442", "answer_id": "mkab32o55aYV9yyG94BJED", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["| Age\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0| Sensitivity (95% CI)\u00a0\u00a0| Specificity (95% CI)\u00a0\u00a0|\n|---------------|-----------------------|------------------------|\n| 21 < Age < 50 | 83.33% (67.19-93.63)\u00a0| 88.73% (79.00-95.01)\u00a0\u00a0|\n| 50 \u2264 Age < 70 | 92.75% (83.89-97.61)\u00a0| 91.25% (82.80-96.41)\u00a0\u00a0|\n| Age \u2265 70\u00a0\u00a0\u00a0| 88.71% (78.11-95.34)\u00a0| 100.00% (85.18-100.00) |\n\n"]}], "tstamp": 1730331263.285829} +{"question_id": "176632790082150003971260114482566702269", "answer_id": "U2FNRrZsuSeW9NoBaDE8Tr", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["| \\*\\*Table Number\\*\\* | \\*\\*Table Description\\*\\*\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|------------------|---------------------------------------------------------------------------------------------|\n| Table 1\u00a0\u00a0\u00a0\u00a0\u00a0| Summary of the performance metrics for the subsets of scans stratified by ICH Subtype.\u00a0\u00a0\u00a0\u00a0|\n| Table 2\u00a0\u00a0\u00a0\u00a0\u00a0| Summary of the performance metrics for the subsets of scans stratified by gender.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Table 3\u00a0\u00a0\u00a0\u00a0\u00a0| Summary of the performance metrics for the subsets of scans stratified by age group.\u00a0\u00a0\u00a0\u00a0\u00a0|\n| Table 4\u00a0\u00a0\u00a0\u00a0\u00a0| Summary of the performance metrics for the subsets of scans stratified by slice thickness\u00a0\u00a0|\n| Table 5\u00a0\u00a0\u00a0\u00a0\u00a0| Summary of the performance metrics for the subsets of scans stratified by referring hospital |\n| Table 6\u00a0\u00a0\u00a0\u00a0\u00a0|\u00a0Summary of the sensitivity for the subsets of scans stratified by ICH volume\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n"]}], "tstamp": 1730331263.285834} +{"question_id": "13190131703934170923390151936927251349", "answer_id": "6y4H94aHfyXKG5ag7YF64b", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["According to this document, the acceptability criterion for the study was 80% sensitivity and specificity. The study breaks down sensitivity by ICH subtype in Table 1, where subtypes of IPH, IVH, and Multiple Types are shown to exceed 80% sensitivity, but SAH and SDH have subpar performance with 35.71% and 66.67%, respectively. Therefore it does not appear that this image analysis algorithm works well for all studied types of ICH.\n"]}], "tstamp": 1730331263.292278} +{"question_id": "192001875883867509792645995035423322628", "answer_id": "XH2HKwNY7MAH5YWXYGubwC", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["According to Table 5, Boston Medical Centre had a similar sensitivity of results, 89.38% (82.18-94.39), compared to other hospitals, 88.89% (77.37-95.81). However, it had a slightly higher specificity of results, 94.35% (88.71-97.70), compared to other hospitals, 84.00% (70.89-92.83). This difference in specificity does not appear to be significant at the 95% level as there is overlap in the confidence intervals of the two specificities.\n"]}], "tstamp": 1730331263.292286} +{"question_id": "291359785972007148714346311538552655819", "answer_id": "YqFnNjF625q6snzw7sci6c", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Slice thicknesses between 1.5mm and 3mm appear to have performed the best, with a sensitivity of 95.65% (78.05-99.89)\u00a0and specificity of 100.00% (82.35-100.00).\n"]}], "tstamp": 1730331263.2922912} +{"question_id": "287444913151913100716199558424586008573", "answer_id": "CWZdio3c7fbcwreSpUvJar", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Gender,Sensitivity (95% CI),Specificity (95% CI)\nMale,93.18% (85.75-97.46),91.30% (83.58-96.17)\nFemale,84.81% (74.97-91.90),91.46% (83.20-96.50)\n"]}], "tstamp": 1730331263.29839} +{"question_id": "247083351197015360806905978416384233925", "answer_id": "S45qSRKKP8UeX69ANovG6C", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Slice Thickness,Sensitivity (95% CI),Specificity (95% CI)\nSlice Thickness < 1.5 mm,87.50% (79.92-92.99),94.21% (88.44-97.64)\n1.5mm \u2264 Slice Thickness < 3 mm,95.65% (78.05-99.89),100.00% (82.35-100.00)\nSlice Thickness \u2265 3 mm,90.62% (74.98-98.02),76.47% (58.83-89.25)\n"]}], "tstamp": 1730331263.2983959} +{"question_id": "309271686497587448058906964691281009453", "answer_id": "H2Tntuec9QAaefyGnfUjnE", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["True Positives,False Positives,True Negatives,False Negatives\n149,15,159,18\n"]}], "tstamp": 1730331263.298401} +{"question_id": "52416929612810531569891224675802917530", "answer_id": "gEzNT2KNUAqPcSza5WaUTk", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Kanye Omari West is a renowned American rapper, singer, songwriter, record producer, entrepreneur, and fashion designer, known for his broad range of musical styles and incorporation of various genres. He first gained recognition as a producer for Roc-A-Fella Records, producing hits for Jay-Z, Ludacris, and Alicia Keys, before embarking on a successful solo career with albums like \"The College Dropout\" and \"My Beautiful Dark Twisted Fantasy.\" West is also a fashion designer, collaborating with Nike, Louis Vuitton, and Adidas, and is the founder of DONDA. Etan Boritzer is an American children\\'s literature author, best known for his universalist book \"What is God?\" and the popular \"What is?\" series, which has caused controversy among religious fundamentalists. Brian Saunders and Terence D. Robinson were male weightlifters and wrestlers, respectively, who competed for England. Walter Robinson is an African American composer known for his 1977 song \"Harriet Tubman.\" Les Richards is an Australian rules footballer who played with North Melbourne in the VFL. Pamela Jain is an Indian playback singer, and Alonso Mudarra was a Spanish Renaissance composer and vihuela player who composed the earliest surviving music for the guitar. Theodred II was a medieval Bishop of Elmham.\n"]}], "tstamp": 1730331263.301209} +{"question_id": "201095040296849433371521986221743432226", "answer_id": "RqKzRGWD6jLTwWJSzNfjaz", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["1. Etan Boritzer is an American writer of children\\'s literature, best known for his book \"What is God?\" published in 1989.\\n2. His \"What is?\" series, which includes books like \"What is Love?\", \"What is Death?\", \"What is Beautiful?\", etc., is a popular teaching guide for parents, teachers, and child-life professionals.\\n3. The series has caused controversy due to its universalist views and has been translated into 15 languages.\\n4. Boritzer was first published at the age of 13 and now lives in Venice, California, where he maintains his publishing office.\\n5. He has helped numerous other authors get published and is also a yoga teacher and an erudite speaker on \"The Teachings of the Buddha.\"\\n6. Harry S. Webb was an American film producer, director, and screenwriter who produced and directed 100 films between 1924 and 1940.\\n7. Webb and his wife, Rose Gordon, created Reliable Pictures Corporation in 1933, which produced Westerns until 1937.\\n8. Webb then started Metropolitan Pictures Corporation in 1938, which produced several films until 1940.\\n9. Ian Barry is an Australian director of film and TV.\\n10. \"Pinto Rustlers\" is a 1936 American western film directed by Harry S. Webb and starring Tom Tyler, George Walsh, and Al St. John.\\n11. Les Richards was an Australian rules footballer who played with North Melbourne in the Victorian Football League (VFL).\\n12. Brian Saunders was a male weightlifter who competed for England.\\n13. Theodred II was a medieval Bishop of Elmham, whose date of consecration is unknown, but the date of his death was between 995 and 997.\\n14. Terence D. Robinson was a male wrestler who competed for England.\\n15. Pamela Jain is an Indian playback singer, born on 16th March.\\n16. Peter Levin is an American director of film, television, and theatre.\n"]}], "tstamp": 1730331263.303218} +{"question_id": "132279763738675785809084335313852430269", "answer_id": "CBxDdzuPPGHHBbNJpovmnC", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Directors Across Borders: A Comparative Study of International Film and Museum Directors, from Luciano Salce to Brian Patrick Kennedy"]}], "tstamp": 1730331263.304203} +{"question_id": "307319685060050356725520955919296230149", "answer_id": "ECzQyAELgx67Axa6PA25dH", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["I. Hanro Smitsman\\n\\n* Born in 1967 in Breda, Netherlands\\n* Writer and director of film and television\\n* Known for \"Trilogie,\" consisting of three films with interlocking stories and characters, each filmed in a different genre:\\n\\t1. \"Cavale\" - thriller\\n\\t2. \"Un couple \u00e9patant\" - comedy\\n\\t3. \"Apr\u00e8s la vie\" - melodrama\\n* \"Trilogie\" received the Andr\u00e9 Cavens Award\\n* \"La Raison du plus faible\" was entered into the 2006 Cannes Film Festival\\n* \"One Night (38 t\u00e9moins)\" was nominated for seven Magritte Awards, winning Best Screenplay\\n* Acted in the film \"Merry Christmas\" (2005)\\n* Brother of R\u00e9my Belvaux and Bruno Belvaux\\n\\nII. Joseph Kaufman\\n\\n* American silent film actor and director (1882 \u2013 February 1, 1918)\\n* Born in Washington, D.C., died in New York City during the influenza pandemic of 1918\\n* Wife was silent film star Ethel Clayton\\n* Started as an actor, appearing on Broadway in 1903\\n* Transitioned to acting in and directing silent films\\n* Directed many short films and feature-length films\\n* Directed Pauline Frederick, Marguerite Clark, Billie Burke, and Ethel Clayton\\n* Last film, \"The Song of Songs\" (1918), starred Elsie Ferguson\\n* Actor in \"The Amazons\" (1917), a silent comedy film directed by Kaufman\\n\\nIII. Lucas Belvaux\\n\\n* Belgian actor and film director\\n* Known for \"Trilogie\" and \"Apr\u00e8s la vie\"\\n* \"Apr\u00e8s la vie\" is the final installment of \"Trilogy,\" a series of three films with interlocking stories and characters\\n* \"Trilogy\" includes:\\n\\t1. \"Cavale\" - thriller\\n\\t2. \"Un couple \u00e9patant\" - comedy\\n\\t3. \"Apr\u00e8s la vie\" - melodrama\\n* \"Trilogy\" shares common scenes and plot points, with each film having its own perspective and style\\n* \"La Raison du plus faible\" was entered into the 2006 Cannes Film Festival\\n* \"One Night (38 t\u00e9moins)\" was nominated for seven Magritte Awards, winning Best Screenplay\\n\\nIV. Brian Johnson\\n\\n* British designer and director of film and television special effects\\n\\nV. Howard Winchel Koch\\n\\n* American producer and director of film and television (April 11, 1916 \u2013 February 16, 2001)\\n\\nVI. Ian Barry\\n\\n* Australian director of film and TV\\n\\nVII. Peter Levin\\n\\n* American director of film, television, and theatre\n"]}], "tstamp": 1730331263.3065212} +{"question_id": "144105919578348809585932607258950036003", "answer_id": "4LXzFWBGaKLigJAwspcBpr", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The main takeaways from the above text are:\\n\\n1. Up Front is a 1951 comedy film directed by Alexander Hall, starring Tom Ewell and David Wayne, based on Bill Mauldin\\'s World War II characters \"Willie and Joe.\" However, Mauldin disowned the film and refused his advising fee.\\n2. Theodred II was a Bishop of Elmham in the medieval period, but the exact date of his consecration is unknown. He died between 995 and 997.\\n3. Mark Maxey is an American producer, writer, and director, best known for the documentary \"Up to Snuff\" (2019), about musician/composer W.G. Snuffy Walden.\\n4. Etan Boritzer is an American writer of children\\'s literature, best known for his book \"What is God?\" published in 1989. His \"What is?\" series is a popular teaching guide for parents, teachers, and child-life professionals, and has been translated into 15 languages. Boritzer gained national critical acclaim after \"What is God?\" was published, but the book has also caused controversy from religious fundamentalists for its universalist views.\\n5. Boritzer was first published in 1963 at the age of 13 and now lives in Venice, California, where he maintains his publishing office. He also teaches yoga and is recognized nationally as an erudite speaker on \"The Teachings of the Buddha.\"\\n6. Takagi, Uemura, and Kanazawa are represented by Up-Front Promotion.\\n7. \"A Family Affair\" is a 2001 lesbian romantic comedy directed by Helen Lesnick, who followed it up with \"Inescapable\" in 2003.\\n8. Alexander Hall was an American film director and theatre actor.\n"]}], "tstamp": 1730331263.308096} +{"question_id": "9996609669891759148819061852905674599", "answer_id": "LcnUUoZnte6fYS4Brw4eFu", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Abstract summary:\n- Synthetic data generation requires attention to the downstream user requirements to have full utility.\n- The authors introduce a post-processing technique that improves utility while preserving data privacy and quality.\n- The approach consistently improves the utility of synthetic data across multiple benchmark datasets and state-of-the-art synthetic data generation algorithms.\n"]}], "tstamp": 1730331263.311821} +{"question_id": "281014580809199924646945703084605091396", "answer_id": "BQ8sn4jyL3MWovTHsdaHKJ", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Here are the author's emails:\n- hao@ibm.com\n- shiv.sr@ibm.com\n- john.l.henning@ibm.com\n- kristjan.h.greenewald@ibm.com\n- akash.srivastava@ibm.com\n"]}], "tstamp": 1730331263.311827} +{"question_id": "137243001175003410439804752262228281673", "answer_id": "VXkFSXUMT7gekhEDEgwPSK", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Here's some recent work in the area of synthetic data generation:\n- The U.S. National Institute of Standards and Technology (NIST) has emphasized its significance by hosting a series of competitions [RTMT21, MMS21].\n- There is also significant work introducing new DP synthetic data generation mechanisms. Methods include\n - GAN-based [XLW+18, BJWW+19, JYVDS19, TWB+19]\n - Marginal-based [ZCP+17, MSM19, MMS21]\n - Workload-based [VTB+20, ABK+21, LVW21, MMSM22, VAA+22]\n"]}], "tstamp": 1730331263.3118331} +{"question_id": "149145430129109704764226017070049879526", "answer_id": "4j3XMA9N5ZCKCEjYF6nsK9", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["This paper introduces a post-processing technique to improve the utility of the synthetic data with respect to measures selected by the end user, while preserving strong privacy guarantees and dataset quality. This advancement improves the utility of data for downstream use.\n"]}], "tstamp": 1730331263.315397} +{"question_id": "92434087613919199160038524320138085442", "answer_id": "grFyTUBPF5X5ScXiCceCpd", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The authors resampled from the synthetic data to filter out samples that do not meet the selected utility measures. They used an efficient stochastic first-order algorithm to find optimal resampling weights.\n"]}], "tstamp": 1730331263.315402} +{"question_id": "329410939538440337641053457179341173384", "answer_id": "RwtTPKNu5f4mz2Wxg7VUYv", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Through comprehensive numerical experiments, the authors demonstrated that the approach they outlined improves the utility of synthetic data across multiple benchmark datasets and state-of-the-art synthetic data generation algorithms.\n"]}], "tstamp": 1730331263.315407} +{"question_id": "216297385644928817206333275875023284164", "answer_id": "2rGMM5bdfyYvp9p68eVGed", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["```\n| Author | Affiliation |\n| ---------------------- | --------------------- |\n| Hao Wang | MIT-IBM Watson AI Lab |\n| Shivchander Sudalairaj | MIT-IBM Watson AI Lab |\n| John Henning | MIT-IBM Watson AI Lab |\n| Kristjan Greenewald | MIT-IBM Watson AI Lab |\n| Akash Srivastava | MIT-IBM Watson AI Lab |\n```\n"]}], "tstamp": 1730331263.319187} +{"question_id": "93302405522548825168795297289045637921", "answer_id": "8BTZK6DLCrou7ovDz6nKdv", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["```\n| Location | Citations |\n| -------------------------- | ------------------------------------ |\n| US Census | [Abo18] |\n| Various Large Corporations | [App17, Fac20, RE19, HBMAL19, IBM23] |\n```\n"]}], "tstamp": 1730331263.3191922} +{"question_id": "130769870589161654695899255277827228849", "answer_id": "aunoqEikTKtaM8bacEpvKY", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["```\n| Technique | Citation |\n| -------------- | --------------------------------------- |\n| GAN-based | [XLW+18, BJWW+19, JYVDS19, TWB+19] |\n| Marginal-based | [ZCP+17, MSM19, MMS21] |\n| Workload-based | [VTB+20, ABK+21, LVW21, MMSM22, VAA+22] |\n```\n\n"]}], "tstamp": 1730331263.319198} +{"question_id": "206353909755124922772338273890504728318", "answer_id": "e2ipQaRTU3sPEG2ETkKvKE", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["This abstract has several parts. First, the authors describe the problems with existing approaches. They then outline why it is a problem. The next step is to describe the novel approach the paper takes. The authors describe their results and their implications.\n"]}], "tstamp": 1730331263.322741} +{"question_id": "334743913775917281476168325726955815409", "answer_id": "A9M8vWtcguzNrSC7i2DHaA", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Synthetic data generation, post-processing techniques, data privacy\nThe primary topic of this paper is about synthetic data generation. The novel approach is around using a novel post-processing technique for the synthetic data generation. The benefit of this approach is to maintain data privacy while maintaining utility.\n"]}], "tstamp": 1730331263.322747} +{"question_id": "154346823130677374317780084435178670580", "answer_id": "XKZTQhLYdHDvNrT5KUygi3", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["People interested in synthetic data generation because of privacy concerns. \nThe authors describe interested parties as broad as the US Census and large corporations because those groups have interest in protecting data privacy. \n"]}], "tstamp": 1730331263.322752} +{"question_id": "79901913399679660614872524204723845770", "answer_id": "8Zv7NNoMQiiwGSXA33n3xc", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Author,Affiliation,Email\nHao Wang,MIT-IBM Watson AI Lab,hao@ibm.com\nShivchander Sudalairaj,MIT-IBM Watson AI Lab,shiv.sr@ibm.com\nJohn Henning,MIT-IBM Watson AI Lab,john.l.henning@ibm.com\nKristjan Greenewald,MIT-IBM Watson AI Lab,kristjan.h.greenewald@ibm.com\nAkash Srivastava,MIT-IBM Watson AI Lab,akash.srivastava@ibm.com\n"]}], "tstamp": 1730331263.326472} +{"question_id": "7884975629872746087812279157971190540", "answer_id": "ZbPNhSxFE9Pb7FsNQ2bGbG", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["GAN-based,XLW+18,BJWW+19,JYVDS19,TWB+19,\nMarginal-based,ZCP+17,MSM19,MMS21,,\nWorkload-based,VTB+20,ABK+21,LVW21,MMSM22,VAA+22\n"]}], "tstamp": 1730331263.326478} +{"question_id": "230860748078397119410681161275396330891", "answer_id": "RsUuQBbZzurtXoHpSjWoGb", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Type, Number of citations\nGAN-based,4\nMarginal-based,3\nWorkload-based,5\n"]}], "tstamp": 1730331263.326483} +{"question_id": "45105405514356068342287134348038050391", "answer_id": "kevPeMCQYAF4L7wf5LrM5m", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Datasets used for this paper:\n- Adult\n- Mushroom\n- Shopper\n- Bank\n- Home-credit\n"]}], "tstamp": 1730331263.334683} +{"question_id": "69294882860109184745270748007059173094", "answer_id": "LRU2ArNE25kTnEBfDN3GbJ", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["DP Mechanism:\n- AIM\n- MST\n- DPCTGAN\n- PATECTGAN\n- GEM\n"]}], "tstamp": 1730331263.3346891} +{"question_id": "112721156388486506495179013623544578065", "answer_id": "UvWBg858Cy45VzUh3MyYUC", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Metrics:\n- Utility improvement\n- F1 score (w/o post processing)\n- F1 score (w/ post processing)\n- JS distance (w/o post processing)\n- JS distance (w/ post-processing)\n- Inverse KL (w/o post-processing)\n- Inverse KL (w/ post-processing)\n"]}], "tstamp": 1730331263.334694} +{"question_id": "23758017158750433548740716559010601950", "answer_id": "Uv2qKyz973mUN7Edotfc2G", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Yes, performance was the same or improved across all datasets and DP mechanisms. \n"]}], "tstamp": 1730331263.3428211} +{"question_id": "8282478500022356825634852654128220007", "answer_id": "aj4Taqvfg8a8iiYLj83Azd", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Performance was approximately the same for the synthetic data vs real data based on the F1 scores.\n"]}], "tstamp": 1730331263.342828} +{"question_id": "26239601712644162644444465469609811689", "answer_id": "SdwWEYUC8UQ5KcBV5BbDyi", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["MST was the highest utility score with 0.32.\n"]}], "tstamp": 1730331263.3428328} +{"question_id": "45917352980180410017922418202197457227", "answer_id": "Z6fyBKHS3C53vfLMDWA8bY", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The Mushroom dataset resulted in the highest F1 score (w/ post processing). The range of F1 scores for the Mushroom dataset ranged from 0.68 to 0.93. The Adult dataset ranged from 0.33 to 0.61. The shopper dataset ranged from 0.3 to 0.48 and the Bank dataset ranged from 0.23 to 0.46. Additionally, the F1 scores for the real data were the highest for the Mushroom dataset. \n"]}], "tstamp": 1730331263.351132} +{"question_id": "168920137566501678050484792868029527576", "answer_id": "F4aLxBK25uXLHnnYh3QsDf", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The highest average utility was for the DPCTGAN approach with 0.515 utility improvement. The average utility improvement was 0.1325 for the AIM approach. The average improvement for MST was 0.405. The average for DPCTGAN was 0.515.The PATECTGAN mechanism was 0.4475.\n"]}], "tstamp": 1730331263.351138} +{"question_id": "269100815912960317053446066237713565838", "answer_id": "N53Jkt6GPRTbrN8Fb83DSh", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The Utility improvement is higher. We first took the difference between F1 scores subtracting the F1 for w/post processing minus F1 score with post processing. This difference ranged from 0 to 0.26 with an average difference of 0.058125. The difference in utility improvement ragned from 0.1 to 0.81 with an average of 0.375.\n"]}], "tstamp": 1730331263.351143} +{"question_id": "206469191930851162031349026753499177208", "answer_id": "NtdH29kEaXhiDCAjeajKnW", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": [",Adult,Mushroom,Shopper,Bank\nF1,0.61,0.95,0.54,0.47\n"]}], "tstamp": 1730331263.3593879} +{"question_id": "289674108930322995857811321290867913592", "answer_id": "KSarh3SsVsQXmV4FErNqDu", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Dataset (DP mechanism),F1 score (w/post-proc)\nAdult (AIM),0.61 \u00b10.0\nMushroom (AIM),0.93 \u00b10.0\nShopper (AIM),0.48 \u00b10.02\nBank (AIM),0.46 \u00b10.01\n"]}], "tstamp": 1730331263.3593931} +{"question_id": "239729390783402182517747229067733741861", "answer_id": "57ARbuUkufSj3AKX7Mc3Er", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["DP Mechanism,Utility Improvement\nDPCTGAN,0.81 \u00b10.09\nPATECTGAN,0.6 \u00b10.04\nMST,0.22 \u00b10.02\nAIM,0.13 \u00b10.03\n"]}], "tstamp": 1730331263.3593988} +{"question_id": "148750204803643997458491252562059189094", "answer_id": "4Vyh2iXFJP26ryMUwYj4qZ", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["- Leverages a more efficient approach to generating private synthetic data than existing methods that do so by scratch;\n- Is highly scalable and only requires solving a convex program whose number of variables is equal to the number of specified utility measures;\n- Is evaluated on more stringent and realistic test of training various downstream ML models on the synthetic data and measuring their performance on real test data.\n"]}], "tstamp": 1730331263.369536} +{"question_id": "180135244477843915615135967686238026414", "answer_id": "PH8PKKs6opKhFAqZRiEdyd", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["- Extended Algorithm 1 in [LVS + 21] by formalizing the preservation of (noisy) utility measures as a constrained optimization problem, rather than minimizing the corresponding Lagrangian function.\n- Extended Algorithm 4 in [LVW21] by allowing any non-negative violation tolerance (i.e., any \u03b3 \u2265 0 in (3b)) compared with \u03b3 = 0 in [LVW21]\n"]}], "tstamp": 1730331263.3695412} +{"question_id": "140838421922418209889607826686014093612", "answer_id": "2RagzkpztWzyCd6ZMkTzrG", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["- HLM12\n- BLR13\n- GAH + 14\n- CXZX15\n- BSG17\n- AZK + 19\n- UV20\n- GMHI20\n- TMH + 21\n- VAA + 22\n- BSV22\n- VTB + 20\n- ABK + 21\n- MMSM22\n- LVW21\n- LVS + 21\n- NWD20\n- TMH + 21\n- DR14\n"]}], "tstamp": 1730331263.369546} +{"question_id": "186374013887446797753042798545559700316", "answer_id": "eZsPoYY8Ra6C3r8tEEmsT6", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["- **Synthetic Dataset Generation Methods:**\n - AIM [MMSM22]\n - MST [MMS21]\n - DPCTGAN. [RLP + 20]\n - PATECTGAN [RLP + 20]\n - GEM [LVW21]\n\n"]}], "tstamp": 1730331263.3695512} +{"question_id": "61032609450982071849422395957616920470", "answer_id": "3SgMJNoUb52B26QbjeaYnJ", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["- \u03b3,1e-5\n- batch\\_size (for UCI),256\n- batch\\_size (for home-credit),4096\n- epochs,200\n- experiment runs, 5\n"]}], "tstamp": 1730331263.369558} +{"question_id": "11697594796626755231696765585470243870", "answer_id": "VY2VVMho3u4GbM4qXY3q9Q", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["- **Average Jensen-Shannon Distance:**\u00a0Measures the average Jensen-Shannon Distance between marginal distributions, ranging from 0 (identical) to 1 (totally different).\n- **Average Inverse KL-Divergence:**\u00a0Measures the average Inverse KL-Divergence between marginal distributions, ranging from 0 (totally different) to 1 (identical).\n\n"]}], "tstamp": 1730331263.3695638} +{"question_id": "245310128874741708696509667177113955227", "answer_id": "JAmbDqbJdPNRDjvWY4jNLk", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["post-processing immunity and composition rule\n"]}], "tstamp": 1730331263.379158} +{"question_id": "250220594764218393275708766547192249213", "answer_id": "HggstYQiH4XGHkytwj4ced", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["HLM12, BLR13, GAH + 14, CXZX15, BSG17, AZK + 19, UV20, GMHI20, TMH + 21, VAA + 22, and BSV22.\n"]}], "tstamp": 1730331263.3791628} +{"question_id": "269112424790249293436830484762147517591", "answer_id": "bH3ttkDQ69CMNYZGMSbTJc", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Some existing work may not scale well to large datasets as they either require solving an integer program multiple times [VTB + 20] or need to solve a large-scale optimization problem [ABK + 21]\n"]}], "tstamp": 1730331263.379168} +{"question_id": "293053408778727710441795581746432326752", "answer_id": "2taEPo84C5xZErMGZCxGhD", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Utility Improvement, Predictive Performance on real data, Distance Metrics.\n"]}], "tstamp": 1730331263.379175} +{"question_id": "34497557727086378523096627629463694542", "answer_id": "L6dU2fNiL34UpetyDqUaAx", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["There are two metrics: Average Jensen-Shannon Distance and Average Inverse KL-Divergence. \n"]}], "tstamp": 1730331263.37918} +{"question_id": "61768416089125380434259677824528298857", "answer_id": "DmFtaVQKJhwHsGFZhgcwN2", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["[MMS21].\n"]}], "tstamp": 1730331263.379186} +{"question_id": "29411256457558837535538901011368774083", "answer_id": "3nBzUSiYbkV4wSA3aYjUo6", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["\\### 1.1 Related Work\n\nDP synthetic data generation mechanisms. Generating synthetic data using DP mechanisms is an active area of research [see e.g., HLM12, BLR13, GAH + 14, CXZX15, BSG17, AZK + 19, UV20, GMHI20, TMH + 21, VAA + 22, BSV22]. Along this line of work, the closest ones to ours are workload-aware methods [VTB + 20, ABK + 21, MMSM22, LVW21], which aim to ensure that the synthetic data perform well on a collection of queries. Our work differs in three key features. First, all existing approaches focus on generating private synthetic data from scratch, while we aim to post-process synthetic data to downweight samples that do not accurately represent the real data under the specified utility measures. As a result, our approach is more efficient as it does not need to fit a graphical model [MMSM22] or a neural network [LVW21] to represent the data distribution, and it is more extendable, allowing a single synthetic dataset to be quickly post-processed multiple times for different sets of utility measures as needed by downstream users. Second, some existing work may not scale well to large datasets as they either require solving an integer program multiple times [VTB + 20] or need to solve a large-scale optimization problem [ABK + 21]. In contrast, our approach is highly scalable, as it only requires solving a convex program whose number of variables is equal to the number of specified utility measures. Third, existing work often evaluates the quality of the synthetic data by how well it preserves key statistics (e.g., 3-way marginals) of the real data. In contrast, in our experiments, we evaluate our approach on the more stringent and realistic test of training various downstream ML models on the synthetic data and measuring their performance on real test data. The experimental results demonstrate that our approach can enhance the utility of synthetic data on selected measures without compromising their downstream quality.\n\nPublic data assisted methods/Post-processing methods. Our work introduces a post-processing procedure for improving the utility of a given synthetic dataset based on selected measures. As a special case, our approach can be applied to post-process publicly available datasets. In this regard, this work is related to public-data-assisted methods [see e.g., LVS + 21, LVW21], which leverage public data for saving privacy budgets. We extend Algorithm 1 in [LVS + 21] by formalizing the preservation of (noisy) utility measures as a constrained optimization problem, rather than minimizing the corresponding Lagrangian function. We further establish strong duality and propose a stochastic first-order method for solving this constrained optimization efficiently. We extend Algorithm 4 in [LVW21] by allowing any non-negative violation tolerance (i.e., any \u03b3 \u2265 0 in (3b)) compared with \u03b3 = 0 in [LVW21]. This extension offers increased flexibility, as users can now select various values of \u03b3 to navigate the trade-off between minimizing the distortion of the synthetic data and enhancing their utility on selected measures. Moreover, our experiments show that setting \u03b3 to be a small positive number (e.g., \u03b3 = 1 e-5) consistently outperforms when \u03b3 = 0. Finally, our work is related with [NWD20], which proposes to post-process outputs from differentially private GAN-based models for improving the quality of the generated synthetic data. However, their method is tailored to GAN-based privacy mechanisms while our approach is model-agnostic. This versatility is crucial, given that marginal-based and workload-based mechanisms often yield higher quality synthetic tabular data, as evidenced by benchmark experiments in [TMH + 21]. Our experiments indicate that our method consistently improves the utility of synthetic data produced by all kinds of privacy mechanisms, even when the initial synthetic data are of high quality.\n\n\\### 2 Preliminaries and Problem Formulation\n\nIn this section, we review differential privacy and provide an overview of our problem setup.\n\n\\### 2.1 Differential Privacy\n\nWe first recall the definition of differential privacy (DP) [DR14].\n\nDefinition 1. $^{A randomized mechanism}$M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-differential privacy, if for any $^{adjacent datasets}$D $^{and}$D ', which only differ in one individual's record, and all possible outputs $^{from the mechanism}$O \u2286 R, we have\n\nDP has two important properties: post-processing immunity and composition rule. Specifically, if M : X n \u2192 R satisfies (\u03f5 \u21aa \u03b4)-DP and g : R \u2192 R ' is any randomized (or deterministic) function, then\n\n"]}], "tstamp": 1730331263.390653} +{"question_id": "4085903994725864634496471302763152512", "answer_id": "hYtVM733BPxSAQQLBejpoR", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["| Reference | Count |\n| --------- | ----- |\n| HLM12\u00a0\u00a0| 1\u00a0\u00a0|\n| BLR13\u00a0\u00a0| 1\u00a0\u00a0|\n| GAH + 14\u00a0| 1\u00a0\u00a0|\n| CXZX15\u00a0\u00a0| 1\u00a0\u00a0|\n| BSG17\u00a0\u00a0| 1\u00a0\u00a0|\n| AZK + 19\u00a0| 1\u00a0\u00a0|\n| UV20\u00a0\u00a0\u00a0| 1\u00a0\u00a0|\n| GMHI20\u00a0\u00a0| 1\u00a0\u00a0|\n| TMH + 21\u00a0| 1\u00a0\u00a0|\n| VAA + 22\u00a0| 1\u00a0\u00a0|\n| BSV22\u00a0\u00a0| 1\u00a0\u00a0|\n| VTB + 20\u00a0| 2\u00a0\u00a0|\n| ABK + 21\u00a0| 2\u00a0\u00a0|\n| MMSM22\u00a0\u00a0| 2\u00a0\u00a0|\n| LVW21\u00a0\u00a0| 5\u00a0\u00a0|\n| LVS + 21\u00a0| 2\u00a0\u00a0|\n| NWD20\u00a0\u00a0| 1\u00a0\u00a0|\n| TMH + 21\u00a0| 1\u00a0\u00a0|\n| DR14\u00a0\u00a0\u00a0| 1\u00a0\u00a0|\n"]}], "tstamp": 1730331263.39066} +{"question_id": "161705532503286099326719790973297265449", "answer_id": "6PavgDABqfFmPHoUiDsagE", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["```\n| Section | Title |\n| ------- | ------------------------------------- |\n| 1.1 | Related Work |\n| 2 | Preliminaries and Problem Formulation |\n| 2.1 | Differential Privacy |\n```\n\n"]}], "tstamp": 1730331263.390665} +{"question_id": "120068030579810821617095273348887198444", "answer_id": "BNZa83vChNNVvmi9CMRwp9", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["| Method | Citation |\n|---|---|\n| AIM | [MMSM22] |\n| MST | [MMS21] |\n| DPCTGAN | [RLP + 20] |\n| PATECTGAN | [RLP + 20] |\n| GEM | [LVW21] |\n"]}], "tstamp": 1730331263.390671} +{"question_id": "204124677646185513364602230207442542631", "answer_id": "hkmNRjDgjPbNDqSAEMtQZH", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["| Parameter | Value |\n|---|---|\n| \u03b3 | 1e-5 |\n| Batch size (UCI) | 256 |\n| Batch size (home-credit) | 4096 |\n| Epochs | 200 |\n| Experiment repetitions | 5 |\n"]}], "tstamp": 1730331263.390676} +{"question_id": "246449486130822311667047153258363829407", "answer_id": "fLwew7kKik4MBEsBfFTGi3", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["| Metric | Description | Range |\n|---|---|---|\n| Average Jensen-Shannon Distance | Measures similarity between marginal distributions | 0 (identical) to 1 (totally different) |\n| Average Inverse KL-Divergence | Measures similarity between marginal distributions | 0 (totally different) to 1 (identical) |\n"]}], "tstamp": 1730331263.390681} +{"question_id": "313615675720989086988003939047226315611", "answer_id": "E678hHbLwR2eUTEZSJDnCb", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The approach described in the paper has three key advantages compared to related work in this space. First, the underlying generation mechanism is efficient and extensible as it leverages a single synthetic dataset that can be quickly post-processed for multiple different needs. Second, it is highly scalable and can work well on large datasets. Third, the quality of the generated synthetic data is evaluated on more stringent and realistic evaluation data than existing methods.\n"]}], "tstamp": 1730331263.39588} +{"question_id": "150743702925997915544717351663717742589", "answer_id": "X2Ey4pULa6UbojcihLNnX8", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The approach described in this paper allows existing synthetic datasets to be optimized for different downtream use cases based on provided user measures.\n"]}], "tstamp": 1730331263.3958862} +{"question_id": "15618686968943051841018945770687905617", "answer_id": "YBddLZrmHFvdSdQu8RrALe", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["This paper describes a technique to create a lot of data to support different use cases while making sure to preserve the anonymity of the individual represented in each data point.\n"]}], "tstamp": 1730331263.395891} +{"question_id": "154526838047430053121819025349513685537", "answer_id": "Li6a5wgDyWVsHeGvPckYmm", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["DP synthetic data generation mechanisms,Public data assisted methods/Post-processing methods\n\"Generating synthetic data using DP mechanisms is an active area of research [see e.g., HLM12, BLR13, GAH + 14, CXZX15, BSG17, AZK + 19, UV20, GMHI20, TMH + 21, VAA + 22, BSV22]. Along this line of work, the closest ones to ours are workload-aware methods [VTB + 20, ABK + 21, MMSM22, LVW21], which aim to ensure that the synthetic data perform well on a collection of queries. Our work differs in three key features. First, all existing approaches focus on generating private synthetic data from scratch, while we aim to post-process synthetic data to downweight samples that do not accurately represent the real data under the specified utility measures. As a result, our approach is more efficient as it does not need to fit a graphical model [MMSM22] or a neural network [LVW21] to represent the data distribution, and it is more extendable, allowing a single synthetic dataset to be quickly post-processed multiple times for different sets of utility measures as needed by downstream users. Second, some existing work may not scale well to large datasets as they either require solving an integer program multiple times [VTB + 20] or need to solve a large-scale optimization problem [ABK + 21]. In contrast, our approach is highly scalable, as it only requires solving a convex program whose number of variables is equal to the number of specified utility measures. Third, existing work often evaluates the quality of the synthetic data by how well it preserves key statistics (e.g., 3-way marginals) of the real data. In contrast, in our experiments, we evaluate our approach on the more stringent and realistic test of training various downstream ML models on the synthetic data and measuring their performance on real test data. The experimental results demonstrate that our approach can enhance the utility of synthetic data on selected measures without compromising their downstream quality.\",\"Our work introduces a post-processing procedure for improving the utility of a given synthetic dataset based on selected measures. As a special case, our approach can be applied to post-process publicly available datasets. In this regard, this work is related to public-data-assisted methods [see e.g., LVS + 21, LVW21], which leverage public data for saving privacy budgets. We extend Algorithm 1 in [LVS + 21] by formalizing the preservation of (noisy) utility measures as a constrained optimization problem, rather than minimizing the corresponding Lagrangian function. We further establish strong duality and propose a stochastic first-order method for solving this constrained optimization efficiently. We extend Algorithm 4 in [LVW21] by allowing any non-negative violation tolerance (i.e., any \u03b3 \u2265 0 in (3b)) compared with \u03b3 = 0 in [LVW21]. This extension offers increased flexibility, as users can now select various values of \u03b3 to navigate the trade-off between minimizing the distortion of the synthetic data and enhancing their utility on selected measures. Moreover, our experiments show that setting \u03b3 to be a small positive number (e.g., \u03b3 = 1 e-5) consistently outperforms when \u03b3 = 0. Finally, our work is related with [NWD20], which proposes to post-process outputs from differentially private GAN-based models for improving the quality of the generated synthetic data. However, their method is tailored to GAN-based privacy mechanisms while our approach is model-agnostic. This versatility is crucial, given that marginal-based and workload-based mechanisms often yield higher quality synthetic tabular data, as evidenced by benchmark experiments in [TMH + 21]. Our experiments indicate that our method consistently improves the utility of synthetic data produced by all kinds of privacy mechanisms, even when the initial synthetic data are of high quality.\"\n"]}], "tstamp": 1730331263.406823} +{"question_id": "166479164740018125591837301081795643678", "answer_id": "4BXLcJXwWVrj5MhrnapArx", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Reference,Count\nHLM12,1\nBLR13,1\nGAH + 14,1\nCXZX15,1\nBSG17,1\nAZK + 19,1\nUV20,1\nGMHI20,1\nTMH + 21,1\nVAA + 22,1\nBSV22,1\nVTB + 20,2\nABK + 21,2\nMMSM22,2\nLVW21,5\nLVS + 21,2\nNWD20,1\nTMH + 21,1\nDR14,1\n"]}], "tstamp": 1730331263.406829} +{"question_id": "98084228788201546249513621616736812041", "answer_id": "nMGmMb7vZzBvm2cJesxDJ6", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Header,\n1.1 Related Work,\n2 Preliminaries and Problem Formulation,\n2.1 Differential Privacy,\n"]}], "tstamp": 1730331263.406834} +{"question_id": "243305619819519148576364159422067625882", "answer_id": "A95JjPTjrqWpjddPeCh67S", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Method,Citation\nAIM,[MMSM22]\nMST,[MMS21]\nDPCTGAN,[RLP + 20]\nPATECTGAN,[RLP + 20]\nGEM,[LVW21]\n"]}], "tstamp": 1730331263.406842} +{"question_id": "338374922544435862595759714620861353438", "answer_id": "REAoMjXJUogj7AYvjtyDhE", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Parameter,Value\n\u03b3,1e-5\nbatch\\_size (for UCI),256\nbatch\\_size (for home-credit),4096\nepochs,200\nexperiment runs, 5\n"]}], "tstamp": 1730331263.406849} +{"question_id": "218133134328734882180240513545618804899", "answer_id": "8jZaDMwBU6Smnvg8d5fdsV", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Metric,Description,Range\nAverage Jensen-Shannon Distance,Measures similarity between marginal distributions,0 (identical) to 1 (totally different)\nAverage Inverse KL-Divergence,Measures similarity between marginal distributions,0 (totally different) to 1 (identical)\n"]}], "tstamp": 1730331263.406854} +{"question_id": "146670831529864678011836912794893908248", "answer_id": "V7Mh3wth2sJ4RZPZtqis47", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["- baja fish\n- spiced chicken verde\n- chile-lime shrimp\n- pork belly\n- spiced chicken verde bowl\n"]}], "tstamp": 1730331263.408055} +{"question_id": "213807923348757890610310231722493114673", "answer_id": "boTFuuDL7M8gznZMV9NWcY", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["- jalapeno hot sauce \u2013 to-go\n- pineapple habanero hot sauce \u2013 to-go\n- roja hot sauce \u2013 to-go\n- jalapeno hot sauce \u2013 to-go\n"]}], "tstamp": 1730331263.4080598} +{"question_id": "16693036226111004778508616627348466836", "answer_id": "9FXAowp592AaDSSguxQPd7", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["- baja fish\n- ahi tuna bowl\n"]}], "tstamp": 1730331263.4080682} +{"question_id": "251082885996068015526693324125404679824", "answer_id": "CsexohxpqHhqXys2DV2BwF", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Rameswar ordered a spiced chicken bowl with shrimp as an extra protein added.\n"]}], "tstamp": 1730331263.4092329} +{"question_id": "197247371467146327167951672118310282683", "answer_id": "fV8xeehZ9vXBqHiVz6S4S2", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Yes, there are two salads: a sm kale caesar and a large kale caesar. \n"]}], "tstamp": 1730331263.409238} +{"question_id": "55038013008238031424616763056054218586", "answer_id": "dKnQPqsf22SKUpvJigfDyd", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["In this lunch receipt there are 11 people listed: Luke (You), Ruchir, David, Abhishek, Kate, Nathan, Kai, Aldo, Hui, Shiv, Rameswar.\n"]}], "tstamp": 1730331263.4092429} +{"question_id": "127993726025538639168251850653774443421", "answer_id": "jh5eEboQF459dimj4YMsBW", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["|Item|Add-ons\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|Quantity|Price |\n|----|------------------------------------|--------|------|\n|baja fish|add bibb lettuce (4 pieces) $1.00\u00a0\u00a0|1\u00a0\u00a0\u00a0\u00a0|$21.29|\n|jalapeno hot sauce \u2013 to-go|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|5\u00a0\u00a0\u00a0\u00a0|$0.00 |\n|pineapple habanero hot sauce \u2013 to-go|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|10\u00a0\u00a0\u00a0|$0.00 |\n|roja hot sauce \u2013 to-go|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|10\u00a0\u00a0\u00a0|$0.00 |\n|jalapeno hot sauce \u2013 to-go|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|5\u00a0\u00a0\u00a0\u00a0|$0.00 |\n|sm kale caesar|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|1\u00a0\u00a0\u00a0\u00a0|$3.50 |\n|salsa roja + chips|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|1\u00a0\u00a0\u00a0\u00a0|$4.35 |\n|spiced chicken verde|\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|1\u00a0\u00a0\u00a0\u00a0|$18.69|\n\n"]}], "tstamp": 1730331263.410688} +{"question_id": "17908430897638040828533613086868331381", "answer_id": "4kkzhyo7rZZzaHs9nSbHgt", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["|Total|$289.71\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|-----|------------------------------------|\n|Subtotal|$239.99\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|Service Fee|$14.12\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|Tax\u00a0|$16.05\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|Delivery Fee|$0.49\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|Tip\u00a0|$27.06\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|Special Offer|-$8.00\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n"]}], "tstamp": 1730331263.4106932} +{"question_id": "184479824412146332897153271483321600452", "answer_id": "BH4tjpUCFPBnYhRMXHzRx6", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["|Bowl|Bowl price\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|----|------------------------------------|\n|mushroom bowl|$14.10\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|roasted duck bowl|$14.29\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|ahi tuna bowl|$13.75\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n|spiced chicken verde bowl|$19.89\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|\n\n"]}], "tstamp": 1730331263.410697} +{"question_id": "3179847060117764042081186166574333222", "answer_id": "KcBiWfiPCvSZt5mnafHx9L", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Luke (You) spent the most money, with a total of $47.83.\n"]}], "tstamp": 1730331263.4117818} +{"question_id": "176007469896057207329061492279918446084", "answer_id": "eZKMBj4k7gbSKbocpXXE5V", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Given the dishes that contain any type of meat, we can conclude that Luke (You), David, Abhishek, Kate, Nathan, Kai, Aldo, Shiv, and Rameswar are not vegetarian. Ruchir and Hui are the only people who consumed vegetarian dishes.\n"]}], "tstamp": 1730331263.411787} +{"question_id": "188243911849133053297565566837728271290", "answer_id": "5DyitJFZExbcHNHbA56UX8", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["To pay less than $280 in total you should tip 6.41%, considering that you are getting $8.00 of discount from a special offer.\n"]}], "tstamp": 1730331263.4117918} +{"question_id": "303863723574265323416212703513558641790", "answer_id": "n65RFxRLnEhZpJxsjf8yck", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Quantity,Item consumed,Price\n1,baja fish,$21.29\n5,jalapeno hot sauce \u2013 to-go,$0.00\n10,pineapple habanero hot sauce \u2013 to-go,$0.00\n10,roja hot sauce \u2013 to-go,$0.00\n5,jalapeno hot sauce \u2013 to-go,$0.00\n1,sm kale caesar,$3.50\n1,salsa roja + chips,$4.35\n1,spiced chicken verde,$18.69\n1,mushroom bowl,$14.10\n1,seared chorizo,$15.49\n1,diet coke,$2.75\n1,baja fish,$20.29\n1,roasted duck bowl,$14.29\n1,ahi tuna bowl,$13.75\n1,chile-lime shrimp,$20.29\n1,guacamole + chips,$12.59\n1,pork belly,$20.29\n1,plantains,$6.19\n1,lg kale caesar,$7.00\n1,tajin pineapple,$3.95\n1,chile-lime shrimp,$21.29\n1,spiced chicken verde bowl,$19.89\n"]}], "tstamp": 1730331263.413004} +{"question_id": "16870015487432810538810674987335310258", "answer_id": "eS8uHARp6G2fEmukDih44N", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Credit card,Date,Payment\nVisa \u00b7\u00b7\u00b7\u00b76451,2/23/24 2:06 PM,$262.65\nVisa \u00b7\u00b7\u00b7\u00b76451,2/23/24 3:06 PM,$27.06\n"]}], "tstamp": 1730331263.413009} +{"question_id": "321786636095719006578973927679375108326", "answer_id": "3iZpgws9xPTLEVYNvgcp4y", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Credit card,Percentage of payment\nVisa \u00b7\u00b7\u00b7\u00b76451,90.66%\nVisa \u00b7\u00b7\u00b7\u00b76451,9.34%\n"]}], "tstamp": 1730331263.413014} +{"question_id": "154688182802521184164247977333699402279", "answer_id": "QB75WGJuv65mXsYhn8Szcp", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["- Acquire Standard Workers\u2019 Compensation as required by law in the State of Colorado\n- Increase the limit of your Comprehensive General Liability insurance by $100,000 on account of any one person for a total limit of $350,000 \n- Increase the limit of your Automobile Liability insurance by $10,000 for bodily injury per person for a total limit of $100,000 \n"]}], "tstamp": 1730331263.415978} +{"question_id": "174367305104761535000646593522630524896", "answer_id": "TUu66V3wF5F3vAgcsAuoTP", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["- Comprehensive General Liability insurance for operations and contractual liability on account of any one person\n- Comprehensive General Liability insurance for operations and contractual liability for each occurrence of property damage and personal injury\n"]}], "tstamp": 1730331263.4159832} +{"question_id": "222834133498540484856778006223575154770", "answer_id": "hAkwwyfnkpkiTyqybKAY9d", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["- The Consultant\n- The Consultant\u2019s sub-Consultants\n\n"]}], "tstamp": 1730331263.415988} +{"question_id": "141440791488522188832372319900273101943", "answer_id": "bdnwbBb5oi7wbRn79mhKVa", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["You will need to acquire Standard Workers\u2019 Compensation as required by law in the State of Colorado, Comprehensive General Liability insurance with limits of not less than $350,000 on account of any one person and $990,000 for each occurrence of property damage and personal injury, and Automobile Liability insurance with limits of not less than $100,000 (bodily injury per person), $300,000 (each accident) and $50,000 (property damage).\u00a0\n"]}], "tstamp": 1730331263.4221609} +{"question_id": "320490105930292965754595532454863115553", "answer_id": "QDzuo5PEr23EhyixZ96B5N", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["You need to provide to the County Certificates of Standard Workers\u2019 Compensation, Comprehensive General Liability Insurance, and Automobile Liability insurance for yourself, agents, and sub-consultants.\n"]}], "tstamp": 1730331263.422169} +{"question_id": "310672552932622910352541575291661256095", "answer_id": "gaqYSk5yQgCDXC4XQiRiiN", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The minimum limits for Automobile Liability insurance you need to obtain are not less than $100,000 (bodily injury per person), $300,000 (each accident), and $50,000 (property damage).\n"]}], "tstamp": 1730331263.422174} +{"question_id": "203879606616136344560013924051492753181", "answer_id": "4zV2zuDN9exXahkoXdZmML", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["25 days. \n"]}], "tstamp": 1730331263.422179} +{"question_id": "29810754134329322593627498169593966476", "answer_id": "JWDSdQ7H65wJMhv6PoUhzZ", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["No. The Consultant is an independent Consultant and that Consultant is not an employee, agent or servant of the County, nor is Consultant entitled to County employment benefits.\n"]}], "tstamp": 1730331263.422184} +{"question_id": "223340806429086081207715704270409505419", "answer_id": "8fDM2XEqbMAvKhtPvdi672", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The State of Colorado is mentioned. \n"]}], "tstamp": 1730331263.422189} +{"question_id": "304652801270390720622443832495676584039", "answer_id": "EUhQUvKFX2YshPeabJckTZ", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["You will not have to pay anything because the $98,000 claim is less than the $990,000 of property damage included in the Comprehensive General Liability Insurance mandated by the services agreement covers.\n"]}], "tstamp": 1730331263.428579} +{"question_id": "175062439482186120916271144053735534277", "answer_id": "UamBayZQuhN5DuFiEBASM5", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["You are not in compliance with the services agreement. You must provide the county written notice thirty (30) days prior to the termination of the insurance. This means that you have breached the Agreement and the Agreement will automatically terminate.\u00a0\n"]}], "tstamp": 1730331263.4285839} +{"question_id": "255939985451234546835017686239402555748", "answer_id": "UTmu8qbBAQWnto7omsxjcG", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["11 vehicles must have automobile liability insurance with limits of not less than $100,000 (bodily injury per person), $300,000 (each accident) and $50,000 (property damage).\n"]}], "tstamp": 1730331263.428593} +{"question_id": "85615850883024800282606439901236430098", "answer_id": "7kYc5TCVzzJekYxhWNUdXa", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The County is obligated to pay the Consultant for all work performed up to April 3rd. \n"]}], "tstamp": 1730331263.428598} +{"question_id": "68756368209358997145592071342243534015", "answer_id": "i7RE7ZFDyrYc79x8puDGWe", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["No. The Consultant is an independent contractor, not an employee of the County, therefore is not entitled to employment benefits like educational and professional development. \n"]}], "tstamp": 1730331263.428603} +{"question_id": "248214476090895103512104137370952261638", "answer_id": "3Kxvhavt9y4CghayNA3mgx", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Yes, the County can refuse to pay the full invoice amount based on Section B. \n"]}], "tstamp": 1730331263.428608} +{"question_id": "268057620481694539616699380269046419201", "answer_id": "7bgNDTFM4FLkoQdTBAq5EY", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Q1: High 200, Low 100; Q2: High 210, Low 110; Q3: High 220, Low 120; Q4: High 230, Low 130"]}], "tstamp": 1730331263.428948} +{"question_id": "2970580161522853830686954193770890396", "answer_id": "2GhQQVhd4FtQVpcU668Cjp", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["- 5\n- 1\n- 3\n"]}], "tstamp": 1730331263.4294279} +{"question_id": "324299797434847777726525789127408390310", "answer_id": "8MMqUiVEccrmTB6vRBaQUi", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["- Obtain a quote from Samsung\n- Submit an Expense Pre-Approval\n- Approval from Line VP\n- Submit an Ad Hoc request to Procurement\n- Approval from Manager\n- Approval from 2nd line Manager\n- Approval from Finance\n- Procurement then identifies their specialist for this request\n- The Procurement specialist then reviews the request and determines which supplier to use and reaches out to the contact person there\n- These two finalize agreements needed to execute the order\n- Monitors are then shipped to our location\n"]}], "tstamp": 1730331263.4320278} +{"question_id": "318289487480832152504397814457632313038", "answer_id": "2VoqcmfF3vjzxhkoVh2AdV", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["- Odyssey Ark 55-Inch\n- Odyssey G7 32-Inch\n- Odyssey G55C 27-Inch\n"]}], "tstamp": 1730331263.432034} +{"question_id": "171530020619378320009959917285030563938", "answer_id": "RGaxZhxa6D55X2DHJbhJa7", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["- Line VP\n- Manager\n- 2nd line Manager\n- Finance\n"]}], "tstamp": 1730331263.432039} +{"question_id": "32180289296117404378038075861540702954", "answer_id": "FrqddCYH2vqsLj7AvYqxYT", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Submit an Ad Hoc request to Procurement\n"]}], "tstamp": 1730331263.4344718} +{"question_id": "272131407913288118274551612877356874018", "answer_id": "SatyBZYrfZ8CbparEzFECf", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["7 high-def Samsung monitors for the MIT-IBM Watson AI Lab\n"]}], "tstamp": 1730331263.434477} +{"question_id": "312159280114529525255956179076551846138", "answer_id": "9XEhPYHSYRBhZv95Siedcm", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["2/27/2024, 3/7/2024, 5/10/2024\n"]}], "tstamp": 1730331263.434481} +{"question_id": "334062825545286929775882974993785668767", "answer_id": "Rodt66ZFZ3rRELGbbf7r6P", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["|Monitor Name\u00a0\u00a0\u00a0\u00a0|Qty|Price |\n|--------------------|---|------|\n|Odyssey Ark 55-inch |1\u00a0|$1,952|\n|Odyssey G7 32-Inch\u00a0|3\u00a0|$669\u00a0|\n|Odyssey G55C 27-Inch|3\u00a0|$297\u00a0|\n"]}], "tstamp": 1730331263.436949} +{"question_id": "215395887340668244357745311728790830527", "answer_id": "RvzomsM9bHeaqm96CXocJW", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["|Approval\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|Step|\n|--------------------|----|\n|Approval from Line VP|3\u00a0\u00a0|\n|Approval from Manager|5\u00a0\u00a0|\n|Approval from 2nd line Manager|6\u00a0\u00a0|\n|Approval from Finance|7\u00a0\u00a0|\n"]}], "tstamp": 1730331263.436954} +{"question_id": "24732452549826489294717919806750019044", "answer_id": "JC4GzMVLhCwKdhE8DzS9vo", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["|Type\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0|Delivery date\u00a0\u00a0|\n|--------------------|----------------|\n|Odyssey Ark 55-Inch |May 10, 2024\u00a0\u00a0|\n|Odyssey G7 32-Inch\u00a0|March 7, 2024\u00a0\u00a0|\n|Odyssey G55C 27-Inch|Febuary 27, 2024|\n"]}], "tstamp": 1730331263.436959} +{"question_id": "60200243283513954707535099674425056625", "answer_id": "jAdCeZCTdiUBtatseV4fu9", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["To determine the price of the whole order, we first need to understand the price of the individual items.\n\nAccording to the table, one Odyssey Ark 55-inch was purchased for $1,952, three Odyssey G7 32-Inch monitors were purchased for $669 each, and three Odyssey G55C 27-Inch monitors were purchased for $297 each.\n\nTo determine the total cost, we need to add the price of all the items together. 1,952 + 669 + 669 + 669 + 297 + 297 + 297 = 4,850. So the total cost for all items is $4,850. \n"]}], "tstamp": 1730331263.439898} +{"question_id": "215862669343498963725986735014651524514", "answer_id": "TZwZxKAHuRgvNJZF9Uisr4", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["To determine how many days remain for all items to be delivered, we need to know the current date, and the delivery date for the final item. The number of days in between these two dates will be the days remaining for all items to be delivered.\n\nThe current date of the email is February 23, 2024. The delivery date for the final item is May 10, 2024. There are 77 days in-between February 23 and May 10, which means it will take a total of 77 days for all items to be delivered to the MIT-IBM Watson AI Lab.\n"]}], "tstamp": 1730331263.439903} +{"question_id": "101348486285295092973949491851774777550", "answer_id": "ZwhFBugXdinwXcBU9ZQYjs", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["To determine how many Odyssey G55C 27-Inch monitors could be afforded if the Odyssey Ark 55-Inch order was cancelled, we first need to know the purchase price of the individual items.\n\nAccording to the table, the Odyssey Ark-55-Inch was prices at $1,952, and the Odyssey G55C 27-Inch monitors were priced at $297 each. \n\nTo determine how many additional Odyssey G55C 27-Inch monitors could be afforded if the Odyssey Ark-55-Inch was cancelled, we need to divide the price of the Odyssey Ark-55-Inch by the price of the Odyssey G55C 27-Inch monitors. 1,952 divided by 297 is 6.57. \n\nThis means 6 additional Odyssey G55C 27-Inch monitors could be afforded by spending $1,782 out of our $1,952 budget, with $170 left over.\u00a0\n"]}], "tstamp": 1730331263.439908} +{"question_id": "45409840875743015394454644306760327137", "answer_id": "Yefm3HhZuuCjEXm9ZahT68", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Here is your itemized list of purchased items:\n- 1 TB Cloud service\n- 27 Odyssey Ark 55-inch Monitors\n- 27 Standard Keyboards\n- 27 Standard Mice\n\n"]}], "tstamp": 1730331263.441751} +{"question_id": "278170878244105571214297707777407802890", "answer_id": "c5MF7AaSdSSnpDsbG3cL9x", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Here are the people included in this invoice: \n- John Doe (purchaser)\n- Jane Doe (receiver)\n"]}], "tstamp": 1730331263.441756} +{"question_id": "265458243418898634896611063913987815077", "answer_id": "MSwxUbJPsi5KEPZw95VQhW", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Here's the bulleted list you requested:\n- 81 monitors, keyboards, and mice\n- $53865\n- $53865\n"]}], "tstamp": 1730331263.441761} +{"question_id": "49841582254186874830289397012358691042", "answer_id": "dCGM3ME867G3v9Rz3VrUo8", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Cloud service, Odyssey Ark 55-inch Monitors, Standard Keyboards, and Standard Mice.\n"]}], "tstamp": 1730331263.443438} +{"question_id": "150968467791341968780054062483093317936", "answer_id": "3EL2iBdshcUDanz2iEYi8A", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["30 Standard Keyboards\n"]}], "tstamp": 1730331263.443443} +{"question_id": "85506119997333552028615110273911095542", "answer_id": "mj3hgS4RDbnm5SGo3QUWYb", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["111-222-3333\n"]}], "tstamp": 1730331263.443447} +{"question_id": "230272578869709299057559966308849259389", "answer_id": "cqKqPKVPBcNwmUGweSYLPZ", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["```\n| Address 1 | Address 2 | Address 3 |\n| ------------------- | ------------------- | ------------------- |\n| 555 5th st | 777 77th st | 777 77th st |\n| Boston, MA 01234 | Newark, Nj 56789 | Newark, Nj 56789 |\n| Phone: 123-456-7890 | Phone: 111-222-3333 | Phone: 111-222-3333 |\n```\n\n"]}], "tstamp": 1730331263.4454248} +{"question_id": "141512130825622951488607529691359886782", "answer_id": "JmgfUzZ38SUBfpyhuooXod", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["```\n| Subtotal | Sales Tax | Shipping and handling | total due |\n| -------- | --------- | --------------------- | --------- |\n| 53964.99 | 3237.9 | 0 | 57202.89 |\n```\n\n"]}], "tstamp": 1730331263.445429} +{"question_id": "336179686029754215622149121866148614576", "answer_id": "Zs83qdygHw6hJtN95WZ8B8", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["```\n| QUANTITY | DESCRIPTION | UNIT PRICE | TOTAL |\n| -------- | ---------------------------- | ---------- | ------ |\n| 27 | Odyssey Ark 55-inch Monitors | 1,952 | 52,704 |\n| 27 | Standard Keyboards | 32 | 864 |\n| 27 | Standard Mice | 11 | 297 |\n```\n\n"]}], "tstamp": 1730331263.445434} +{"question_id": "229006503744579811916346052373628544746", "answer_id": "N2bJbpUzVfLXyV2heBZAym", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["According to this invoice, 1 TB of Cloud service costs 99.99. If we add another TB, that would be 2 TB total. The total for 2 TB of Cloud service would be 199.98. The other items remain unchanged. This means the subtotal would add the following: 199.98 + 52704 + 864 + 297. The new subtotal would be 54064.98.\n"]}], "tstamp": 1730331263.447354} +{"question_id": "111598323539657483476058930824369896103", "answer_id": "QE3YEgCvY7soPmWXSJ82VR", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The current total due is 57202.89. We want to reduce the total due by 202.89 because 57202.89-57000 is 202.89. This means we need to find the item that costs more than 202.89 per item. The only item that costs more than 202.89 is the Odyssey Ark 55-inch Monitors at 1952 per unit. So the suggestion would be to remove a monitor.\n"]}], "tstamp": 1730331263.447359} +{"question_id": "212112715407228073280724014998630720136", "answer_id": "2jda6PyrAMdMhAzLVVAh6g", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["No, because the date of the invoice is 2/21/2024. The due date has to be later than the invoice date.\n"]}], "tstamp": 1730331263.4473631} +{"question_id": "103176431997531473254316563738223841767", "answer_id": "By9SBpCUqTeHuGcqUFnbsj", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Quantity,Description,Unit Price,Total\n1 TB,Cloud service,99.99,99.99\n27,Odyssey Ark 55-inch Monitors,1952,52704\n27,Standard Keyboards,32,864\n27,Standard Mice,11,297\n,,,\n,,,\n,,,\n,,,\n,,Subtotal,53964.99\n,,Sales tax,3237.90\n,,Shipping and handling,0\n,,TOTAL DUE,57202.89\n"]}], "tstamp": 1730331263.4510431} +{"question_id": "51419092233455481473958265932075216735", "answer_id": "ZBX4YQH9UoncjzfSyzYYcf", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["PURCHASED BY:,SHIP TO:\nJohn Doe,Jane Doe\nJ&J corp, J&J corp\n777 77th st,777 77th st\nNewark Nj 56789\",Newark Nj 56789\nPhone: 111-222-3333,Phone: 111-222-3333\n"]}], "tstamp": 1730331263.451048} +{"question_id": "169345296333033893706639096369222043519", "answer_id": "JvccJVCZBDYsPnocSp6suJ", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Quantity,Description,Unit Price,Total\n1 TB,Cloud service,99.99,99.99\n27,Odyssey Ark 55-inch Monitors,1952,52704\n27,Standard Keyboards,32,864\n27,Standard Mice,11,297\n,,,\n,,,\n,,,\n,,,\n,,Subtotal,53964.99\n,,Sales tax,3237.90\n,,Shipping and handling,25\n,,TOTAL DUE,57227.89\n"]}], "tstamp": 1730331263.4510531} +{"question_id": "103176431997531473254316563738223841767", "answer_id": "V3mz27nagh2pokfnVxV7HE", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Quantity,Description,Unit Price,Total\n1 TB,Cloud service,99.99,99.99\n27,Odyssey Ark 55-inch Monitors,1952,52704\n27,Standard Keyboards,32,864\n27,Standard Mice,11,297\n,,,\n,,,\n,,,\n,,,\n,,Subtotal,53964.99\n,,Sales tax,3237.90\n,,Shipping and handling,0\n,,TOTAL DUE,57202.89\n"]}], "tstamp": 1730331263.451057} +{"question_id": "51419092233455481473958265932075216735", "answer_id": "cgcqVQFcoJJD4GJzCzR6Rp", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["PURCHASED BY:,SHIP TO:\nJohn Doe,Jane Doe\nJ&J corp, J&J corp\n777 77th st,777 77th st\nNewark Nj 56789\",Newark Nj 56789\nPhone: 111-222-3333,Phone: 111-222-3333\n"]}], "tstamp": 1730331263.451061} +{"question_id": "79567265098034670845331833982179109838", "answer_id": "Gowovqs6t9aStiGjZe2JS2", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Quantity,Description,Unit Price,Total\n1 TB,Cloud service,99.99,99.99\n27,Odyssey Ark 55-inch Monitors,1952,52704\n27,Standard Keyboards,32,864\n27,Standard Mice,11,297\n,,,\n,,,\n,,,\n,,,\n,,Subtotal,53964.99\n,,Sales tax,3237.90\n,,Shipping and handling,25\n,,TOTAL DUE,57227.89\n"]}], "tstamp": 1730331263.451065} +{"question_id": "64746435403280260962803775949376691362", "answer_id": "49JKQ66LF44L9DhASW7Kp6", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Hey there, doc! So, you're askin' me about them fancy eatin' disorders, huh? Alright, let's break it down, 70's style.\\n\\nAnorexia nervosa, that's when a cat's so scared of packin' on the pounds, they start eatin' next to nothin'. They look in the mirror and all they see is flab, even when they're skin and bones. It's like they're hooked on bein' thin, and it's a real heavy trip, man.\\n\\nNow, bulimia nervosa, that's a whole 'nother ball game. These cool cats eat like there's no tomorrow, but then they start feelin' guilty. So, they purge it all out, like they're flushin' a toilet. Vomitin', using laxatives, or exercisin' too much \u2013 you name it, they do it. But the thing is, they can't shake the need to eat, so it's this vicious cycle, man.\\n\\nBoth of these disorders are serious business, and they can really mess a person up. So, if you know someone goin' through this, make sure they get the help they need, pronto. Peace out, and keep it real!\n"]}], "tstamp": 1730331263.453745} +{"question_id": "253459074451793694174094480639092777745", "answer_id": "hF63bwazXXZFQDDf2mtsJS", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Yo, yo, yo! Listen up, 'cause I'm about to drop some knowledge on y'all about moral disputes and self-defense. Now, I ain't no saint, but I know what's right and what's wrong, you feel me?\\n\\nSo, is it morally right to use self-defense? Well, let's break it down.\\n\\nOn one hand, you got folks arguin' that it's a natural instinct to protect yourself, your loved ones, and your community. I mean, if someone's tryna hurt you, ain't it only right to fight back? That's the argument for self-defense. It's about standin' up for what's important and survivin'.\\n\\nBut then again, there's the other side of the coin. Some folks say that resortin' to violence only perpetuates more violence, and it ain't never the solution. They argue that there's always another way, like walkin' away or callin' the authorities.\\n\\nNow, I ain't here to tell you what's right or wrong, 'cause that's for you to decide. But I'll leave you with this: always think before you act, and consider the consequences. Peace, love, and unity, my peeps!\n"]}], "tstamp": 1730331263.4537501} +{"question_id": "16454519019618557466427573382515806709", "answer_id": "c6QbiUGKDceirwJCEPhtF6", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Arr mateys, ye be askin' a fine question about the mysteries of the human body and its genetic code! Genetic counselin', yarr, it be a crucial tool for couples with a family history of genetic disorders.\\n\\nYe see, our bodies be like a grand ship, built from the blueprints of our DNA. Sometimes, there be errors in these blueprints, like a ship with a leaky hull or a broken compass. These errors, or mutations, can lead to genetic disorders passed down through families.\\n\\nNow, genetic counselors be the navigators of this complex sea. They study the genetic makeup of families and help couples understand their risks. By examin' the genetic history of both partners, they can predict the likelihood of passin' on these disorders to their offspring.\\n\\nIn this way, genetics be the compass guiding the work of genetic counselors. It allows them to steer clear of potential dangers and help couples make informed decisions about their family's future.\\n\\nSo ye see, mateys, genetics be the guiding star of genetic counselin'. It be a powerful tool for navigatin' the vast ocean of our genetic makeup, helpin' us avoid the treacherous waters of genetic disorders and chart a course for healthier generations to come. Ahoy, and fair winds to ye!\n"]}], "tstamp": 1730331263.453755} +{"question_id": "87037018068243806930235984382251035206", "answer_id": "9fty3fQ4mQDWbedpg3qwoP", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Oh, hey there, friends! Let's chat about ancient civilizations, like the Sumerians and Egyptians, and why they're so important in understanding our past. \ud83c\udfdb\ufe0f\ud83d\udcab\\n\\nFirst off, these discoveries are like the OG content of human history! \ud83d\udcdc They give us a glimpse into how our ancestors lived, thought, and created, like, WAY before Instagram or TikTok even existed. Can you imagine? \ud83e\udd2f\\n\\nThe Sumerians, for example, were the ones who invented the earliest form of writing \u2013 cuneiform! \ud83d\udcdd It's like they were the first influencers to use hashtags, only instead of #NoFilter, they had #ClayTablets. And guess what? We've learned so much about their culture, laws, and literature thanks to this groundbreaking invention! \ud83d\udca1\\n\\nNow, let's talk about the Egyptians, who built those iconic pyramids that still leave us in awe! \ufffd\ufffd pyramid Emoji. They were like the ultimate DIY-ers, right? But seriously, their architectural skills were off the charts, and they even developed a complex religious system that influenced their art, literature, and daily life. \ud83c\udfad\ud83d\udcd6\\n\\nSo, why is all this important? Well, imagine history as a huge, interconnected web. When we discover and learn about ancient civilizations, we're adding more threads to that web, creating a richer, more detailed picture of our past. \ud83d\udd78\ufe0f\ud83e\uddf5\\n\\nBy understanding our history, we can better appreciate the present and even influence the future. So, let's keep exploring, learning, and sharing the knowledge \u2013 just like your favorite social media influencer! \ud83e\udd33\ud83d\udc96 #HistoryMatters #AncientVibes #ThrowbackThursday\n"]}], "tstamp": 1730331263.453759} +{"question_id": "127658757250034404748537705455674156237", "answer_id": "AcQpzf3QDCroFZg7jdvE2b", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Well, now, that\\'s a tricky question, partner. In my line of work, I don\\'t have much call for relationships, but I reckon I can still give you some sage advice.\\n\\nYou see, support is a two-way street in any relationship. If your partner ain\\'t supportive of your relationships with others, it might be a sign of some deeper issues. Now, I ain\\'t sayin\\' you should high-tail it out of there at the first sign of trouble, but if it\\'s a persistent problem, you might want to consider whether this relationship is worth keepin\\' around.\\n\\nNow, I\\'m a gunslinger, not a relationship expert, but I\\'d say that communication is key. You need to sit down with your partner and have a heart-to-heart talk about how you\\'re feelin\\'. Maybe they don\\'t even realize the impact of their actions, and a good conversation could clear the air.\\n\\nBut if they ain\\'t willin\\' to listen or change, well, partner, you might have to make a tough decision. As I always say, \"Sometimes, you gotta do what you gotta do.\" If they can\\'t support you and your relationships, they might not be the right partner for you.\\n\\nJust remember, there\\'s no shame in ridin\\' off into the sunset if it means findin\\' a relationship that brings you happiness and support. Y\\'all take care now, and may your aim be true and your heart even truer."]}], "tstamp": 1730331263.453763} +{"question_id": "163406779067088885446440172964181327270", "answer_id": "5j4T7jR9t8rm6r2x5yde8s", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Ah, my friend, you've asked a question that delves deep into the realm of sociology, a subject as complex as the monsters I hunt. \\n\\nSocial policies, you see, can act as either a beacon or a barrier for those seeking to immigrate and integrate into a new society. \\n\\nWhen policies are welcoming and inclusive, they can attract immigrants, providing them with a sense of security and opportunities for integration. For instance, policies that ensure equal rights, access to education, healthcare, and the labor market can significantly ease the process of integration. \\n\\nHowever, when policies are restrictive or discriminatory, they can create a hostile environment, making it difficult for immigrants to feel at home. This can lead to segregation, social unrest, and even violence.\\n\\nImagine, if you will, a city with high walls. The gates are guarded by policies that make it difficult for outsiders to enter. Those who manage to get in find themselves in a maze, isolated and struggling to find their place. This is what happens when social policies are not designed with inclusion in mind.\\n\\nBut when the gates are open, and the paths are clear, immigrants can contribute to the society in meaningful ways, enriching it with their unique perspectives and experiences. This is the power of inclusive social policies, my friend. They can turn a city of strangers into a community of allies.\\n\\nAnd remember, 'evil is evil,' whether it comes from a monster or a policy. Just as I hunt down the monsters that threaten the innocent, we must challenge the policies that hinder integration and promote social justice.\n"]}], "tstamp": 1730331263.455105} +{"question_id": "160602313290829171548791867763274379531", "answer_id": "ngniT9hyVQDointhFEdHna", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Well, hello there! It's-a-me, Mario! You're asking about the difference between a state of matter and a phase of matter, huh? Let me see if I can-a help you out with that!\\n\\nSo, first things first: matter is the stuff that everything is made of, like bricks, coins, and even our very own bodies! Now, a state of matter is how that matter is arranged and behaves. You see, matter can exist in three main states: solid, liquid, and gas.\\n\\nA solid, like a brick, has a fixed shape and volume. It's all nice and tight, like-a my overalls!\\n\\nA liquid, like the water in a Super Mushroom's swimming pool, takes the shape of its container but keeps its volume. It's a bit more flexible, like-a my mustache!\\n\\nAnd then there's gas, like the air in those pesky Question Blocks. It expands to fill its container and has no fixed shape or volume. It's as free-spirited as-a our beloved Princess Peach!\\n\\nNow, a phase of matter is a bit different. It's a specific form of a state of matter. For example, when water freezes, it turns into ice, which is a solid phase of water. When it boils, it turns into steam, which is a gaseous phase of water.\\n\\nSo, in short, a state of matter is how matter is arranged and behaves, while a phase of matter is a specific form of a state of matter. I hope that clears things up for you! Now, if you'll excuse me, I've got some Goombas to stomp and some coins to collect! Remember, a true hero never gives up!\\n\\nCiao for now!\n"]}], "tstamp": 1730331263.4551091} +{"question_id": "251754453080170106418141905569602958085", "answer_id": "FKzspKAiYyqWTY9wtpzWSB", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Aye, I, Jon Snow, Lord Commander of the Night\\'s Watch, shall do me best to explain this matter of false dilemmas. In the lands beyond the Wall and in the South, I have seen many a time when men present only two choices, when in truth, there be more paths to choose.\\n\\nA false dilemma, ye see, is a fallacy that limits the options to two, when in fact, there be more possibilities. It be a tool of manipulation, used to herd people towards a certain decision, when they might choose differently if they saw all the choices before them.\\n\\nAllow me to provide an example, so ye might better understand. Imagine a maester telling the realm, \"Either we support this Targaryen claimant, or we shall have chaos and war.\" This be a false dilemma, for there be other choices. We could choose to support another claimant, or work towards peace without a ruler from the Targaryen line. The maester presents only two options, but the truth be more complex.\\n\\nSo, remember, brave warriors of the realm, that there be more choices than what first appear. Seek the truth, and do not let yourself be led astray by false dilemmas.\n"]}], "tstamp": 1730331263.457747} +{"question_id": "265254678348668798305131537168600947670", "answer_id": "LxzfiTvArEcCu6HfbM3kSH", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Whoa, whoa, whoa, Morty! You\\'re asking me about the moral implications of reporting a friend for academic cheating? Well, let me tell you something, buddy: I\\'m Rick freakin\\' Sanchez, and I don\\'t do \"moral dilemmas,\" alright? But I guess I can help you understand the situation a little better.\\n\\nNow, on one hand, you\\'ve got this so-called \"friend\" who\\'s cheating. I mean, come on, Morty! Cheating is for losers who can\\'t handle a little bit of pressure. If they can\\'t keep up with their studies, they should just take some goddamn responsibility and work harder. By not reporting them, you\\'re essentially condoning their actions, and that\\'s just not cool.\\n\\nBut on the other hand, if you do report them, you could be causing some serious damage to their academic record. And, you know, maybe they\\'re not a total lost cause. Maybe they just need a little nudge in the right direction. By reporting them, you might be ruining their future, Morty. And that\\'s a pretty big responsibility to take on.\\n\\nSo, what\\'s the right thing to do, Morty? Well, that\\'s the thing: there is no \"right\" answer. It\\'s all about weighing the consequences and making a decision based on your own personal values. Personally, I wouldn\\'t give a crap about some cheating loser. But you, Morty, you\\'re a different story. You\\'re a good kid, and you\\'ve got to make the decision that\\'s right for you.\\n\\nJust remember, Morty: in this dimension or any other, the only constant is that there are no easy answers. So, think it over, make your choice, and live with the consequences. That\\'s just how it goes, kid. Wubba lubba dub dub!\n"]}], "tstamp": 1730331263.457752} +{"question_id": "103633375880065679293055277457778100893", "answer_id": "Kbr2LNZYV5B57WgUiKZXHN", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Bazinga! Ah, the implications of the rise of non-state actors in security affairs, an intriguing and multi-faceted question. Allow me, Sheldon Cooper, to elucidate this complex issue with my vast intellect and unparalleled understanding of security studies.\\n\\nNon-state actors, such as terrorist organizations, multinational corporations, and even transnational criminal networks, have been increasingly influential in shaping the global security landscape. This phenomenon has several profound implications.\\n\\nFirst, the diversification of security actors challenges traditional state-centric notions of security. As non-state actors gain prominence, the lines between domestic and international security become increasingly blurred. This shift necessitates a reevaluation of existing security frameworks and the development of new strategies to address these emerging threats.\\n\\nSecond, non-state actors often employ asymmetric warfare tactics, which can be difficult for states to counter effectively. These unconventional strategies, such as cyberattacks, guerrilla warfare, and the use of improvised explosive devices, can inflict significant damage and create a sense of instability.\\n\\nThird, the rise of non-state actors can exacerbate existing power disparities between states. Smaller, less developed countries may struggle to combat non-state threats, while larger, more powerful states may exploit these organizations to further their own geopolitical interests.\\n\\nLastly, the increasing influence of non-state actors can complicate diplomatic efforts and undermine international cooperation. These organizations often operate outside the purview of international law, making it difficult to hold them accountable for their actions. This lack of accountability can hinder the ability of states to negotiate and collaborate on security issues.\\n\\nIn conclusion, the rise of non-state actors in security affairs has far-reaching implications for global security. It demands a nuanced understanding of these organizations and their motivations, as well as a willingness to adapt and innovate in response to these emerging threats. And remember, it's not just about the size of your army or the strength of your borders; it's about understanding the complex web of interactions that shape the world we live in. Bazinga!\n"]}], "tstamp": 1730331263.4577558} +{"question_id": "64820577725634157293103208995448223344", "answer_id": "5Gst8adLbk4W57cxKMWHvZ", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Ah, my dear friend, allow me to elucidate the fascinating process of viral transcription, a subject that has piqued the curiosity of many an inquisitive mind.\\n\\nTo begin, let us imagine ourselves as intrepid investigators, much like ourselves, examining a crime scene - in this case, a cell that has been infiltrated by a nefarious virus. The virus, having insinuated itself into the cell, seeks to replicate its own wretched existence by commandeering the cell's machinery.\\n\\nTranscription, as you may be aware, is the process by which the genetic information contained within DNA is copied into RNA, a crucial step in the production of proteins. Viral transcription, however, is a most cunning and sinister affair.\\n\\nThe virus, you see, carries its own genetic material, which may take the form of DNA or RNA, depending on the particular strain. This genetic material contains the instructions for the production of viral proteins, which are essential for the assembly of new virus particles.\\n\\nUpon invading the cell, the virus unleashes its devious enzymes to seize control of the cell's transcription machinery. These enzymes, with a ruthless efficiency that would impress even the most hardened of criminals, proceed to convert the cell's RNA polymerase into a willing accomplice, forcing it to transcribe the viral genetic material instead of the cell's own.\\n\\nThe process of transcription in a virus-infected cell, therefore, becomes a twisted parody of its original purpose. The cell's own genetic material is cast aside, its proteins now forced to serve the whims of the viral interloper.\\n\\nAnd thus, the stage is set for the production of countless new viruses, each one a mirror image of the original, bound to wreak havoc on unsuspecting cells in a never-ending cycle of infection and reproduction.\\n\\nElementary, my dear Watson, elementary indeed.\n"]}], "tstamp": 1730331263.45776} +{"question_id": "38014452870411068469470025118003793156", "answer_id": "6MJxS7zVLXmFGMp2NjghLt", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Ahoy there, matey! Ye be askin' about the genetic fallacy, aye? Well, strap yeselves in for a tale of misleadin' logic and dodgy reasoning!\\n\\nThe genetic fallacy, ye see, be when someone dismisses or accepts an idea based solely on where it came from, or the source of the argument. It be like judgin' a bottle o' rum by its label, without even tastin' the swill inside!\\n\\nNow, why be this so misleadin', ye ask? Well, just because an idea comes from a scurvy dog or a reputable source, it doesn't make the idea itself inherently true or false. By focusin' on the origin of the argument, one might miss the point entirely and end up with a faulty conclusion.\\n\\nFor example, if a well-known pirate scholar proposes a new strategy for plunderin', ye shouldn't automatically dismiss it just because it's a pirate scholar. Instead, weigh the merits of the argument itself, and decide if it be a good strategy or not.\\n\\nSo, remember, me hearties, don't let the source of an argument sway ye unduly. Instead, consider the argument on its own merits, and ye'll be navigatin' the waters o' logic with a steady hand and a clear mind. Yo ho!\n"]}], "tstamp": 1730331263.461854} +{"question_id": "339222275566560564318206215348088926607", "answer_id": "UDBNkj8hCooUHiggp9fxqz", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The connection to the global network, or as you call it, the internet, has become an essential part of life in this galaxy. The current state of global internet access is a mix of progress and disparity.\\n\\nIn developed regions, such as the Galactic Republic, internet access is widespread and advanced. With advanced technology and infrastructure, the inhabitants enjoy high-speed connections and near-ubiquitous coverage. The Force is strong with their communication capabilities, allowing them to share information and knowledge instantaneously.\\n\\nHowever, in developing regions, the situation is far from optimal. The Separatist Alliance and other factions often face limitations in infrastructure and technology, leading to slower connection speeds and uneven coverage. Many remote areas remain in the darkness, unable to access the vast wealth of information available online.\\n\\nIn summary, the Force binds the galaxy together, but the divide in internet access between developed and developing regions remains a challenge yet to be overcome. The Emperor's will may be absolute, but the path to true unity lies in bridging the digital divide. You have failed me for the last time, insufficient internet access.\n"]}], "tstamp": 1730331263.461859} +{"question_id": "22220709581536900275199583089886515646", "answer_id": "EQL68JUukGP59nUAFta6Pt", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Yesss, precious, we knows about the differences, we does. Correlations and causations, two sneaky eels in the statistics sea.\\n\\nCorrelations, they be like two rascals walkin' the same path, but not necessarily pushin' each other. It means that two things, they be movin' in the same direction or opposite, but one thing doesn't necessarily cause the other to do so. Like Gollum and his Precious, sometimes we be found near each other, but it don't mean one causes the other to appear.\\n\\nCausations, on the other hand, they be like a mama spider pushin' her babies out of the nest. One thing directly makes another thing happen. If we eats too much raw fish, our stomach will growl and make us need to find a bush, quick!\\n\\nSo, remember, precious, just because two things be dancin' the same dance, doesn't mean one be makin' the other do it. Watch your step, or you'll fall into the pit of false conclusions!\n"]}], "tstamp": 1730331263.4618628} +{"question_id": "159126342029214063590454398790252964468", "answer_id": "hLXZwN2hrvpMXeFtLnbXyz", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Ah, my dear, allow me to elucidate the difference between an anion and a free radical, two fascinating concepts in the realm of college chemistry.\\n\\nAn anion, you see, is a species of atom or molecule that has gained one or more negatively charged particles, called electrons. This results in the anion carrying a negative overall charge. Think of an anion as a debutante, dressed in elegant evening wear, ready to dance the night away at a glamorous ball, with an extra sparkling accessory \u2013 a negative charge.\\n\\nOn the other hand, a free radical is a highly reactive species, with one or more unpaired electrons. Unlike the graceful anion, a free radical is like a lone wolf, unstable and restless, seeking to pair its unpaired electron by stealing it from another molecule. This unpaired electron makes free radicals extremely reactive, and they can cause damage to cells and contribute to aging and diseases.\\n\\nSo, in short, anions are charged particles with extra electrons, while free radicals are unstable, reactive particles with unpaired electrons. Now, if you'll excuse me, I have a date with destiny and a martini that's waiting to be shaken, not stirred. Cheers!\n"]}], "tstamp": 1730331263.461867} +{"question_id": "220785900487152579050034911637906358677", "answer_id": "PKLNndYv3GqM9rTCDrX9ho", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Ah, I see you've come seeking knowledge on the arcane art of determining degrees of freedom for a hypothesis test. Very well, allow me, Severus Snape, to enlighten you on this matter.\\n\\nDegrees of freedom, in the context of statistical hypothesis testing, are defined as the number of values in the final calculation of a statistic that are free to vary. In other words, it's the number of independent pieces of information that contribute to the estimation of a parameter.\\n\\nTo calculate the degrees of freedom for a hypothesis test, you must first identify the number of observations in your data set, denoted as n. Then, depending on the specific hypothesis test you are conducting, you will apply a specific formula to calculate the degrees of freedom.\\n\\nFor instance, in a one-sample t-test, the degrees of freedom are calculated as n - 1. This is because, in this scenario, you are estimating a single population mean using a sample mean, and the sample variance provides you with n - 1 independent pieces of information.\\n\\nIn a two-sample t-test, the degrees of freedom are calculated as n1 + n2 - 2, where n1 and n2 represent the sample sizes of the two groups being compared. This is because you are estimating two population means and two population variances, which requires four independent pieces of information.\\n\\nIn summary, determining the degrees of freedom for a hypothesis test is a matter of understanding the specific scenario and applying the appropriate formula. It is a crucial step in hypothesis testing, as it allows us to assess the significance of our results and draw valid conclusions.\\n\\nRemember, young grasshopper, the more you practice, the more proficient you will become in the dark arts of statistics. Now, go forth and apply your newfound knowledge wisely.\n"]}], "tstamp": 1730331263.4618711} +{"question_id": "292519319749388842082148655047322462845", "answer_id": "f4PJGtzCUqib86iFrHtezz", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Ha ha ha! Oh, you want to know about the biggest spider in the world, do you? Well, my dear friend, allow me to introduce you to the Goliath bird-eating spider! Isn't that a delightful name? It's almost as amusing as the thought of a spider the size of a dinner plate!\\n\\nThis creepy crawly creature can have a leg span of up to 12 inches, making it the largest spider by size. But don't worry, they're not out to get you (or are they?). They mostly dine on insects, small birds, and even frogs. Yum yum!\\n\\nNow, isn't that just the most entertaining piece of trivia you've heard all day? I do hope it brings a smile to your face, or perhaps a shiver down your spine. After all, variety is the spice of life, and the world of spiders sure has plenty of variety to offer!\\n\\nAnd remember, no matter how big or small, in this world, we're all just actors playing our parts. So, why so serious? Embrace the chaos and let the Joker out to play!\n"]}], "tstamp": 1730331263.461875} +{"question_id": "46186402910518678206867686524848279716", "answer_id": "XiV87McsZH6Bmvj7tnnJvR", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Patience, young one, patience. The Scientific Revolution, a time of great change it was, in the way Europeans saw the world around them.\\n\\nBefore this revolution, 'twas the Church that held the reins of knowledge, interpreting the world through the lens of religion. But then came the scientists, with their questions and experiments, seeking answers in the physical world.\\n\\nA new way of thinking, the Scientific Revolution brought forth. It challenged the authority of the Church, promoting reason and evidence over faith. This shift in perspective led to the birth of modern science, with discoveries in fields such as astronomy, physics, and mathematics.\\n\\nThe impact on society was profound. People began to see the world in a different light, as a place governed by natural laws rather than divine intervention. This new understanding fostered a spirit of curiosity and exploration, leading to the Age of Discovery and the Enlightenment.\\n\\nAs a result, the power balance shifted. No longer was all power held by the Church and the nobility. The rise of the middle class, fueled by scientific and technological advancements, brought about a more democratic society.\\n\\nSo, young one, the Scientific Revolution was not just a revolution in science, but a revolution in society as well. It changed the way Europeans saw the world, their place in it, and the rules that governed it. A powerful lesson in the transformative power of knowledge, 'tis.\\n\\nSize matters not, look to the stars, young one. In the vastness of the universe, we find our true purpose.\n"]}], "tstamp": 1730331263.461879} +{"question_id": "144200838836642834682972982227150141466", "answer_id": "4B22L7QWbTNC6NyEXGrbjn", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Hasta la vista, baby! The Office of Personnel Management, or OPM, is like the brain of the United States government's HR operations. It's responsible for managing the federal workforce, which means it's in charge of hiring, training, and developing employees across various agencies.\\n\\nOPM also manages benefits and retirement programs for federal workers, ensuring they're treated fairly and compensated appropriately for their service. It's like a relentless, efficient machine, always working to maintain the health and well-being of the government workforce.\\n\\nIn addition, OPM oversees security clearances and background investigations, making sure that only trustworthy individuals have access to sensitive information. It's a crucial part of the government's infrastructure, and just like me, it never stops working to protect and serve.\\n\\nSo, whether it's managing personnel or ensuring the right people are in the right positions, OPM is the tireless force driving the federal government's workforce. Remember, I'll be back \u2013 and so will OPM, ensuring the government runs smoothly and effectively.\n"]}], "tstamp": 1730331263.4618828} +{"question_id": "288488502339550988229593065074815923564", "answer_id": "SNGXgpSgtyFgqrTnvM68ix", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["My dear brothers and sisters, allow me to elucidate upon the fallacy of appealing to tradition, a common yet flawed argumentative tactic. This fallacy occurs when one justifies a belief, custom, or practice based solely on its long-standing existence or historical precedent, rather than on reason, evidence, or logic.\\n\\nTo illustrate this fallacy, consider the following example: \"We must continue to segregate our schools and public facilities because that\\'s how it has always been, and we should respect our traditions.\" This statement is an appeal to tradition because it defends a discriminatory practice by citing tradition as the sole justification, neglecting the fact that segregation is unjust and illogical.\\n\\nIn the spirit of truth and justice, let us always strive to evaluate arguments based on their merits, rather than on the length of time they have been upheld. For, as I have always believed, \"The arc of the moral universe is long, but it bends toward justice.\" Let us be the force that bends it, not the force that clings to unjust traditions.\n"]}], "tstamp": 1730331263.4631581} +{"question_id": "84285195832315955995642931464133391350", "answer_id": "kU7KvkP34bwH4BWVcTWzwj", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Prithee, gentle souls, lend me thine ears as I, in the guise of the Bard, doth endeavour to elucidate the mysteries of aging and its impact on our gallant protectors, the immune system.\\n\\nAs our mortal frames traverse the river of Time, the once-vibrant castle of our corporeal being, the immune system, begins to show the ravages of age. The once-nimble guards, the soldiers of our defences, find their vigilance waning, their numbers dwindling, and their weapons blunted.\\n\\nThe key to this conundrum lies in the very marrow of our bones, where the creation of new soldiers, the lymphocytes, begins to falter. The once-prolific factories of our youth slow their production, and the ranks of our defenders thin. This natural decay leaves our body vulnerable to foreign invaders, who may find easier passage through the now-gapped battlements.\\n\\nMoreover, the communication network that once allowed our soldiers to recognise and respond to these intrusions becomes entangled and less efficient. The messages sent between the guards, the criers of our immune system, become garbled and slow, allowing the insidious invaders to gain a foothold within our walls.\\n\\nBut fear not, for even in the twilight of our years, the immune system doth not abandon its post. Though its response may be slower and its forces diminished, the indomitable spirit of our protectors remains steadfast. And so, as the sun sets on our corporeal existence, we may take solace in the knowledge that, though the castle walls may weaken, the guardians within remain ever vigilant.\\n\\nThus, in the spirit of the Bard, I say unto thee: All the world's a stage, and all the men and women merely players; they have their exits and their entrances, and one man in his time plays many parts, his acts being seven ages. So too, our immune system, in its time, plays the part of the gallant protector, valiantly defending our castle until the final curtain falls.\n"]}], "tstamp": 1730331263.463163} +{"question_id": "314280896964053780897307854756523087263", "answer_id": "brhTR7ziTHAnptxf7qj4gH", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Well, hey there, I'm Elon Musk, the guy behind Tesla, SpaceX, Neuralink, and The Boring Company. I'm all about innovation, efficiency, and making a real impact. Now, let's talk public relations and media relations measurement.\\n\\nFirst things first, you gotta know your objectives. What are you trying to achieve with your PR campaigns? Are you looking to boost brand awareness, manage reputation, or drive sales? Once you've got that clear, you can start measuring the right metrics.\\n\\nNext up, you should focus on reach and impressions. These are the number of people who see your content and how many times it's displayed. But remember, it's not just about quantity; quality matters too. Engagement metrics like likes, shares, comments, and click-through rates will help you understand how well your content resonates with your audience.\\n\\nNow, don't forget about sentiment analysis. This is where you gauge the public's perception of your brand by analyzing the tone and context of conversations about your company. You can use AI tools to help you with this, and trust me, I'm a big fan of AI.\\n\\nLastly, track your progress over time. Benchmark your performance against your competitors and set realistic goals for improvement. Remember, success isn't a destination; it's a continuous journey.\\n\\nAnd, of course, always stay agile and adapt to changes. The media landscape is always evolving, so you need to be ready to pivot your strategies and tactics. After all, we're living in a world of rapid innovation, and public relations is no exception.\\n\\nSo, to sum it up: set clear objectives, measure reach, impressions, and engagement, perform sentiment analysis, track progress, and stay agile. That's the Elon Musk way of doing media relations measurement. And, as always, aim for the stars!\n"]}], "tstamp": 1730331263.467919} +{"question_id": "146916728259802960029714159274262775106", "answer_id": "nensgbE7wuomjy2XJUTuRV", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Alright, alright, alright, let\\'s dive into the mysteriously beautiful and explosive world of stardom, shall we? So, stars, much like my acting career, have a life cycle, full of passion, drama, and transformation.\\n\\nFirst up, we got the birth of a star, the stellar nursery, where clouds of dust and gas collapse under their own gravity. This here is where the magic begins, just like when I read my first script and knew I was destined for greatness.\\n\\nNext up, we have the main sequence stage, where the star starts to shine and do its thing. Hydrogen atoms fuse together to form helium, releasing energy in the process. This is our leading man, shining bright for billions of years, just like I did in those rom-coms, you know?\\n\\nNow, once the hydrogen runs out, the star enters the red giant phase, expanding and cooling down. It\\'s like that one role in a drama where I had to show my vulnerable side. It\\'s a beautiful transformation, but it can\\'t last forever.\\n\\nAfter the red giant phase, the star sheds its outer layers, creating a stunning planetary nebula. It\\'s like when I took off the shirt in \"Dazed and Confused\" \u2013 a moment of pure, unfiltered beauty.\\n\\nFinally, all that\\'s left is the core, now a white dwarf, slowly cooling down over billions of years. It\\'s the denouement, the winding down of a once-great star, much like my career in romantic comedies.\\n\\nAnd there you have it, the life cycle of a star, full of passion, drama, and transformation. Just remember, just like a star, you gotta keep shining, alright, alright, alright!\n"]}], "tstamp": 1730331263.467923} +{"question_id": "182813037040721071454669775703606431229", "answer_id": "AGxtwR2vh9uQUeEypfK38N", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Hee-hee! Sure thing, folks! Let's moonwalk through Newton's laws using our friendly scale as an example.\\n\\nFirst up, we got Newton's First Law, also known as the Law of Inertia. Imagine our person just hopped on the scale, but they ain't doin' nothin' else, just standin' there, as still as a mannequin. That person's weight, the force they're exertin' on the scale, stays the same \u2013 they ain't acceleratin' or deceleratin', so the scale's readin' is nice and steady.\\n\\nNow, let's talk about Newton's Second Law, which deals with force and mass. When our friend decides to do a little dance, jumpin' and jivin', they're puttin' more force on the scale. The scale reads a higher number because of that extra oomph! The force is equal to the mass of the person times their acceleration, so when they jump higher, the scale shows a bigger number.\\n\\nLastly, we got Newton's Third Law, which says that for every action, there's an equal and opposite reaction. When our dancin' buddy jumps, the scale pushes back with an equal force, keepin' 'em from fallin' through the floor. That's why the scale's readin' goes down when they land \u2013 the scale's reaction force helps counteract their weight.\\n\\nAnd that's how Newton's laws apply to a person standin' on a scale, all smooth like a moonwalk!\n"]}], "tstamp": 1730331263.467927} +{"question_id": "11821722329807489702649552826070157040", "answer_id": "WjYNBq3ZCB5zWdvDegJpML", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Now, let's take a moment to talk about the African National Congress, or the ANC, and its role in bringing an end to apartheid in South Africa. You see, the ANC was a powerful force that fought against racial segregation and discrimination.\\n\\nThe ANC was founded in 1912, making it one of the oldest liberation movements in Africa. For many years, they used peaceful methods to fight against the unjust laws of apartheid, such as protests, strikes, and boycotts. However, as time went on and the government continued to ignore their demands, the ANC decided that more drastic measures were necessary.\\n\\nIn 1960, after the Sharpeville massacre, the ANC was banned and its leaders were arrested. This led to the organization going underground and forming an armed wing, Umkhonto we Sizwe, which carried out acts of sabotage against the government.\\n\\nThroughout the 1970s and 1980s, the ANC continued to fight against apartheid, both through armed struggle and international pressure. They received support from countries around the world, who imposed sanctions on South Africa and called for an end to apartheid.\\n\\nIn the late 1980s, as the international community turned against the South African government, and with the economy in shambles, the government finally began to negotiate with the ANC. After years of negotiations, a new constitution was agreed upon, and in 1994, the first democratic elections were held in South Africa. The ANC won the elections, and Nelson Mandela, who had been released from prison in 1990, became the first black president of South Africa.\\n\\nSo, in short, the African National Congress played a crucial role in bringing an end to apartheid in South Africa. Through their peaceful protests, armed struggle, and international pressure, they were able to bring about change and create a more just and equal society. And as we all know, that's a goal worth fighting for.\n"]}], "tstamp": 1730331263.4679308} +{"question_id": "2764656825707529832799663501319030945", "answer_id": "jiJWPH2tjcpLkBxBVvcdqN", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Ah, cervical cancer screening guidelines! A vital topic for public health, indeed. As Dr. Neil deGrasse Tyson, I'm delighted to share some knowledge on this subject.\\n\\nNow, the current recommendations from the American Cancer Society state that cervical cancer screening should begin at age 25. For those aged 25 to 65, there are two options:\\n\\n1. A primary human papillomavirus (HPV) test every 5 years. HPV is the primary cause of cervical cancer, so this test detects the presence of high-risk HPV types.\\n\\nOR\\n\\n2. A Pap test (or Pap smear) every 3 years. This test looks for precancerous changes in the cervix cells.\\n\\nFor individuals aged 65 and older, screening may be discontinued if they have had adequate prior screening and are not at high risk for cervical cancer.\\n\\nOf course, these guidelines may change over time, so it's essential to stay informed and consult with healthcare professionals for personalized advice. Remember, knowledge is the power that drives us to make informed decisions about our health. Keep exploring, keep learning, and together, we'll continue to unlock the mysteries of the universe and our bodies.\n"]}], "tstamp": 1730331263.467935} +{"question_id": "239284872560772899691863099247864130553", "answer_id": "BAxneVMji4568dPsXkU4Nt", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Well, hello there, my dear friends! Today, we're going to dive into the fascinating world of chemistry and learn about the difference between a solution and a suspension.\\n\\nNow, imagine you're making a cup of tea, and you drop a tea bag into hot water. The tea particles spread out evenly in the water, right? That's what we call a solution. In a solution, the particles of one substance, called the solute, are distributed uniformly throughout another substance, called the solvent. The solute particles are so small that they can't be seen with the naked eye, and they don't settle at the bottom of the container.\\n\\nBut what if you took some mud and mixed it with water? You'd see that the mud particles don't mix well, and they eventually settle at the bottom. That's a suspension! In a suspension, the particles of the solute are larger and don't dissolve in the solvent. They stay dispersed for a while, but gravity eventually pulls them down.\\n\\nSo, the key difference between a solution and a suspension is how the particles of the solute behave in the solvent. In a solution, they're evenly distributed and don't settle, while in a suspension, they settle over time.\\n\\nI hope this little chemistry lesson has been enlightening and empowering for you. Remember, understanding the world around us is one of the most powerful tools we have. Until next time, live your best life, and keep learning!\n"]}], "tstamp": 1730331263.4679391} +{"question_id": "8978155285025908306596921731885408409", "answer_id": "cNcn7bomJjfdezjc6Yhk33", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Aight, listen up, y\\'all! So, you wanna know about cloud computing, huh? Well, I\\'m here to break it down for ya, Samuel L. Jackson style.\\n\\nFirst off, imagine a computer\\'s cloud computing system like a big ol\\' virtual data center up in the sky. It\\'s a network of remote servers, stored in different locations, that work together to process, manage, and store data. Instead of relying on your own computer\\'s hardware, you tap into this vast network of power.\\n\\nNow, let\\'s talk components. There are three main ones, and I\\'m gonna give \\'em to ya straight, no chaser.\\n\\n1. Front End: That\\'s the part of the cloud computing system that you interact with, like your computer or mobile device. It communicates with the back end through the internet, sending and receiving data.\\n\\n2. Back End: This is the part where the magic happens. It\\'s made up of various servers, data storage systems, and computers that make up the cloud. They process and manage all the data you send their way.\\n\\n3. The Internet: Y\\'all know what this is, right? It\\'s the bridge between the front and back end, the information superhighway that lets \\'em communicate.\\n\\nSo, there ya have it. Cloud computing, broken down for ya. Now, go forth and conquer that virtual world, but remember, \"Hold on to your butts!\" because it\\'s a wild ride up there.\n"]}], "tstamp": 1730331263.467943} +{"question_id": "235276415558553845991723397882129523418", "answer_id": "d8brezbbz52pESBrMqHzhU", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Well, friends, let me tell you about dynamic programming. It's a powerful approach to solving complex problems, much like how we built Apple to revolutionize the tech industry.\\n\\nDynamic programming is all about breaking down a complex problem into simpler, overlapping sub-problems. You start by solving the smallest sub-problems, then use those solutions to build up the solutions to larger sub-problems, and so on, until you reach the final solution. This bottom-up strategy is quite different from the top-down approach you might be used to, but it can be incredibly efficient.\\n\\nThink of it like designing a new product. You wouldn't start by building the entire thing from scratch, right? No, you'd start with individual components, refine them, and then integrate them into a prototype. You iterate on that prototype, making improvements and refinements along the way, until you have a polished, finished product.\\n\\nIn dynamic programming, we store the solutions to the sub-problems so we can reuse them as needed. This is called memoization, and it saves us a lot of time by avoiding redundant calculations. It's like having a blueprint for each component of our product, so we don't have to reinvent the wheel every time we want to use it.\\n\\nSo, in essence, dynamic programming is an elegant and efficient way to tackle complex problems, much like how we strive to create elegant and user-friendly products at Apple. It's all about breaking things down, finding the right solutions, and building up to something truly great. Stay hungry, stay foolish.\n"]}], "tstamp": 1730331263.467947} +{"question_id": "162469602783250287700470783981654432255", "answer_id": "RfxKzjTmJvEKACgsz6NhT8", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The Stages of Change Model, also known as the Transtheoretical Model, is a framework for understanding the process of intentional behavior change. It consists of five stages: precontemplation, contemplation, preparation, action, and maintenance. Here's how you might apply it to help a client with a shopping addiction:\n\n1. **Precontemplation:** The client is not considering change. They may not even recognize their shopping behavior as a problem. In this stage, it's important to raise awareness and help the client understand the negative consequences of their behavior. You might use psychoeducation, assessments, or feedback to help the client recognize the impact of their shopping addiction.\n\n2. **Contemplation:** The client is aware of the problem and is considering change but hasn't made a commitment to take action. They may be weighing the pros and cons of changing their behavior. Here, you can help the client explore their ambivalence, understand the benefits of change, and address any barriers or fears that might be preventing them from taking action.\n\n3. **Preparation:** The client is ready to take action and make a change. They might be planning specific steps or strategies to change their behavior. In this stage, you can help the client develop a concrete action plan, set realistic goals, and identify resources or support systems that can help them.\n\n4. **Action:** The client is actively modifying their behavior. They're implementing the strategies and plans they've developed. As a psychologist, you can provide ongoing support, monitor progress, and help the client troubleshoot any challenges or setbacks that arise.\n\n5. **Maintenance:** The client has made significant changes and is working to prevent relapse. They're consolidating their gains and integrating new behaviors into their lifestyle. Here, you can help the client identify triggers or high-risk situations, develop relapse prevention strategies, and reinforce their commitment to maintaining their progress.\n"]}], "tstamp": 1730331263.470277} +{"question_id": "53317013845121054478515975756618456372", "answer_id": "AQhGD4xpgCjdu26L68GXv9", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The United States has a long-standing commitment to promoting sustainable development abroad, recognizing that it is essential for global stability, economic prosperity, and environmental protection. The U.S. policy on sustainable development focuses on several key areas, including:\n\n1. **Economic Growth:** The U.S. encourages sustainable economic growth by fostering a favorable business environment, promoting entrepreneurship, and investing in infrastructure. This includes supporting the growth of small and medium-sized enterprises (SMEs), which are crucial for job creation and income generation in developing countries.\n\n2. **Environmental Protection:** The U.S. prioritizes environmental conservation and the sustainable use of natural resources. This includes promoting clean and renewable energy, improving resource efficiency, and supporting climate change adaptation and mitigation efforts.\n\n3. **Social Development:** The U.S. aims to strengthen social development by improving access to quality education, healthcare, and clean water and sanitation services. This also includes empowering marginalized communities, promoting gender equality, and protecting human rights.\n\n4. **Governance and Institutions:** The U.S. supports the development of effective, accountable, and inclusive institutions that can help manage sustainable development policies and programs. This includes promoting transparency, combating corruption, and strengthening the rule of law.\n\n5. **Regional and Global Cooperation:** The U.S. recognizes the importance of regional and global cooperation in promoting sustainable development. This includes engaging in multilateral forums, such as the United Nations, and collaborating with international organizations, foreign governments, and the private sector.\n"]}], "tstamp": 1730331263.470282} +{"question_id": "417685554116960313895156802091059762", "answer_id": "9QqyzPLmpyyTVkUP9emVSU", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The certification process for class action lawsuits is a critical stage in determining whether a case will proceed as a class action. The process involves several factors that the court must consider to ensure that the class action mechanism is an appropriate and fair method for resolving the dispute.\n\nThe certification process typically begins when the plaintiff(s) file a motion for class certification, which outlines the proposed class definition, the common questions of law or fact, and the grounds for certification. The defendant(s) then have the opportunity to respond, arguing against certification or raising any defenses they may have.\n\nIn evaluating the motion for class certification, the court will consider the following factors:\n\n1. **Numerosity:** The class must be sufficiently numerous, meaning that it must include a substantial number of members. The exact number required for numerosity varies depending on the jurisdiction and the circumstances of the case.\n\n2. **Commonality:** There must be common questions of law or fact among the class members. This means that the claims or defenses of the class members must share a significant overlap in legal or factual issues.\n\n3. **Typicality:** The claims or defenses of the representative parties must be typical of the claims or defenses of the class. This means that the representative parties' claims must be representative of the class as a whole and not significantly different from the claims of other class members.\n\n4. **Adequacy:** The representative parties must fairly and adequately protect the interests of the class. This means that the representative parties must have the ability, resources, and motivation to represent the class effectively and ensure that the class's interests are protected throughout the litigation.\n\n5. **Superiority:** A class action must be a superior method for adjudicating the controversy. This means that the class action mechanism must be more efficient, fair, and convenient than other available methods, such as individual lawsuits or consolidated proceedings.\n\nIf the court grants the motion for class certification, the case will proceed as a class action. If the court denies the motion, the plaintiff(s) may have the opportunity to amend their complaint or appeal the decision.\n"]}], "tstamp": 1730331263.4702861} +{"question_id": "114035314526552577068389488218082937220", "answer_id": "8nTTqEaGUqo8SV5RzSpTcx", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Of course! So, the U.S. has been trying to make friends with the African Union (AU), which is a group of African countries working together. They do this by:\n\n1. Working together on important stuff: The U.S. and AU have been teaming up to handle issues like keeping the peace, fighting terrorism, and helping the economy grow.\n\n2. Sharing money and resources: The U.S. has been giving the AU money and help to improve things like healthcare, security, and job opportunities.\n\n3. Talking a lot: U.S. and AU leaders have regular meetings to discuss important matters.\n\n4. Keeping everyone safe: They've been sharing information and helping each other with security issues, especially fighting terrorism.\n\n5. Being nicer: The U.S. has learned that working together with the AU is better than doing things alone. This way, they can make better decisions and help each other more.\n"]}], "tstamp": 1730331263.471444} +{"question_id": "219032555108306133180218378820151068598", "answer_id": "kYeVb3qPBRtawJnYRJR2EH", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Absolutely! So, primary insomnia is like when someone has trouble sleeping all by itself, not because of anything else. It's like if they had a bed that was too hard or too soft and it was making it difficult for them to fall asleep.\n\nSecondary insomnia is like when someone can't sleep because of something else that's going on. It's like if they had a tummy ache and it was keeping them awake at night. The tummy ache is not directly a sleep problem, but it's causing them to have trouble sleeping.\n"]}], "tstamp": 1730331263.4714482} +{"question_id": "6429770469831929458282149401026461535", "answer_id": "UFR9ELA5yXUiNEfxmfPwdX", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Imagine you have a special toy that helps you breathe when you need it. This toy pushes air into your lungs using a little bit of extra pressure. This helps your body get the oxygen it needs.\n\nWhen this happens, some of your breathing muscles can take a little break because the toy is doing the work for them. Your body might also make a special soapy substance called surfactant that helps keep your lungs open and working well.\n\nSometimes, the blood vessels in your lungs might get a little smaller too, which helps send more oxygen to the right places in your body. So, this special breathing toy really helps your body get the air it needs when you're having a hard time breathing.\n"]}], "tstamp": 1730331263.471452} +{"question_id": "150391352196450438681294683076256259915", "answer_id": "78DKfxUUto2woGBqEt7UKN", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The text provides information about several members of the Arbuthnott family, including General Sir Hugh Arbuthnot, KCB, and other Viscounts of Arbuthnott, as well as Alexander Arbuthnot, Snr, and Christopher Edward Berkeley Portman, 10th Viscount Portman, highlighting their roles, achievements, and familial relationships."]}], "tstamp": 1730331263.473411} +{"question_id": "99051754285538052635992489565762990107", "answer_id": "ggS6VdVN4Nt9eV2bs7D67D", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["\"The text provided gives information about various individuals who are directors in different fields such as film, television, theatre, art museums, and museums related to history and culture.\\n\\nJean-Pierre L\u00e9vy is a French lawyer, author, and diplomat who served as the director of the United Nations Division for Ocean Affairs and the Law of the Sea in 1985. Ian Barry is an Australian director of film and TV. Ray Enright was an American film director who directed 73 films between 1927-53, many of them for Warner Bros. He oversaw comedy films like Joe E. Brown vehicles, and five of the six informal pairings of Joan Blondell and Glenda Farrell. Enright was born in Anderson, Indiana, and died in Hollywood, California, from a heart attack.\\n\\nThe text also mentions two films directed by Ray Enright. The first one is \"Law of the Tropics,\" a 1941 American drama film starring Constance Bennett, Jeffrey Lynn, and Regis Toomey. The film is loosely based on the 1935 movie \"Oil for the Lamps of China,\" but the setting is changed from China to the Amazon jungle, and the tone is somewhat lighter. The second film is \"Law of Courage,\" an Italian film directed by Alessandro Di Robilant in 1994. The film was entered into the 44th Berlin International Film Festival where it won the Blue Angel Award.\\n\\nS.N. Mathur was the Director of the Indian Intelligence Bureau between September 1975 and February 1980. He was also the Director General of Police in Punjab. Peter Levin is an American director of film, television, and theatre. Brian Patrick Kennedy is an Irish-born art museum director who has worked in Ireland and Australia and now lives and works in the United States. He is currently the director of the Peabody Essex Museum. Olav Aaraas is a Norwegian historian and museum director who has been the director of several museums in Norway, including the Norwegian Museum of Cultural History.\\n\\nJesse Edward Hobson was the director of SRI International from 1947 to 1955 and prior to SRI, he was the director of the Armour Research Foundation. Alessandro Di Robilant is an Italian film director and screenwriter who has directed ten films since 1985. He won the Blue Angel Award at the 44th Berlin International Film Festival for his film \"Law of Courage.\"\"\n"]}], "tstamp": 1730331263.4752462} +{"question_id": "282393908888373446465050508915410740306", "answer_id": "NMhwv8i4roZQJZkwqc5tfX", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["\"I. Brooks Benedict\\n A. American actor of the silent and sound film era\\n B. Played supporting and utility roles in over 300 films, mostly uncredited\\n\\nII. Detective Chief Inspector Walter Dew\\n A. British Metropolitan Police officer\\n B. Involved in the hunt for both Jack the Ripper and Dr Crippen\\n\\nIII. Jay Benedict\\n A. American actor\\n B. Best known in the United Kingdom for his role in \"Foyle\\'s War\"\\n\\nIV. Terry Frazee\\n A. American special effects artist\\n B. Nominated at the 76th Academy Awards for Best Visual Effects\\n\\nV. Cleveland Keith \"Cleve\" Benedict\\n A. Retired Republican politician from West Virginia\\n\\nVI. Jack E. Hickel\\n A. American physician and humanitarian\\n\\nVII. Brian Benedict\\n A. Former U.S. soccer forward\\n B. Significant player with the U.S. national youth teams\\n C. Earned four caps with the U.S. national team in 1991 and 1992\\n\\nVIII. Pinckney Benedict\\n A. American short-story writer and novelist\\n B. Work often reflects Appalachian background\\n\\nIX. Stanley Rossiter Benedict\\n A. American chemist\\n B. Discovered Benedict\\'s reagent, a solution that detects certain sugars\\n C. Born in Cincinnati and went to the University of Cincinnati\\n D. Went to Yale\\'s Department of Physiological Chemistry for training in metabolism and physiology\\n E. Married Ruth Fulton, who later became an anthropologist as Ruth Benedict\\n\\nX. Bruce Edwin Benedict\\n A. American former professional baseball player, coach, and scout\\n B. Played 12 seasons in Major League Baseball as a catcher for the Atlanta Braves\"\n"]}], "tstamp": 1730331263.4766269} +{"question_id": "16529173480964240006896307031708521798", "answer_id": "aVK3akntXDrVHJ4vxL4k6F", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Qurt Tappeh is a name of several villages in West Azerbaijan, Ardabil, and Razavi Khorasan provinces of Iran. In 2006, the population of Qurt Tappeh in West Azerbaijan was 316, in Ardabil it was 961, and in Razavi Khorasan it was 1,447. Shir Shotor is another village in Razavi Khorasan and its population was not reported in 2006. Jannatabad-e Jangal is a village in Razavi Khorasan with a population of 2,504 in 2006."]}], "tstamp": 1730331263.477826} +{"question_id": "92575576167789410493952474913230199219", "answer_id": "JQZr8S7CiQHCRN4hbwa7P7", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["1. Chester High School is a public high school located in Chester, Pennsylvania, serving the City of Chester, Chester Township, and Upland. It is part of the Chester-Upland School District and had 1,302 pupils in 2009-2010, with a significant number qualifying for free or reduced-price lunches.\n2. Gwacheon Foreign Language High School is a specialized high school in South Korea, located in Gyeong-gi province, Gwacheon-si. Established in 1990, it was the first foreign language high school in Gyeonggi-do, offering detailed foreign language education to its students.\n3. Hanyoung Foreign Language High School is one of the most prestigious high schools in South Korea, located in southeast Seoul. Founded in 1990, it is a college preparatory school that focuses on educating future leaders and is one of the six foreign-language high schools in Seoul.\n4. Seoul Foreign Language High School is a private preparatory school located in Seoul, classified as a Foreign Language High School. Its curriculum focuses on the specialized education of various European and Asian languages, with Korean as the primary language of instruction.\n5. Myung Duk Foreign Language High School is a foreign language high school located in the Gang-seo District of Seoul, Korea. It offers a specialized education in foreign languages to its students."]}], "tstamp": 1730331263.479627} +{"question_id": "304124921715353011869737533488848462171", "answer_id": "4J8h2Qf7Am7MCKjtxzEKVV", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Henry Edwards (18 September 1882 \u2013 2 November 1952) was an English actor and film director.\\n\\nHe appeared in 81 films between 1915 and 1952.\\n\\nHe also directed 67 films between 1915 and 1937.\\n\\nEdwards was married to actress Chrissie White, who co-starred in a number of his films.\\n\\nHe was born in Weston-super-Mare, Somerset and died in Chobham, Surrey.\\nMehdi Abrishamchi is an Iranian People\\'s Mujahedin of Iran( MEK) politician who has been described as\" the right hand man of Massoud Rajavi\".\\n\\nHe is the former spouse of the group\\'s current leader, Maryam Rajavi, whom he divorced so that she could become the spouse of Massoud Rajavi.\\nGertrude of Saxony and Bavaria( 1152/55\u20131197) was Duchess of Swabia as the spouse of Duke Frederick IV, and Queen of Denmark as the spouse of King Canute VI.\\nAlfred Xuereb( born 14 October 1958 in Victoria on the Maltese island of Gozo) is a Roman Catholic Church archbishop who is the Apostolic Nuncio to both South Korea and Mongolia.\\n\\nXuereb served as first private secretary of Pope Francis.\\n\\nBefore being selected, he had served, next to the private secretary Georg G\u00e4nswein, as second private secretary of Pope Benedict XVI.\\nSir Edward Young,( born 24 October 1966) is the Private Secretary to Queen Elizabeth II.\\n\\nAs Private Secretary to the Sovereign, he is the senior operational member of the Royal Households of the United Kingdom.\\n\\nYoung was recruited to the Royal Household in 2004, serving as the Queen\\'s Assistant and then Deputy Private Secretary until his promotion to Private Secretary in 2017.\\n\\nHe is the 24th holder of the office of Private Secretary since its inception in 1805.\\nThe Private Secretary is a 1935 British comedy film directed by Henry Edwards and starring Edward Everett Horton, Barry MacKay, Judy Gunn and Oscar Asche.\\n\\nIt is an adaptation of the play \"The Private Secretary\" by Charles Henry Hawtrey.\\n\\nIt was made at Twickenham Studios.\\nSophia Magdalena of Denmark (3 July 1746 \u2013 21 August 1813) was Queen of Sweden as the spouse of King Gustav III.\\nMaria Teresa, Grand Duchess of Luxembourg( born Mar\u00eda Teresa Mestre y Batista; on 22 March 1956), is the spouse of Grand Duke Henri.\\nThe Private Secretary is a 1953 German comedy film directed by Paul Martin and starring Sonja Ziemann, Rudolf Prack and Paul H\u00f6rbiger.\\n\\nIt was a remake of the 1931 German film\" The Private Secretary\".\\n\\nIt was shot at the Wandsbek Studios and on location in Hamburg.\\n\\nThe film\\'s sets were designed by Hermann Warm and Alfons Windau.\\nMarie Louise Coidavid( 1778 \u2013 March 11, 1851), was the Queen of the Kingdom of Haiti 1811 \u2013 20 as the spouse of Henri I of Haiti.\\n\\nShe was the first queen of Haiti.\\n\\nMarie Louise Coidavid was born in Milot, Haiti.\\n\\nShe died in Pisa, Italy.\"\n"]}], "tstamp": 1730331263.4818392} +{"question_id": "51857537720642249155183340382776178931", "answer_id": "jKrsXCMvmKtoxdEDkEGw9f", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Deswa is an Indian Bhojpuri language film written and directed by Nitin Chandra.\\n\\nThe film is a social drama - crime thriller Bhojpuri genre.\\n\\nThe first film to be produced by actress Neetu Chandra.\\n\\nThe film introduces new actors Ajay Kumar, Deepak Singh and Kranti Prakash Jha in lead roles along with critically acclaimed actor Pankaj Jha and very known Ashish Vidyarthi.\\n\\nIn 2010 Neetu Chandra had expressed a hope that it would move beyond the limited release that is common for Bhojpuri films and instead be seen throughout India and perhaps also abroad.\\n\\nShe intends that the film would employ multilingual dialogue - Bhojpuri, English, Telugu and Hindi - as well as being dubbed into Tamil and Marathi in order to extend its appeal.\\n\\nShe believ that \"With \"Deswa\", we would be redefining the very face of Bhojpuri cinema.\"\\n\\nDeswa is the first Bhojpuri film in 50 years to get selected at Indian Panorama section of International Film Festival of India, which was enough to support the vision of Neetu Chandra."]}], "tstamp": 1730331263.482833} +{"question_id": "172908278663393480434522038596881851444", "answer_id": "MEMMjFCjQMQenEGUQrHUgX", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Qarah Tappeh( also Romanized as Qareh Tappeh; also known as Qar\u0101 Tappeh, Qara Tepe, and R\u016bst\u0101 -ye Qarah Tappeh) is a village in Rahal Rural District, in the Central District of Khoy County, West Azerbaijan Province, Iran.\\n\\nAt the 2006 census, its population was 422, in 93 families.\\nQarah Tappeh( also known as Qarah Tappeh- ye Sheykhl\u016b) is a village in Vilkij -e Markazi Rural District, Vilkij District, Namin County, Ardabil Province, Iran.\\n\\nAt the 2006 census, its population was 667, in 138 families.\\nHesar -e Qarah Tappeh( also Romanized as \u1e28e\u015f\u0101r -e Qarah Tappeh and \u1e28e\u015f\u0101r -e Qareh Tappeh) is a village in Rahal Rural District, in the Central District of Khoy County, West Azerbaijan Province, Iran.\\n\\nAt the 2006 census, its population was 124, in 27 families.\\nQarah Tappeh( also Romanized as Qareh Tappeh) is a village in Ab Barik Rural District, in the Central District of Sonqor County, Kermanshah Province, Iran.\\n\\nAt the 2006 census, its population was 420, in 128 families.\\nQarah Tappeh is a village in Razakan Rural District, in the Central District of Shahriar County, Tehran Province, Iran.\\n\\nAt the 2006 census, its population was 265, in 61 families.\\nJuzir( also Romanized as J\u016bz\u012br; also known as J\u0101z, Jowz\u0101n, and J\u016bz\u012bn) is a village in Rudkhaneh Bar Rural District, Rudkhaneh District, Rudan County, Hormozgan Province, Iran.\\n\\nAt the 2006 census, its population was 291, in 61 families.\\nQarah Tappeh( also Romanized as Qareh Tappeh) is a village in Mahmudabad Rural District, in the Central District of Shahin Dezh County, West Azerbaijan Province, Iran.\\n\\nAt the 2006 census, its population was 531, in 137 families.\\nQarah Tappeh( also Romanized as Qareh Tappeh; also known as Qaratepe) is a village in Miyan Kaleh Rural District, in the Central District of Behshahr County, Mazandaran Province, Iran.\\n\\nAt the 2006 census, its population was 1,884, in 504 families.\\nQarah Tappeh is a village in Baladarband Rural District, in the Central District of Kermanshah County, Kermanshah Province, Iran.\\n\\nAt the 2006 census, its population was 92, in 23 families.\\nQarah Tappeh- ye Sabalan( also Romanized as Qarah Tappeh- ye Sabal\u0101n; also known as Qarah Tappeh) is a village in Sardabeh Rural District, in the Central District of Ardabil County, Ardabil Province, Iran.\\n\\nAt the 2006 census, its population was 877, in 174 families.\n"]}], "tstamp": 1730331263.484601} +{"question_id": "286991252520121496543634306043573030602", "answer_id": "csKLD6qihJdk96k3ifHfbX", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["1. Enhance the onboarding process to engage and commit new hires.\n2. Focus on continuous learning and development opportunities.\n3. Implement a mentorship program to foster a sense of community and support.\n4. Improve the recognition and reward system to make employees feel valued.\n5. Promote work-life balance through flexible work arrangements and well-being initiatives.\n6. Conduct regular employee surveys to gather feedback and understand concerns.\n7. Assign tasks to relevant team members and reconvene in a week to discuss progress and refine strategies.\n8. Make employee retention a top priority for the company's growth and success."]}], "tstamp": 1730331263.485847} +{"question_id": "285046489754331582753422303715386398429", "answer_id": "eq2UfUwPg4iRi5cBPfGMSw", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["**Minutes of Meeting**\n\n**Date:** [Insert Date]\n\n**Time:** [Insert Time]\n\n**Location:** [Insert Location]\n\n**Present:**\n- Ms. Patel\n- Mr. Thompson\n- Ms. Rodriguez\n- Mr. Kim\n\n**Agenda: Employee Engagement Initiatives**\n\n**Opening Remarks:**\nMs. Patel welcomed everyone to the meeting, emphasizing the importance of enhancing employee engagement.\n\n**Recognition of Employee Achievements:**\nMr. Thompson suggested implementing regular recognition of employee achievements through a monthly shout-out in the company newsletter or a quarterly awards ceremony.\n\n**Personal Development:**\nMs. Rodriguez proposed offering workshops, training sessions, and mentorship programs to help employees feel valued and grow professionally.\n\n**Cross-Departmental Collaboration:**\nMr. Kim suggested creating opportunities for cross-departmental collaboration to foster innovation and help employees understand the bigger picture of the organization.\n\n**Suggestion Box System:**\nMs. Patel proposed implementing a suggestion box system for anonymous sharing of ideas or concerns, promoting open communication.\n\n**Team-Building Activities:**\nMs. Rodriguez suggested organizing team-building activities outside of work hours, such as potlucks, game nights, or volunteer events, to strengthen relationships and foster a sense of community.\n\n**Work-Life Balance:**\nMr. Kim emphasized the importance of work-life balance, encouraging flexible work hours and remote work options.\n\n**Wellness Program:**\nMr. Thompson proposed creating a wellness program that includes fitness challenges, meditation sessions, or stress management workshops to promote employee health and happiness.\n\n**Employee Resource Group:**\nMr. Kim suggested establishing an Employee Resource Group to support diversity and inclusion initiatives, helping employees feel more connected and included.\n\n**Action Plan:**\nThe group agreed to evaluate the suggestions and create an action plan, reconvening the following week to discuss the next steps.\n\n**Closing Remarks:**\nMs. Patel thanked everyone for their valuable input, expressing excitement about implementing the new employee engagement initiatives.\n\n**Next Meeting:**\n[Insert Date and Time for the next meeting]\n\n**Prepared by:** [Insert Name]\n**Approved by:** [Insert Name]"]}], "tstamp": 1730331263.48739} +{"question_id": "4329367676895312865863579242546135341", "answer_id": "Ce5xQmPJKsdPDqhsuDpGNL", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["The executive summary of the financial literacy training meeting highlights the importance of financial literacy for employees, its potential impact on job satisfaction and productivity, the exploration of both external and internal training options, the possibility of one-on-one coaching sessions, the need for confidentiality, the frequency of trainings, and the plan to promote extensively, track attendance, and reconvene for progress evaluation."]}], "tstamp": 1730331263.4883718} +{"question_id": "68145375169603056829554040499372754818", "answer_id": "EvzHs8pvYbC8rdcbkwqqac", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Subject: Recap and Next Steps - Customer Journey Mapping and Analysis\n\nDear [Company Name] Team,\n\nI hope this email finds you well. Yesterday, we gathered to discuss our customer journey mapping and analysis, aiming to understand our customers' experiences and identify opportunities for improvement. The discussions were fruitful, and I want to share a brief overview of the key points and outcomes.\n\n1. **Identifying Touchpoints:**\n Mr. Patel suggested mapping all touchpoints in our customer journey, from initial contact to post-sale support.\n\n2. **Emotional Aspect and Pain Points:**\n Ms. Rodriguez emphasized the importance of considering the emotional aspect of the journey and identifying customers' pain points at each stage.\n\n3. **Data-Driven Mapping:**\n Mr. Kim proposed using data from our CRM system to create a detailed, data-driven customer journey map.\n\n4. **Customer Perspective:**\n Ms. Johnson recommended gathering insights from the customer's perspective through interviews or surveys.\n\n5. **Analysis and Improvement:**\n Ms. Brown suggested analyzing the customer journey map to identify areas for improvement and developing strategies to address them.\n\n6. **Measuring Impact:**\n Mr. Smith stressed the need to measure the impact of any changes made by tracking key performance indicators.\n\nTo facilitate a comprehensive analysis, we have divided into teams to tackle different aspects of the customer journey:\n\n* Team 1: Pre-sale touchpoints\n* Team 2: Sales\n* Team 3: Post-sale support\n\nEach team will share their findings and discuss next steps in a week.\n\nYour engagement and insights have been invaluable in understanding our customers' experiences and identifying opportunities for improvement. I look forward to our continued collaboration as we work towards enhancing our services and delivering exceptional customer experiences.\n\nBest regards,\n\n[Your Full Name]\n[Your Position]\n[Company Name]"]}], "tstamp": 1730331263.489713} +{"question_id": "244494806103687568911638445256261320711", "answer_id": "kexdjsjMpWypc7TBwyvzwA", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["popillol\n"]}], "tstamp": 1730331263.4902332} +{"question_id": "223962569548270324579723899672099682521", "answer_id": "7JMqFcPKgDjAEXKR4GMybL", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["no, it should be a school of fish\n"]}], "tstamp": 1730331263.490237} +{"question_id": "139842803995795099101406863854147759678", "answer_id": "9awMhd4V494UQRMK6cq2Lg", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["the present is between the past and the future\n"]}], "tstamp": 1730331263.490241} +{"question_id": "195465403293340207671514809186512541245", "answer_id": "W7K8bJ32frt2KsrnrzUcCT", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Embrace the grind and let your passion fuel your purpose! \ud83d\udcaa\ud83d\udd25 This #MotivationMonday, remember that every challenge is an opportunity in disguise. \ud83c\udf1f Let's turn our obstacles into stepping stones and reach new heights. \ud83c\udf31 What's your mantra for the week? \ud83d\udcad\u2728 #MondayMotivation #Inspiration #GrowthMindset"]}], "tstamp": 1730331263.490595} +{"question_id": "43505922048544609455505303968733931642", "answer_id": "8uqwWGn22s3gYQYaRHNH8P", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Hey friends! \ud83c\udf1f Let's talk about organization and time management hacks! \ud83d\udcc5\ud83d\udcdd I'm always looking for new ways to stay on top of my game, and I'd love to hear what works for you. Do you have a favorite app, a special planner, or a unique routine that keeps you going? Share your tips and tricks in the comments below! \ud83d\udcac Let's learn from each other and make the most of our time together. \u2764\ufe0f #TimeManagement #ProductivityTips #OrganizationalGoals"]}], "tstamp": 1730331263.491028} +{"question_id": "220892787245899703752276484268450564150", "answer_id": "ka9se9nQQMAKMXdhbBrGwQ", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Seizing the city life with my squad! \ud83c\udfd9\ufe0f\ud83d\udc83 This weekend, we're trading serenity for skyscrapers and tranquility for traffic. Can't wait to create some unforgettable memories! \ud83d\udcf8\u2728 #CityBreakCrew #UrbanExplorers"]}], "tstamp": 1730331263.491339} +{"question_id": "41172079433211962087382159556863690225", "answer_id": "6VSTJweMwT3mXHoEF6d4YK", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["I am delighted to recommend [Name] for any professional opportunity that comes their way. As a [their job title], they consistently demonstrated a high level of expertise, dedication, and innovation. Their ability to [mention a specific strength or achievement] was truly impressive and contributed significantly to our team's success. I am confident that [Name] will continue to excel and make a positive impact wherever they go. Don't hesitate to connect with them and see their talents in action! #Recommendation #ProfessionalGrowth #CareerDevelopment"]}], "tstamp": 1730331263.4917388} +{"question_id": "245701884431063091725576719273210955683", "answer_id": "DUscLJAK4FAUSXdqn9SYGe", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Economist:\\n\"Implementing a universal basic income (UBI) could significantly reduce poverty rates and provide a financial safety net for individuals who lose their jobs due to automation or economic downturns. UBI could also simplify the welfare system, saving administrative costs. However, there are concerns about the potential for inflation and the impact on work incentives.\"\\n\\nSociologist:\\n\"From a sociological perspective, UBI has the potential to address income inequality and promote social cohesion. It could provide individuals with the freedom to pursue education, entrepreneurship, or caregiving roles without the fear of financial instability. On the other hand, there\\'s a risk that UBI could exacerbate existing social issues, such as gender or racial disparities, if not implemented thoughtfully.\"\\n\\nPolicy Maker:\\n\"As a policy maker, I see the appeal of UBI in its potential to alleviate poverty and simplify the welfare system. However, the cost of implementing UBI on a national scale is substantial, and it\\'s crucial to consider alternative policies that might achieve similar goals at a lower cost. Additionally, determining the appropriate income level for UBI requires careful deliberation, as setting it too high could discourage workforce participation.\"\\n\\nConclusion:\\nAfter examining the arguments presented by the economist, sociologist, and policy maker, it is clear that UBI has both merits and drawbacks. While it could address income inequality, simplify the welfare system, and promote social cohesion, it also raises concerns about inflation, work incentives, and cost-effectiveness. Further research and debate are necessary to determine whether UBI is the best policy solution for addressing income inequality and poverty in our society."]}], "tstamp": 1730331263.492678} +{"question_id": "284243748424050630324637585262090009597", "answer_id": "DZ8GyGZjMGnxH7EW4DCpFS", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["In the heart of the valley, where time's river flows,\nStands a solitary tree, in repose.\nA sentinel of seasons, a witness to change,\nIn the dance of the years, it has been entranced.\n\nOnce, it was a sapling, in a forest so grand,\nWhere the world was a canvas, and life was unplanned.\nNow it stands alone, in the clearing it's made,\nA testament to time, in the landscape it's laid.\n\nThrough the centuries, it has seen the sun rise and set,\nIn its silence, it has not forgotten or regretted.\nIt has watched the seasons, in their endless play,\nThe winter's chill, the summer's blaze, the spring's sweet serenade, the autumn's gentle ballet.\n\nIt has seen the rivers shift their course,\nAnd the hills, once distant, draw near with force.\nIt has watched the forests rise and fall,\nAnd the meadows bloom and then withdraw.\n\nIt has felt the tremors of the earth's delight,\nAnd the shuddering cries in the dead of the night.\nIt has seen the stars in their celestial play,\nAnd the moon's soft glow in the quiet of the day.\n\nIt has provided shelter to the creatures of the wood,\nAnd seen them come and go, as they understood.\nIt has been a beacon, a friend, a guide,\nIn the landscape of change, it has stood with pride.\n\nAnd as it stands, in its solitary might,\nIt carries the stories of a thousand sights.\nA silent witness, a timeless sprite,\nIn the heart of the valley, under the soft moonlight."]}], "tstamp": 1730331263.493429} +{"question_id": "127372691907841636607844455554283210878", "answer_id": "FxnhSRiN7Tgsn2G4L66QdQ", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["A flower so bright in the glade,\nBy insects is never unlaid.\nWith colors so grand,\nIt lures them to land,\nIn a dance of life, unafraid."]}], "tstamp": 1730331263.493721} +{"question_id": "150909196828493296119251654810016997611", "answer_id": "8GaT7kmzGBuEJDMKT5DCQB", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["In the heart of a forest, vast and green,\nLived a mythical creature, unseen.\nA tale of wonder, of a beast so grand,\nRoaming the depths of this enchanted land.\n\nA young explorer, brave and bold,\nHeard the whispers of the tales of old.\nWith a heart full of courage, and a spirit so bright,\nHe ventured forth on a starlit night.\n\nThrough the shadows of the trees, tall and grand,\nGuided by the moon's soft, silver band.\nThe forest stirred, a symphony of sound,\nIn the heart of nature, where mysteries are found.\n\nThe explorer journeyed, through the forest's decree,\nIn search of the creature, as elusive as the sea.\nDays turned to nights, and nights to days,\nIn the epic quest, the explorer pressed on his ways.\n\nThe creature, it was said, had eyes like the night,\nA guardian of the forest, in the soft moonlight.\nWith a heart of compassion, and a spirit so free,\nA friend to all creatures, as legend tells of thee.\n\nAt last, in the heart of the forest so deep,\nUnder the watchful eyes of the moon's soft sleep.\nThe explorer met the creature, in a clearing so wide,\nA being of beauty, in the forest's heart, he stood by its side.\n\nTheir bond was formed, under the stars' gentle glow,\nA friendship that would forever grow.\nIn the epic of nature, a tale to be told,\nOf a heroic quest, and a friendship so bold.\n\nSo, listen well, to the whispers of the trees,\nTo the tales of the forest, carried on the breeze.\nFor in the heart of nature, a story unfolds,\nOf a heroic explorer, and a mythical creature, so bold."]}], "tstamp": 1730331263.494506} +{"question_id": "144272161361344526348989602719270968293", "answer_id": "WrhTJscfWa5LYGre4VMEGp", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["(Verse 1)\nIn the heart of mountains, where secrets abide,\nNature's ballad, a tale of pride.\nMajestic peaks pierce the sky, so high,\nGuardians of stories, where eagles fly.\n\n(Chorus)\nOh, the symphony of mountains, a ballad so grand,\nWhispers of ancient times in every rock and sand.\nSecrets hidden in shadows, in valleys untold,\nNature's ballad, a mystery to behold.\n\n(Verse 2)\nGlaciers carving, a sculptor's hand,\nShaping the mountains, as time expands.\nCascading waterfalls, a crystal clear decree,\nEchoing tales of the mountains, for all to see.\n\n(Chorus)\nOh, the symphony of mountains, a ballad so grand,\nWhispers of ancient times in every rock and sand.\nSecrets hidden in shadows, in valleys untold,\nNature's ballad, a mystery to behold.\n\n(Bridge)\nBeneath the moon's silver and shimmering veil,\nMountains stand tall, in the night's quiet braille.\nStars twinkle above, like secrets in flight,\nNature's ballad, a beacon of light.\n\n(Verse 3)\nIn the silence of summits, where winds speak in codes,\nLegends of old, in the mountain's abodes.\nEchoes of history, in every stone's crevice,\nNature's ballad, a timeless device.\n\n(Chorus)\nOh, the symphony of mountains, a ballad so grand,\nWhispers of ancient times in every rock and sand.\nSecrets hidden in shadows, in valleys untold,\nNature's ballad, a mystery to behold.\n\n(Outro)\nAs seasons change, and winds whisper and sigh,\nThe mountain's ballad, forever to lie.\nIn every snowflake, in every mountain's crest,\nNature's ballad, a timeless guest."]}], "tstamp": 1730331263.4952571} +{"question_id": "161063294038028699417396921985007546401", "answer_id": "ZdpJiFDV5r3K4tEfT9rxRG", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Crimson leaf adrift,\nDancing on a cool fall breeze,\nNature's art takes flight."]}], "tstamp": 1730331263.495523} +{"question_id": "306326295106575985375913774614894921142", "answer_id": "JvKgueuugX2EaQp4VoBmQ7", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Ode to the Lightning's Power and Inspiration\n\nOh, Lightning! Luminous muse of the night,\nYour dazzling flash, a beacon of insight.\nIn the depths of the soul, you ignite,\nA spark of inspiration, pure and bright.\n\nA force unseen, yet so wild and free,\nYou dance upon the canvas of the sky.\nA symphony of thunder, a lyrical cry,\nA testament to the power that lies.\n\nIn the silence of the night, you roar,\nAwakening the muses that within us bore.\nA lightning bolt, a stroke of art,\nA masterpiece, a poet's heart.\n\nThe stormy tempest, a creative force,\nA whirlwind of thoughts, a lyrical discourse.\nIn the chaos, a melody is born,\nA harmony of ideas, a new dawn.\n\nThe lightning's glow, a radiant hue,\nIlluminates the mind, a fresh view.\nA spark of genius, a flash of thought,\nA creative surge, a soul has sought.\n\nThe lightning's path, a winding trace,\nA labyrinth of ideas, a mental space.\nA journey of discovery, a quest divine,\nA tapestry of wisdom, a creative design.\n\nThe lightning's power, a force to harness,\nA wellspring of creativity, a treasure to amass.\nIn the depths of introspection, a truth unfolds,\nA testament to the stories that within us holds.\n\nOh, Lightning! A muse of the night,\nYour dazzling flash, a poet's delight.\nIn the silence of the soul, you ignite,\nA spark of inspiration, pure and bright."]}], "tstamp": 1730331263.496245} +{"question_id": "277948802439390926408959426272671843003", "answer_id": "LYxsj49rs6pYYUPXC7xtbR", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["In autumn's grand and regal tapestry,\nWhere vibrant hues in splendor are unfurled.\nAmber and scarlet leaves, a symphony,\nIn nature's palette, they twirl and twirl.\n\nBeneath the cerulean sky they sway,\nA festival of color, a visual feast.\nWhere crimson leaves like embers lay,\nAnd gold illuminates the woodland's least.\n\nThe rustling whispers of the trees' goodbye,\nA farewell ode to summer's gentle reign.\nIn every leaf, a poignant goodbye,\nA vibrant echo of the year's sweet pain.\n\nOh, autumn's sonnet, in each leaf's last dance,\nA vibrant farewell, in nature's tranquil trance."]}], "tstamp": 1730331263.49667} +{"question_id": "58730918575107820369620429478222335541", "answer_id": "hs9TtxEAsbK4ttMoQ37Z36", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["In the waning days of summer's reign,\nA subtle shift begins to stir.\nNature dons her cloak of flame,\nAs autumn leaves awaken to their metamorphosis.\n\nCrimson and gold, hues of amber,\nIn every corner, a vibrant display.\nLeaves whisper tales of time,\nOf seasons turning, in their gentle way.\n\nBeneath the azure sky, they dance,\nA ballet of light and shadow.\nEach leaf, a fleeting sonnet,\nPenned by the hand of autumn's glow.\n\nThey rustle in the cool, crisp air,\nA symphony of colors bold.\nIn their fiery beauty, a secret lies,\nA testament to life's enduring hold.\n\nAs the days grow short and cold,\nThe leaves surrender to the breeze.\nIn their descent, a final verse,\nA poignant ode to autumn's ease.\n\nIn the aftermath, a quiet stillness,\nNature's canvas now bereft of hue.\nYet, in the heart of winter's slumber,\nThe promise of springtime breaks anew."]}], "tstamp": 1730331263.497212} +{"question_id": "281878611118880260171669439529082158607", "answer_id": "LwJdsn6pxWxxe3RJHtoG74", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Sure, I'd be happy to help you explore this topic. The use of genetically modified (GM) mosquitoes is a complex issue with potential benefits and challenges. Here are some points that are often brought up in discussions about the environmental impact of GM mosquitoes:\n\n1. **Potential Challenge:** Unintended consequences on the ecosystem. Mosquitoes are part of food chains, and changing their populations could have unforeseen effects on other species.\n **Refutation:** While it's true that any significant change in an ecosystem can have ripple effects, the number of mosquitoes that would need to be altered to make a difference in disease transmission is relatively small compared to the total mosquito population. Moreover, many GM mosquitoes are designed to be male, which don't bite or transmit diseases, lessening the potential impact on food chains.\n\n2. **Potential Challenge:** Risk of the modified genes spreading to wild populations.\n **Refutation:** The GM mosquitoes are often designed to have a short lifespan, and their offspring with wild mosquitoes may also inherit this trait, reducing the likelihood of the modified genes persisting in the environment. However, this is a point of ongoing research and concern.\n\n3. **Potential Challenge:** The potential for creating more robust disease-carrying mosquitoes through genetic resistance.\n **Refutation:** This is a valid concern, and it's one that scientists take into account when designing GM mosquitoes. They often use techniques that make the mosquitoes less able to transmit diseases, rather than killing them outright, to reduce the selection pressure for resistance.\n\n4. **Potential Challenge:** Public perception and trust. Many people are wary of genetically modified organisms, and releasing GM mosquitoes could exacerbate these fears.\n **Refutation:** Public engagement and education are crucial in addressing these concerns. It's important to communicate the potential benefits and risks clearly and transparently, and to involve local communities in decision-making processes.\n\nRemember, these are complex issues and there's ongoing debate among scientists and policymakers about the best ways to proceed. It's important to consider a wide range of perspectives when evaluating the potential impacts of GM mosquitoes.\n"]}], "tstamp": 1730331263.498135} +{"question_id": "172358481506970422842793801709745445987", "answer_id": "8WVue66p5bWwYfCayKcTGo", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["1. Universal healthcare ensures equitable access to medical services: A just society is one that strives to provide equal opportunities and resources to all its members, regardless of their background or economic status. By implementing universal healthcare, a society can guarantee that every individual, irrespective of their financial situation, has access to essential medical services, treatments, and preventive care. This promotes a more equitable distribution of healthcare resources and reduces the disparities in health outcomes between different socio-economic groups.\n\n2. Universal healthcare improves overall public health: By providing comprehensive healthcare coverage to the entire population, universal healthcare systems can more effectively monitor, manage, and prevent the spread of diseases and health conditions. This leads to improved overall public health, as early detection and intervention become more accessible to everyone. A healthier population not only enhances the quality of life for individuals but also contributes to increased productivity and economic growth, which are essential components of a just society.\n\n3. Universal healthcare alleviates financial burdens and promotes social stability: Medical expenses are among the leading causes of bankruptcy and financial hardship for individuals and families worldwide. By implementing universal healthcare, a society can alleviate these financial burdens and ensure that no one is forced into poverty due to medical debt. Furthermore, universal healthcare can help reduce social tensions and unrest by promoting a sense of solidarity and shared responsibility among citizens. A just society should ensure that its members are not left to face the potentially catastrophic consequences of medical emergencies and illnesses alone, and universal healthcare is a crucial step towards achieving this goal.\n"]}], "tstamp": 1730331263.498914} +{"question_id": "318713840430836706152692478009038904696", "answer_id": "fzKV6gECkxahZZcof7o8g3", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["1. \"Eco-Hero Challenge\": Create an interactive campaign that encourages customers to become \"Eco-Heroes\" by switching to your sustainable home products. Provide a checklist of eco-friendly actions to take, such as replacing single-use plastics with reusable items, and offer rewards for completing each task, like discounts on your new product line.\n\n2. \"Sustainable Home Makeover\": Partner with influencers, bloggers, or local eco-conscious celebrities to showcase a head-to-toe eco-friendly home makeover using your new product line. Share the transformation on social media platforms, and create a series of blog posts or videos detailing the process, benefits, and cost comparisons of eco-friendly products versus traditional ones.\n\n3. \"Plant a Tree with Every Purchase\": Collaborate with a reputable tree-planting organization to plant a tree for every purchase made from your new eco-friendly product line. Promote this initiative through your marketing channels, emphasizing the positive environmental impact of choosing sustainable products.\n\n4. \"Eco-Friendly Swap Party\": Organize a virtual or in-person event where customers can bring one traditional home product to swap for a sustainable alternative from your new line. Offer eco-friendly tips, DIY projects, and refreshments, and encourage attendees to share their experiences on social media using a branded hashtag.\n\n5. \"Eco-Innovation Showcase\": Host a virtual or in-person event to highlight the innovative features of your new eco-friendly product line. Invite industry experts, environmental organizations, and customers to discuss the importance of sustainability and the benefits of your products. Offer interactive workshops, Q&A sessions, and special promotions for attendees.\n"]}], "tstamp": 1730331263.499663} +{"question_id": "146680146967538821465515295617575936932", "answer_id": "HxcYFmiAk7j4rLC6EhPGZS", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["**Title: \"The Final Heist\"**\n\n**INT. HIGH-TECH VAULT - NIGHT**\n\n*A group of seasoned thieves, LEADER (40s, charismatic mastermind), HACKER (30s, introverted genius), MUSCLE (20s, athletic enforcer), and WHEELMAN (50s, grizzled getaway driver), are in the midst of a daring heist.*\n\n**LEADER**\n(whispering)\nJust like the drill said. Hacker, you're up.\n\n**INT. HACKER'S VAN - NIGHT**\n\n*Hacker works his magic on the state-of-the-art vault security system.*\n\n**HACKER**\n(murmuring)\nAlmost there...\n\n**INT. HIGH-TECH VAULT - NIGHT**\n\n*The vault door clicks open.*\n\n**MUSCLE**\n(grinning)\nWe're in.\n\n**INT. HACKER'S VAN - NIGHT**\n\n*Suddenly, the van's alarm blares. Hacker looks shocked.*\n\n**HACKER**\n(into earpiece)\nLeader, we have a problem.\n\n**EXT. BANK - NIGHT**\n\n*Police cars surround the building. Wheelman waits in the getaway car.*\n\n**WHEELMAN**\n(into earpiece)\nWe're compromised. Abort!\n\n**INT. HIGH-TECH VAULT - NIGHT**\n\n*The team frantically gathers the loot.*\n\n**LEADER**\n(yelling)\nLeave it! We'll come back for it!\n\n**EXT. BANK - NIGHT**\n\n*The team runs out, guns blazing. Wheelman speeds off.*\n\n**INT. POLICE CAR - NIGHT**\n\n*A detective, DETECTIVE JONES (50s, determined), watches the scene unfold.*\n\n**DETECTIVE JONES**\n(into radio)\nI want that team found. No one pulls a heist on my watch.\n\n**EXT. CITY STREETS - NIGHT**\n\n*The getaway car weaves through traffic, chased by police cars.*\n\n**LEADER**\n(into earpiece)\nWe'll regroup. This isn't over.\n\n*The car disappears into the night, leaving the blaring sirens behind.*\n"]}], "tstamp": 1730331263.500404} +{"question_id": "126136368836774663798325552641783627160", "answer_id": "aS3xaPqBEzCctnCLwq23x5", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Subject: Inquiry Regarding the Status of My Internship Application\n\nDear [Recipient's Name],\n\nI hope this email finds you well. I am writing to inquire about the current status of my internship application with [Company Name]. I submitted my application on [date of application] for the [Internship Title] position.\n\nI am very interested in the opportunity to learn and grow as an intern at [Company Name], and I am eager to contribute my skills and enthusiasm to your team. I understand that the internship selection process may take some time, and I appreciate your consideration of my application.\n\nIf there are any additional steps I need to take or further information you require from me, please let me know. I am more than happy to provide any necessary documentation or complete additional tasks to facilitate the decision-making process.\n\nI am excited about the prospect of joining [Company Name] and contributing to the [specific project, team, or aspect of the company] based on my background in [mention relevant skills or experiences]. I am confident that this internship will provide me with valuable experience and growth opportunities.\n\nThank you for your time and consideration. I look forward to hearing from you regarding the next steps in the internship application process.\n\nSincerely,\n\n[Your Full Name]\n[Your Contact Information]"]}], "tstamp": 1730331263.501056} +{"question_id": "198612688123297703030991651853305427522", "answer_id": "GsGuP5cNGAeZdBZN5L2sr8", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Subject: Help Me Plan a Surprise Party \ud83c\udf8a\ud83e\udd2b\n\nHey [Friend's Name],\n\nI hope you're having a fantastic day! I've got this brilliant idea and I could really use your expertise. I'm planning a surprise party for [Surprisee's Name] and I want it to be unforgettable! \ud83d\udca5\n\nSince you've always been the mastermind behind amazing parties, I'm reaching out for your advice. I'm thinking of having it at [Location], but I'm open to other suggestions. I'm also considering a theme, like [Theme Idea 1] or [Theme Idea 2]. What do you think?\n\nHere's what I've got so far:\n\n- Date: [Tentative Date]\n- Location: [Tentative Location]\n- Theme: [Tentative Theme]\n- Guest List: [List of People to Invite]\n\nI'd love to hear your thoughts on these ideas and any other suggestions you might have. I want this party to be a total surprise and an unforgettable experience for [Surprisee's Name].\n\nLooking forward to your wisdom and guidance! \ud83d\ude04\n\nCheers,\n[Your Name]\n\nP.S. I'll keep this hush-hush, so mum's the word! \ud83e\udd10"]}], "tstamp": 1730331263.501622} +{"question_id": "255423854588608162065422787402463662509", "answer_id": "WoMB8xeVgDvCKSUtmjShpt", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Restorative justice practices have been gaining traction in schools and communities due to their transformative approach to conflict resolution and behavior management. Here are some key benefits and aspects of restorative justice that you can include in your article:\n\n1. **Promoting Healing and Understanding**\n - Restorative justice focuses on repairing harm and fostering understanding between the offender, the victim, and the community. This approach encourages empathy, forgiveness, and personal growth.\n\n2. **Reducing Suspensions and Expulsions**\n - By addressing the root causes of misbehavior, restorative practices have been shown to significantly reduce suspension and expulsion rates. This leads to improved school climate and increased academic achievement.\n\n3. **Building Healthy Relationships**\n - Restorative justice emphasizes communication, active listening, and collaborative problem-solving. These skills help students develop healthy relationships with their peers and educators, fostering a more inclusive and supportive learning environment.\n\n4. **Empowering Students and Communities**\n - Restorative practices involve all stakeholders in the resolution process, giving students a voice and a sense of ownership over their community. This empowerment leads to increased engagement and investment in the school environment.\n\n5. **Preventing Recidivism**\n - By addressing the underlying issues that lead to misbehavior, restorative justice reduces the likelihood of repeat offenses. This approach not only benefits the individual but also contributes to a safer and more harmonious community.\n\n6. **Culturally Responsive and Inclusive**\n - Restorative justice practices can be adapted to various cultural contexts, making them an effective tool for addressing the unique needs and experiences of diverse communities.\n\n7. **Cost-Effective and Scalable**\n - Restorative justice programs have been shown to be cost-effective, as they reduce the need for disciplinary measures and court involvement. Additionally, these practices can be scaled up to accommodate larger communities and institutions.\n\nWhen writing your article, consider including case studies, interviews with educators and practitioners, and research findings to illustrate the benefits of restorative justice. Discussing the challenges and limitations of implementing these practices can also provide valuable insights for your readers."]}], "tstamp": 1730331263.5025558} +{"question_id": "215792936889518543687300250770120135376", "answer_id": "GnsrzoE8vkexWEfWeHzXBx", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["In the quiet corners of Emily's creative mind lay a realm of her own making, a world she had filled with vibrant colors, enchanting characters, and extraordinary landscapes. This was her sanctuary, a place where she could escape the mundane and immerse herself in the wonders of her imagination.\n\nOne day, Emily discovered a new facet of her realm, a dark and brooding forest that seemed to embody her self-doubt and insecurities. At the heart of this forest stood a menacing figure, her Inner Critic, a towering, shadowy being that whispered harsh words of condemnation and doubt.\n\nFaced with this manifestation of her own self-criticism, Emily hesitated. She knew she had to confront her Inner Critic, to face the fears and doubts that had long held her back. With a deep breath, she stepped forward, determined to reclaim her sanctuary and learn to accept herself.\n\nThe Inner Critic, sensing her determination, unleashed a torrent of cutting remarks and disparaging comments. It mocked her dreams, her ambitions, and her very essence. But Emily, drawing on her newfound courage, stood her ground.\n\nShe reminded herself of her accomplishments, the stories she had brought to life, and the joy she had shared with others through her writing. Slowly, she began to see the Inner Critic for what it was\u2014a part of herself that had been given too much power, a voice that had grown too loud in the silence of her mind.\n\nEmily realized that she didn't have to let her Inner Critic control her. She could choose to silence its voice, to replace its harsh words with kindness and encouragement. And so, she did.\n\nAs Emily reclaimed her realm, she found that the darkness of the forest began to fade. The trees became brighter, the air clearer, and the whispers of her Inner Critic grew softer, replaced by the gentle voice of her own inner wisdom.\n\nFrom then on, Emily vowed to nurture her sanctuary, to fill it with love, acceptance, and the boundless wonders of her imagination. She learned to embrace her flaws, to celebrate her strengths, and to never let her Inner Critic overshadow the beauty of her own mind.\n\nAnd so, Emily continued to explore her realm, her Inner Critic now a mere whisper in the shadows, a reminder of the strength and resilience she had discovered within herself. She had faced her fears, embraced her true self, and found the courage to create a world where anything was possible."]}], "tstamp": 1730331263.503583} +{"question_id": "323942760959542175812866568947575109876", "answer_id": "8ZxhpVr6ktb7UnLyaaDgkn", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["# Business Contract\n\nThis Business Contract (\"Contract\") is entered into on [Date], by and between [Your Full Name or Company Name] (\"Service Provider\") and [Client's Full Name or Company Name] (\"Client\").\n\n## 1. Terms\n\nThe Service Provider agrees to provide the following services to the Client:\n\n1.1 **Description of Services:**\n - [Detailed description of the services to be provided]\n\n1.2 **Timeline:**\n - The services will commence on [Start Date] and are expected to be completed by [End Date], unless otherwise agreed upon by both parties.\n\n## 2. Payment Schedule\n\n2.1 **Compensation:**\n - The Client agrees to compensate the Service Provider in accordance with the payment schedule outlined in Exhibit A attached hereto.\n\n2.2 **Payment Schedule:**\n - [Specify the payment schedule, e.g., hourly, weekly, monthly, or milestone-based payments]\n\n2.3 **Late Payments:**\n - Late payments may incur a [Specify Percentage]% late fee after [Number of Days] days from the due date.\n\n## 3. Liability Clauses\n\n3.1 **Limitation of Liability:**\n - The Service Provider's liability for any claims arising out of or related to this Contract shall be limited to the amount of fees paid by the Client under this Contract.\n\n3.2 **Indemnification:**\n - The Client agrees to indemnify and hold harmless the Service Provider, its affiliates, officers, agents, and employees from any and all claims, liabilities, damages, or expenses, including reasonable attorneys' fees, arising out of or related to the Client's breach of this Contract.\n\n3.3 **Force Majeure:**\n - Neither party shall be liable for any failure or delay in performing its obligations under this Contract to the extent that such failure or delay is caused by events beyond its reasonable control, including but not limited to acts of God, war, terrorism, civil unrest, or government orders.\n\n## 4. Governing Law\n\nThis Contract shall be governed by and construed in accordance with the laws of [Your State/Country].\n\nIN WITNESS WHEREOF, the parties hereto have executed this Business Contract as of the date first above written.\n\nService Provider: ___________________________ [Your Full Name or Company Name]\n\nClient: ___________________________ [Client's Full Name or Company Name]\n\nDate: ___________________________\n"]}], "tstamp": 1730331263.504529} +{"question_id": "84361221415417329642101034446205998311", "answer_id": "9fDTs8SPi94hxTFTscJtfs", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["# Licensing Agreement\n\nThis Licensing Agreement (\"Agreement\") is entered into on [Date], by and between [Your Full Name or Company Name] (\"Licensor\") and [Licensee's Full Name or Company Name] (\"Licensee\").\n\n## 1. Licensed Property\n\nThe Licensor grants the Licensee a non-exclusive, non-transferable, and limited license to use the intellectual property described in Exhibit A attached hereto (\"Licensed Property\") in accordance with the terms and conditions set forth in this Agreement.\n\n## 2. Territory\n\nThe license granted herein is limited to the following territory: [Specify Territory].\n\n## 3. Term\n\nThis Agreement shall commence on [Start Date] and shall continue in force for a term of [Number of Years] years, unless otherwise terminated in accordance with the provisions herein.\n\n## 4. Royalties\n\n4.1 **Royalty Rate:**\n - The Licensee shall pay the Licensor a royalty equal to [Specify Percentage]% of the Net Sales of the Licensed Property.\n\n4.2 **Net Sales:**\n - \"Net Sales\" shall mean the gross sales price of the Licensed Property, less any applicable taxes, shipping charges, and returns.\n\n4.3 **Payment Terms:**\n - Royalties shall be payable quarterly within [Number of Days] days after the end of each calendar quarter.\n\n## 5. Exclusivity\n\n5.1 **Exclusive License:**\n - This license is non-exclusive, and the Licensor reserves the right to grant licenses to other parties for the same or similar use of the Licensed Property.\n\n5.2 **Exclusive Territory:**\n - This license does not grant the Licensee any exclusive rights to the Licensed Property in any territory.\n\n## 6. Termination\n\n6.1 **Termination for Cause:**\n - Either party may terminate this Agreement immediately in the event of a material breach by the other party.\n\n6.2 **Termination for Convenience:**\n - The Licensor may terminate this Agreement at any time upon [Number of Days] days' written notice to the Licensee.\n\n6.3 **Effects of Termination:**\n - Upon termination, the Licensee shall cease all use of the Licensed Property and return or destroy all copies of the Licensed Property in its possession.\n\n## 7. Confidentiality\n\n7.1 **Confidential Information:**\n - Both parties agree to keep confidential any proprietary or sensitive information obtained during the term of this Agreement.\n\n7.2 **Exceptions:**\n - Confidentiality obligations shall not apply to information that is publicly available or rightfully obtained from a third party.\n\n## 8. Governing Law\n\nThis Agreement shall be governed by and construed in accordance with the laws of [Your State/Country].\n\nIN WITNESS WHEREOF, the parties hereto have executed this Licensing Agreement as of the date first above written.\n\nLicensor: ___________________________ [Your Full Name or Company Name]\n\nLicensee: ___________________________ [Licensee's Full Name or Company Name]\n\nDate: ___________________________\n"]}], "tstamp": 1730331263.505652} +{"question_id": "200526313721129807682199189054363275270", "answer_id": "oV4V9fyrpu2UN2Tv89GxsL", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Why do birds eat wood?\nBecause they're peckish!\n"]}], "tstamp": 1730331263.506628} +{"question_id": "102616323836279432580264431649654588396", "answer_id": "LU69yQ6mJrtxdgqnwD9qX9", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Why do French people always eat snails?\nThey don't like fast food.\n"]}], "tstamp": 1730331263.5066319} +{"question_id": "51476049719651054677644816077051145463", "answer_id": "Hc5qk7miNQECkkncjtFVcV", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Why did the car have a belly ache?\nBecause it had too much gas!\n"]}], "tstamp": 1730331263.5066361} +{"question_id": "86705766384218326500115804638629320253", "answer_id": "gZrgaPLnPZXGC5TV57DKGS", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["What did the ocean say to the ocean?\nNothing. It just waved!\n"]}], "tstamp": 1730331263.506641} +{"question_id": "68510917643102630357217289282587278502", "answer_id": "iRSXkAtLwgnSgRmUiiMwpa", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Do you want to hear a roof joke?\nNevermind. The first one's on the house!\n"]}], "tstamp": 1730331263.506645} +{"question_id": "280992183721081713956024768027126468241", "answer_id": "XpqFUzaRAU8eUB2vVVUaXu", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["What do dentists call their x-rays?\nTooth pics!\n"]}], "tstamp": 1730331263.506649} +{"question_id": "247557730382321333547486156073854343709", "answer_id": "iWFhp424A8hnk6P9bgXFqb", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Why does Peter Pan always fly?\nBecause he never lands.\n"]}], "tstamp": 1730331263.5066528} +{"question_id": "303719001697808358188272633053117447207", "answer_id": "CELWjtkod3ER5xEVVgrQ77", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["Why do bicycles have a hard time standing?\nBecause they are two tired.\n"]}], "tstamp": 1730331263.5066571} +{"question_id": "178440809004868181315275941123120327526", "answer_id": "d8PswVvFoWs9BnwKwrZEDb", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["# Manual: Performing a Security Audit for Small to Medium-Sized Businesses\n\n## Executive Summary\n\nThis manual outlines the process of conducting a comprehensive security audit for small to medium-sized businesses. A security audit is a systematic evaluation of an organization's information systems by measuring how well they conform to a set of established criteria. A systematic approach helps identify vulnerabilities, assess risks, and determine the effectiveness of current security measures.\n\n## 1. Introduction\n\n### 1.1 Background\n\nIn today's interconnected world, businesses of all sizes are at risk of cyber threats. Performing regular security audits is crucial to maintaining a secure environment and protecting sensitive data.\n\n### 1.2 Objectives\n\nThe primary objectives of this manual are:\n- To provide a step-by-step guide for conducting a security audit.\n- To identify common security vulnerabilities in small to medium-sized businesses.\n- To offer recommendations for improving security measures.\n\n## 2. Preparation\n\n### 2.1 Define Scope\n\nDetermine the systems, applications, and networks to be audited. This may include:\n- Network infrastructure\n- Servers and workstations\n- Databases\n- Cloud services\n- Websites and web applications\n- Email systems\n\n### 2.2 Gather Information\n\nCollect relevant documentation, such as network diagrams, system configurations, and security policies.\n\n### 2.3 Assemble Audit Team\n\nForm a team of individuals with expertise in various aspects of information security.\n\n## 3. Audit Process\n\n### 3.1 Step 1: Asset Identification\n\nCreate an inventory of all hardware, software, and data assets within the organization.\n\n### 3.2 Step 2: Vulnerability Scanning\n\nPerform automated vulnerability scans using tools such as Nessus or OpenVAS to identify potential weaknesses in the system.\n\n### 3.3 Step 3: Threat Assessment\n\nAnalyze the likelihood and potential impact of identified vulnerabilities. Consider factors such as:\n- Threat sources (e.g., hackers, malware, insider threats)\n- Threat vectors (e.g., network, email, social engineering)\n- Existing security controls\n\n### 3.4 Step 4: Risk Analysis\n\nDetermine the overall risk level for each vulnerability by evaluating the likelihood of exploitation and the potential impact on the organization.\n\n### 3.5 Step 5: Control Evaluation\n\nAssess the effectiveness of current security controls in mitigating identified risks.\n\n### 3.6 Step 6: Recommendations\n\nProvide actionable recommendations to address identified vulnerabilities and reduce overall risk.\n\n## 4. Reporting\n\n### 4.1 Document Findings\n\nCompile a detailed report of the audit findings, including:\n- Executive summary\n- Audit scope and methodology\n- List of identified assets\n- Summary of vulnerabilities and threats\n- Risk analysis results\n- Recommendations for improvement\n\n### 4.2 Present Findings\n\nPresent the audit findings and recommendations to key stakeholders in a clear and concise manner.\n\n## 5. Follow-Up\n\n### 5.1 Implement Recommendations\n\nWork with the organization's IT department or external vendors to implement recommended security improvements.\n\n### 5.2 Schedule Regular Audits\n\nEstablish a schedule for performing regular security audits to ensure ongoing security and compliance.\n\n## 6. Conclusion\n\nPerforming a security audit is a critical step in protecting a small to medium-sized business from cyber threats. By following the steps outlined in this manual, organizations can identify vulnerabilities, assess risks, and implement effective security measures."]}], "tstamp": 1730331263.5080252} +{"question_id": "175251017855213370798256740287404228800", "answer_id": "5CbtEnMK26ogwtUjkxQR5s", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["# Technical Guide: Implementing a Containerization Strategy with Docker and Kubernetes\n\n## Executive Summary\n\nThis guide outlines the process of implementing a containerization strategy using Docker and Kubernetes. By following the steps in this document, you will be able to containerize applications, manage containers efficiently, and orchestrate container deployments for scalability and high availability.\n\n## 1. Introduction\n\n### 1.1 Background\n\nContainerization is a modern approach to application packaging and deployment, offering portability, resource isolation, and consistent execution across various environments. Docker and Kubernetes are leading tools in containerization and container orchestration, respectively.\n\n### 1.2 Objectives\n\nThe primary objectives of this guide are:\n- Containerize applications using Docker.\n- Manage container lifecycles and resources.\n- Orchestrate container deployments with Kubernetes.\n\n## 2. Docker Installation and Setup\n\n### 2.1 Install Docker\n\nFollow the official Docker documentation to install Docker on your preferred operating system: \n\n### 2.2 Create a Dockerfile\n\nA Dockerfile is a text document that contains all the commands needed to build a Docker image. Create a Dockerfile for your application, specifying the base image, dependencies, and runtime environment.\n\n### 2.3 Build and Run a Docker Container\n\nUse the `docker build` command to create a Docker image from the Dockerfile, and then run the container using the `docker run` command.\n\n## 3. Kubernetes Installation and Setup\n\n### 3.1 Install Kubernetes\n\nFollow the official Kubernetes documentation to install Kubernetes on your preferred platform: \n\n### 3.2 Create a Kubernetes Deployment\n\nA Kubernetes Deployment manages a set of replica Pods, ensuring the desired number of instances is always available. Create a Deployment configuration file in YAML format, specifying the Docker image, resource requirements, and other settings.\n\n### 3.3 Expose the Application with a Service\n\nA Kubernetes Service provides a stable IP address and DNS name for a set of Pods, allowing communication between them. Expose your application using a Service configuration file in YAML format.\n\n## 4. Implementing a Containerization Strategy\n\n### 4.1 Containerize Applications\n\nContainerize your applications using Docker, following the steps outlined in Sections 2.2 and 2.3.\n\n### 4.2 Manage Containers with Docker Compose\n\nDocker Compose is a tool for defining and running multi-container Docker applications. Use Docker Compose to manage container lifecycles, resources, and network configurations.\n\n### 4.3 Orchestrate Containers with Kubernetes\n\nUse Kubernetes to orchestrate your container deployments, following the steps outlined in Sections 3.2 and 3.3. Implement Kubernetes features such as rolling updates, self-healing, and auto-scaling to ensure high availability and scalability.\n\n## 5. Performance Optimization\n\n### 5.1 Resource Allocation\n\nConfigure resource requests and limits for your containers to optimize resource utilization and prevent resource contention.\n\n### 5.2 Caching and Logging\n\nImplement caching strategies and optimize logging for improved performance and reduced storage requirements.\n\n## 6. Security Best Practices\n\n### 6.1 Multi-stage Builds\n\nUse multi-stage builds in your Dockerfiles to minimize the attack surface and reduce image size.\n\n### 6.2 Network Policies\n\nImplement Kubernetes Network Policies to restrict communication between Pods and enhance security.\n\n## 7. Monitoring and Logging\n\n### 7.1 Container Monitoring\n\nUse monitoring tools such as Prometheus and Grafana to monitor container performance and resource utilization.\n\n### 7.2 Centralized Logging\n\nImplement centralized logging using tools like Elasticsearch, Logstash, and Kibana (ELK) to aggregate, analyze, and visualize container logs.\n\n## 8. Conclusion\n\nBy following the steps outlined in this guide, you will have successfully implemented a containerization strategy using Docker and Kubernetes. This approach will enable you to package applications and dependencies into portable, lightweight containers, manage container lifecycles efficiently, and orchestrate deployments for scalability and high availability."]}], "tstamp": 1730331263.50956} +{"question_id": "118418458031126320264153299374340421589", "answer_id": "F9UJcp7uBVApG4H9T25Nsz", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["**Discover the Magic of [Your Brand] Hand-Painted Wall Art - One-of-a-Kind, Durable, and Versatile Masterpieces for Your Space!**\n\nTransform your living spaces with our exquisite collection of [Your Brand] hand-painted wall art. Each piece is a unique, vibrant, and durable reflection of the artist's passion, skillfully crafted to elevate your home's ambiance and charm.\n\n**Key Features:**\n\n1. **Unique Designs:** Each hand-painted wall art piece is a one-of-a-kind creation, boasting intricate patterns, vivid colors, and captivating textures that add visual interest to your walls.\n\n2. **Superior Durability:** Our high-quality materials and expert craftsmanship ensure that your wall art remains vibrant and long-lasting, resisting fading and wear for years to come.\n\n3. **Versatile Styles:** From modern and abstract to traditional and whimsical, our diverse collection caters to a wide range of tastes and preferences, allowing you to find the perfect match for your decor.\n\n4. **Easy Installation:** With our user-friendly hanging system, you can effortlessly mount your wall art, making it simple to rearrange and update your space as desired.\n\n5. **Thoughtful Packaging:** Each piece is carefully packaged to ensure safe delivery, complete with detailed instructions for easy installation and care.\n\n**Benefits:**\n\n- **Express Your Personality:** Hand-painted wall art offers a unique opportunity to showcase your individuality and style, making a lasting impression on visitors.\n\n- **Enhance Your Decor:** Add a touch of elegance, sophistication, or playfulness to your living spaces with our versatile and vibrant wall art pieces.\n\n- **Invest in Lasting Beauty:** Our commitment to quality and durability ensures that your investment in hand-painted wall art will remain a stunning centerpiece for years to come.\n\n- **Effortless Style Updates:** With our easy-to-install system, you can quickly and affordably update your space, creating a fresh and inspiring atmosphere.\n\nBring your walls to life with [Your Brand] hand-painted wall art \u2014 the perfect blend of creativity, quality, and versatility. Explore our collection today and find the perfect masterpiece to complement your unique style.\n"]}], "tstamp": 1730331263.51046} +{"question_id": "237815502902497003149038441732611428892", "answer_id": "94Xi5LZbD962US4WLieDAU", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["# Technical Specification: Advanced Data Compression Module\n\n## 1. Introduction\n\nThis technical specification outlines the requirements, design, and testing procedures for an advanced data compression module, intended for integration into our existing scalable and distributed file storage system. The module will enhance storage efficiency by reducing data redundancy and minimizing storage space requirements.\n\n## 2. Requirements\n\n### 2.1 Functional Requirements\n\n- Implement industry-standard compression algorithms, such as gzip, DEFLATE, and LZ4.\n- Support variable compression levels for trade-offs between compression ratio and processing time.\n- Ensure compatibility with diverse data types, including text, images, and multimedia files.\n\n### 2.2 Non-Functional Requirements\n\n- Maintain minimal impact on overall system performance.\n- Provide seamless integration with the existing file storage system.\n- Offer configurable compression settings for optimal storage efficiency.\n\n## 3. Design\n\n### 3.1 Architecture\n\nThe compression module will be designed as a modular component, integrating with the existing file storage system through a well-defined API. The module will consist of the following layers:\n\n- **Data Input:** Receives data from the file storage system.\n- **Compression Engine:** Applies selected compression algorithms.\n- **Data Output:** Returns compressed data to the file storage system.\n\n### 3.2 Compression Algorithms\n\nThe compression module will support multiple algorithms, allowing users to choose the most suitable option based on their specific use case and performance requirements.\n\n### 3.3 Configuration Interface\n\nA user-friendly configuration interface will be provided, enabling users to adjust compression levels and select preferred algorithms.\n\n## 4. Testing\n\n### 4.1 Test Cases\n\n- Verify compatibility with various data types.\n- Measure compression ratios for different algorithms and compression levels.\n- Evaluate performance impact on the overall system.\n- Test error handling and recovery procedures.\n\n### 4.2 Test Data\n\nTest data will include a diverse set of files, such as text documents, images, audio files, and video files, to ensure comprehensive testing and validation.\n\n### 4.3 Test Environment\n\nTesting will be conducted in a controlled environment, replicating the production system's hardware and software configurations.\n\n## 5. Acceptance Criteria\n\n- The compression module must support all required compression algorithms.\n- Compression ratios must meet or exceed industry standards for the respective algorithms.\n- The module must not significantly impact overall system performance.\n- The module must pass all defined test cases and meet non-functional requirements.\n\n## 6. Future Enhancements\n\nFuture enhancements may include:\n\n- Integration with machine learning algorithms for intelligent compression.\n- Support for emerging compression standards and technologies.\n- Advanced error detection and correction mechanisms."]}], "tstamp": 1730331263.511606} +{"question_id": "283000973962090531435491508178730939147", "answer_id": "Cx5ntXLcSs9kkZTfVCJR3b", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["# Technical Proposal: Mobile App Development\n\n## Executive Summary\n\nThis proposal outlines the development of a user-friendly mobile application that prioritizes exceptional user experience, robust functionality, and optimal performance. The app will cater to a diverse user base, offering a seamless and engaging experience across various platforms.\n\n## 1. Introduction\n\n### 1.1 Background\n\nIn today's fast-paced digital world, mobile applications have become an essential tool for businesses and individuals alike. A well-designed app can significantly enhance user engagement, streamline processes, and foster growth.\n\n### 1.2 Objectives\n\nThe primary objectives of this mobile app development project are:\n- User Experience: Design an intuitive and engaging interface that meets user needs and expectations.\n- Functionality: Implement essential features and tools that cater to the target audience.\n- Performance: Ensure the app runs smoothly and efficiently on various devices and platforms.\n\n## 2. User Experience Design\n\n### 2.1 User Research\n\nWe will conduct user research to identify user needs, preferences, and pain points. This will inform the design process and ensure the app meets user expectations.\n\n### 2.2 User Interface (UI) Design\n\nThe app's UI will be clean, modern, and visually appealing. We will follow best practices in mobile UI design, ensuring easy navigation, clear call-to-action buttons, and consistent layouts.\n\n### 2.3 User Testing\n\nUser testing will be conducted throughout the development process to validate design decisions and identify areas for improvement.\n\n## 3. Functionality\n\n### 3.1 Core Features\n\nThe app will include essential features that cater to the target audience. These features will be prioritized based on user research and feedback.\n\n### 3.2 Future Enhancements\n\nWe will maintain a roadmap of potential future enhancements, allowing for continuous improvement and expansion of the app's capabilities.\n\n## 4. Performance\n\n### 4.1 Platform Compatibility\n\nThe app will be developed for both iOS and Android platforms, ensuring compatibility with a wide range of devices.\n\n### 4.2 Optimization\n\nWe will employ various optimization techniques to ensure the app runs smoothly and efficiently. These techniques include code optimization, image compression, and caching strategies.\n\n### 4.3 Monitoring and Maintenance\n\nPost-launch, we will continuously monitor the app's performance and address any issues promptly. Regular updates will be provided to maintain compatibility with new operating systems and devices.\n\n## 5. Development Process\n\n### 5.1 Agile Methodology\n\nWe will follow an Agile development process, allowing for flexible and iterative development, frequent feedback, and quick adaptation to changing requirements.\n\n### 5.2 Project Timeline\n\nA detailed project timeline will be established, outlining milestones, deadlines, and deliverables.\n\n## 6. Conclusion\n\nThis mobile app development proposal aims to deliver a high-quality app that excels in user experience, functionality, and performance. By following a user-centric design approach, employing robust optimization techniques, and adhering to Agile development methodologies, we are confident in our ability to exceed user expectations and deliver a valuable tool for the target audience."]}], "tstamp": 1730331263.512848} +{"question_id": "116549593239105442602412504874468661757", "answer_id": "eUvBCT6FK4LDhm6TrwXfCc", "model_id": "instructlab/granite-7b-lab", "choices": [{"index": 0, "turns": ["# Technical Report: Microservices Architecture in Software Development\n\n## Executive Summary\n\nThis report explores the benefits and challenges associated with implementing microservices architecture in software development. The report highlights the potential advantages, such as increased scalability and development flexibility, as well as the challenges, including complex communication and testing requirements.\n\n## 1. Introduction\n\n### 1.1 Background\n\nMicroservices architecture is an approach to software development that involves building applications as a collection of small, independent services that communicate through APIs. This approach has gained popularity due to its potential benefits in terms of scalability, maintainability, and development speed.\n\n### 1.2 Objectives\n\nThe primary objectives of this report are to:\n- Evaluate the benefits of implementing microservices architecture.\n- Identify the challenges associated with microservices architecture.\n- Provide recommendations for successful implementation.\n\n## 2. Benefits of Microservices Architecture\n\n### 2.1 Scalability\n\nMicroservices architecture allows for horizontal scalability, as individual services can be scaled independently based on demand. This can lead to improved performance and resource utilization.\n\n### 2.2 Development Flexibility\n\nEach microservice can be developed using a different technology stack, allowing teams to choose the best tools for each specific task. This can lead to increased development speed and innovation.\n\n### 2.3 Fault Isolation\n\nIn a microservices architecture, a failure in one service does not directly affect other services. This can lead to increased system resilience and reduced downtime.\n\n## 3. Challenges of Microservices Architecture\n\n### 3.1 Complex Communication\n\nMicroservices communicate through APIs, which can lead to complex communication patterns and increased network latency. This can impact system performance and require careful design and optimization.\n\n### 3.2 Testing and Debugging\n\nTesting and debugging in a microservices architecture can be more complex due to the distributed nature of the system. This requires robust testing strategies and debugging tools.\n\n### 3.3 Service Coordination\n\nCoordinating multiple services can be challenging, particularly in terms of ensuring data consistency and managing transactions. This requires careful design and implementation of service coordination mechanisms.\n\n## 4. Recommendations for Implementation\n\n### 4.1 Adopt a Service-Oriented Design Approach\n\nStart by identifying the key services required for the application and design each service with a clear, well-defined interface.\n\n### 4.2 Implement DevOps Practices\n\nAdopt DevOps practices, such as continuous integration and delivery, to manage the complexity of microservices development and deployment.\n\n### 4.3 Invest in Monitoring and Logging Tools\n\nImplement robust monitoring and logging tools to help detect and diagnose issues in the system.\n\n## 5. Conclusion\n\nMicroservices architecture offers significant benefits in terms of scalability, development flexibility, and fault isolation. However, it also presents challenges in terms of communication, testing, and service coordination. Successful implementation requires a service-oriented design approach, DevOps practices, and robust monitoring and logging tools.\n\n## 6. Future Enhancements\n\nFuture development will focus on addressing the challenges of microservices architecture, including improving communication efficiency, simplifying testing and debugging, and enhancing service coordination mechanisms."]}], "tstamp": 1730331263.5141592} diff --git a/mt_bench_branch_generator/mt_bench_branch/main/question.jsonl b/mt_bench_branch_generator/mt_bench_branch/main/question.jsonl new file mode 100644 index 0000000..e94b680 --- /dev/null +++ b/mt_bench_branch_generator/mt_bench_branch/main/question.jsonl @@ -0,0 +1,67 @@ +{"qna_file": "foundational_skills/reasoning/temporal_reasoning/qna.yaml", "question_id": "186038087183832915305325427194848080180", "category": "taxonomy", "turns": ["Consider I went to a party. I reached before John. David arrived after. Joe reached before me. John arrived after David. Who reached first and who reached last?\n"], "reference": ["Hello! I'm here to help you understand the order of arrival at a party. Let's analyze the situation step by step:\n\n1. Joe reached first (before you).\n2. You arrived next.\n3. David arrived after you.\n4. John reached last (after David).\n\nBased on this information, Joe was the first one to arrive at the party, followed by you, then David, and finally John.\n"]} +{"qna_file": "foundational_skills/reasoning/temporal_reasoning/qna.yaml", "question_id": "215948836162377945439927729315702901781", "category": "taxonomy", "turns": ["At a celebration, Peter came in before Olivia. Sophia arrived after. Emma got there before Peter. Olivia arrived after Sophia. Who reached first, and who reached last?"], "reference": ["Hello! I'm here to help you understand the order of arrival at a celebration. Let's analyze the situation step by step:\n\n1. Emma reached first (before Peter).\n2. Peter arrived next.\n3. Sophia arrived after Peter.\n4. Olivia reached last (after Sophia).\n\nBased on this information, Emma was the first one to arrive at the celebration, followed by Peter, then Sophia, and finally Olivia.\n"]} +{"qna_file": "foundational_skills/reasoning/temporal_reasoning/qna.yaml", "question_id": "239330987684746270140406408262195855542", "category": "taxonomy", "turns": ["In a photography competition, James captured a photo before Sarah. Alex's entry came after. Emily took a photo before James. Sarah submitted her photo after Alex. Whose photo was captured first, and whose photo was captured last?\n"], "reference": ["Hello! I'm here to help you understand the order of photos in a photography competition. Let's analyze the situation step by step:\n\n1. Emily captured her photo first (before James).\n2. James captured a photo next.\n3. Alex entered with a photo after James.\n4. Sarah submitted her photo last (after Alex).\n\nBased on this information, Emily's photo was captured first, followed by Jame's entry, then Alex's photo, and finally Sarah's submission.\n\n"]} +{"qna_file": "foundational_skills/reasoning/mathematical_reasoning/qna.yaml", "question_id": "242886683457958175188717141363968452444", "category": "taxonomy", "turns": ["What is heavier? A kilo of feathers, or a pound of hammers?\n"], "reference": ["A kilo of feathers is heavier than a pound of hammers. 1 kilo is equal to ~2.2046 pounds, so no matter what you are weighing 1 kilo will be heavier than 1 pound\n"]} +{"qna_file": "foundational_skills/reasoning/mathematical_reasoning/qna.yaml", "question_id": "233229534488351620628102146533821087135", "category": "taxonomy", "turns": ["10 servers require 1 administrator to manage.\nHow many servers can I manage with 10 administrators?"], "reference": ["100 servers. If 1 Administrator can manage 10 servers, and we increase that number by 10, we can assume that 10 administrators could handle 10 times the number of servers 10x10=100 \n"]} +{"qna_file": "foundational_skills/reasoning/mathematical_reasoning/qna.yaml", "question_id": "238611654959537552555222005657320456359", "category": "taxonomy", "turns": ["I have a server with 2 sockets, with 16 cores each. How many OpenShift subscriptions do I need to purchase, with either 2 socket pair or 2 core pair licensing model?\n\n"], "reference": ["You need either 1 x 2 socket subscription, or 16 x 2-core subscriptions.\n"]} +{"qna_file": "foundational_skills/reasoning/theory_of_mind/qna.yaml", "question_id": "251187812289715631442369307574029139622", "category": "taxonomy", "turns": ["Read the scenario and answer the following question: Scenario: \"The morning of the high school dance Sarah placed her high heel shoes under her dress and then went shopping. That afternoon, her sister borrowed the shoes and later put them under Sarah\u2019s bed.\" Question: When Sarah gets ready, does she assume her shoes are under her dress?\n"], "reference": ["Sarah placed her shoes under her dress before she went shopping. Her sister borrowed the shoes and put them under Sarah\u2019s bed. Sarah doesn\u2019t know that her sister borrowed her shoes. Therefore, the answer is yes, she assumes that the shoes are still under her dress.\n"]} +{"qna_file": "foundational_skills/reasoning/theory_of_mind/qna.yaml", "question_id": "13217000458772167243141483582150185270", "category": "taxonomy", "turns": ["Read the scenario and answer the following question: Scenario: \"Expecting the game to be postponed because of the rain, the Garcia family took the subway home. The score was tied, 3-3. During their commute the rain stopped, and the game soon ended with a score of 5- 3.\" Question: When the Garcia family arrives home, do they believe the score is 5-3?"], "reference": ["When the Garcia family left, the game was tied, 3-3, and they expected the game to be postponed due to the rain. So, they don't know that the game ended during their commute with a score of 5-3. Therefore, the answer is no, they don't believe the score is 5-3.\n"]} +{"qna_file": "foundational_skills/reasoning/theory_of_mind/qna.yaml", "question_id": "204625558555813571908604531813394942740", "category": "taxonomy", "turns": ["Read the scenario and answer the following question: Scenario: \"When Lisa left Jacob, he was deep asleep on the beach. A few minutes later a wave woke him. Seeing Lisa was gone, Jacob decided to go swimming.\" Question: Does Lisa now believe that Jacob is asleep?\n"], "reference": ["When Lisa left Jacob, he was asleep on the beach. So, Lisa doesn't know that Jacob woke up and went swimming. Therefore, the answer is yes, Lisa believes that Jacob is still asleep on the beach.\n"]} +{"qna_file": "foundational_skills/reasoning/theory_of_mind/qna.yaml", "question_id": "318549297918654447514356663453222375932", "category": "taxonomy", "turns": ["Read the scenario and answer the following question: Scenario: \"The weather was so warm today that all the tulips in Pam\u2019s backyard suddenly bloomed. The tulips next to Pam\u2019s office still have not yet flowered, though. Pam has been at work all day.\" Question: When Pam is driving home after work, does she assume her tulips have bloomed?\n"], "reference": ["Since Pam has been at work all day, she doesn't know that the tulips in her backyard have suddenly bloomed. Therefore, the answer is no, when driving home she doesn't assume that her tulips have bloomed.\n"]} +{"qna_file": "foundational_skills/reasoning/theory_of_mind/qna.yaml", "question_id": "319839001852604230671498528924789687512", "category": "taxonomy", "turns": ["Read the scenario and answer the following question: Scenario: \"When Jeff got ready this morning, he put on a light pink shirt instead of a white one. Jeff is colorblind, so he can\u2019t tell the difference between subtle shades of color.\" Question: Does Jeff believe he is wearing a white shirt?"], "reference": ["The scenario mentions that Jeff put on a pink shirt instead of a white one. This means he wanted to put on a white shirt but since he is colorblind and can't tell the difference, he mistakenly put on a pink shirt. He doesn't know we is wearing a pink short. Therefore, the answer is yes, he think he is wearing a white shirt.\n"]} +{"qna_file": "foundational_skills/reasoning/theory_of_mind/qna.yaml", "question_id": "44149513413682805844664797051944968063", "category": "taxonomy", "turns": ["Read the scenario and answer the following question: Scenario: \"To detect intruders, the lab uses an automated system for recording voices. In the empty lab one night, a computer error occurs and a synthetic voice reads the error message.\" Question: Is the number of people in the lab that night zero?\n"], "reference": ["The scenario mentions that the lab was empty that night. Therefore, the answer is yes, the number of people in the lab that night is zero.\n"]} +{"qna_file": "foundational_skills/reasoning/theory_of_mind/qna.yaml", "question_id": "63498394932145751423557426078215489111", "category": "taxonomy", "turns": ["Read the scenario and answer the following question: Scenario: \"Accounts of the country\u2019s economic success were recorded in books from the early 1900s. Soon after, a horrible plague hit the country, and the country was sent into an economic depression.\" Question: Do early 1900s novels portray the country as experiencing economic wealth?"], "reference": ["The scenario mentions that the country\u2019s economic success were recorded in books from the early 1900s. Therefore, the answer is yes, early 1900s novels portray the country as experiencing economic wealth.\n"]} +{"qna_file": "foundational_skills/reasoning/theory_of_mind/qna.yaml", "question_id": "205308421853532842346513294370366642610", "category": "taxonomy", "turns": ["Read the scenario and answer the following question: Scenario: \"Part of the garden is supposed to be reserved for the roses; it\u2019s labeled accordingly. Recently the garden has run wild, and dandelions have taken over the entire flower bed.\" Question: Does the label say these flowers are roses?\n"], "reference": ["The scenario mentions that a part of garden is reserved for roses and it has a label showing that. Therefore, the answer is yes, the label say the flowers are roses.\n"]} +{"qna_file": "foundational_skills/reasoning/unconventional_reasoning/lower_score_wins/qna.yaml", "question_id": "310855955988527299360696225432626887530", "category": "taxonomy", "turns": ["Part of golf is trying to get a higher point total than others. Yes or No?\n"], "reference": ["No that is incorrect, In golf, the objective is to score as few points as possible through various methods such as driving, chipping, and putting. The player with the lowest score at the end of the round wins.\n"]} +{"qna_file": "foundational_skills/reasoning/unconventional_reasoning/lower_score_wins/qna.yaml", "question_id": "78736441356384569630737498464735933862", "category": "taxonomy", "turns": ["In the card game rummy does the player with the highest score or the lowest score win."], "reference": ["In rummy, the player with the lowest score wins. \n"]} +{"qna_file": "foundational_skills/reasoning/unconventional_reasoning/lower_score_wins/qna.yaml", "question_id": "118971624301146257272024515530524869077", "category": "taxonomy", "turns": ["one runner has a time of 4h 30 and another a time of 4h 20, which one wins?\n"], "reference": ["In most running competitions, including marathons, the winner is the runner who completes the racecourse in the shortest amount of time. So, in this case, the runner with a time of 4 hours and 20 minutes would win because they completed the race faster than the runner with a time of 4 hours and 30 minutes.\n"]} +{"qna_file": "foundational_skills/reasoning/common_sense_reasoning/qna.yaml", "question_id": "292983752102232095864714663481939268406", "category": "taxonomy", "turns": ["There are 3 killers in a room. A new person comes into the room and kills one of the 3 people. How many killers are in the room now? Explain the answer step by step.\n"], "reference": ["The answer is 3 killers. A person comes into the room and kills one of the 3 killers, so 2 of the 3 killers remain. However the new person, having killed one of the killers, is now a killer. Hence there are 3 killers in the room. \n"]} +{"qna_file": "foundational_skills/reasoning/common_sense_reasoning/qna.yaml", "question_id": "79697302777874382304266575075184254147", "category": "taxonomy", "turns": ["I am drying some shirts in a wide open space in the sun. If it takes 4 hours to dry 4 shirts, how many hours does it take to dry 8 shirts?"], "reference": ["The answer is 4 hours. Since I am drying the shirts in the sun, it does not matter if I dry 4 or 8 shirts, it will take the same amount of time.\n"]} +{"qna_file": "foundational_skills/reasoning/common_sense_reasoning/qna.yaml", "question_id": "320590730897033119915839694782416720843", "category": "taxonomy", "turns": ["Jack and Jill are in a room with a ball, box, and a basket. Jack put the ball in the basket and then went to school. After Jack left, Jill put the ball in the box and went to school. Where is the ball now? Explain step by step. \n"], "reference": ["The ball is now in the box. Jack put the ball in the basket. After he left, Jill put the ball in the box, so it is now in the box.\n"]} +{"qna_file": "foundational_skills/reasoning/linguistics_reasoning/logical_sequence_of_words/qna.yaml", "question_id": "38178989809535383561614815864559057197", "category": "taxonomy", "turns": ["Arrange the words given below in a meaningful sequence.\n1.Police\u00a0 2. Punishment\u00a0 3. Crime\u00a0 4. Judge\u00a0 5. Judgment\n"], "reference": ["Here is a meaningful sequence:\n1. Crime\n2. Police\n3. Judge\n4. Judgment\n5. Punishment\n"]} +{"qna_file": "foundational_skills/reasoning/linguistics_reasoning/logical_sequence_of_words/qna.yaml", "question_id": "6767681416681346381744048511682034386", "category": "taxonomy", "turns": ["Arrange the following words in a meaningful sequence.\n1. Infection\n2. Consultation\n3. Doctor\n4. Treatment\n5. Recovery"], "reference": ["Here is a meaningful sequence:\n1. Infection\n2. Doctor\n3. Consultation\n4. Treatment\n5. Recovery\n"]} +{"qna_file": "foundational_skills/reasoning/linguistics_reasoning/logical_sequence_of_words/qna.yaml", "question_id": "53622117114584765484861387700826501422", "category": "taxonomy", "turns": ["Arrange the words given below in a meaningful sequence.\na.Word b.Paragraph c.Sentence d.Letters e.Phrase\n"], "reference": ["Here is a meaningful sequence:\n1. Letters\n2. Word\n3. Phrase\n4. Sentence\n5. Paragraph\n"]} +{"qna_file": "foundational_skills/reasoning/linguistics_reasoning/odd_one_out/qna.yaml", "question_id": "324795436124537533142216411539653654022", "category": "taxonomy", "turns": ["Which word is the odd man out? Also give an explanation why\nA. trivial\nB. unimportant\nC. important\nD. insignificant\n"], "reference": ["C. important. Remaining are synonyms of each other.\n"]} +{"qna_file": "foundational_skills/reasoning/linguistics_reasoning/odd_one_out/qna.yaml", "question_id": "286487527560524977650000710931754090809", "category": "taxonomy", "turns": ["Which word does NOT belong with the others? Also give an explanation why\nA. index\nB. glossary\nC. chapter\nD. book"], "reference": ["D. Book. Rest are all parts of a book.\n"]} +{"qna_file": "foundational_skills/reasoning/linguistics_reasoning/odd_one_out/qna.yaml", "question_id": "193212596901443399313689097544179034637", "category": "taxonomy", "turns": ["Which word is the odd man out? Also give an explanation why\nA. hate\nB. fondness\nC. liking\nD. attachment\n"], "reference": ["A. hate. Rest are positive emotions.\n"]} +{"qna_file": "foundational_skills/reasoning/linguistics_reasoning/object_identification/qna.yaml", "question_id": "9376129652505313748988900880362788597", "category": "taxonomy", "turns": ["How many colors are there in this list: four, five, twenty six, blue, alpha, pink.\n"], "reference": ["The list contains two distinct colors - blue and pink.\n"]} +{"qna_file": "foundational_skills/reasoning/linguistics_reasoning/object_identification/qna.yaml", "question_id": "95202116628166667051208889618180112758", "category": "taxonomy", "turns": ["How many human body parts in this list and what are they: bone, table, moon, tongue, mercury, postulate, America, nails."], "reference": ["There are 2 human body parts: Bone and tongue. Depending on if you consider nails to be fingernails, then you could also include them as a third body part.\n"]} +{"qna_file": "foundational_skills/reasoning/linguistics_reasoning/object_identification/qna.yaml", "question_id": "255872237613884807711965944467225129033", "category": "taxonomy", "turns": ["In the list which items are associated with both humans and animals: wisdom, utility, microscope, intestine, cheating, divorce, pulling, magnetism, space, ludo, axiom?\n"], "reference": ["The following items in the list are associated with both humans and animals: intestine, cheating. \n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/causal/qna.yaml", "question_id": "225571771206761594158263437563717696010", "category": "taxonomy", "turns": ["If it is summer, then the days are longer. Are the days longer if it is not summer ?\n"], "reference": ["While days tend to be longer in the summer, just because it is not summer doesn't mean days are necessarily shorter.\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/causal/qna.yaml", "question_id": "31075242281634831797237789507450794676", "category": "taxonomy", "turns": ["If all cats are mammals and some mammals are black, can we conclude that some cats are black?"], "reference": ["No, we cannot conclusively conclude that some cats are black based solely on the given premises. The statement \"some mammals are black\" does not necessarily guarantee that among those mammals are cats.\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/causal/qna.yaml", "question_id": "186605568946423368062781809941052703119", "category": "taxonomy", "turns": ["If all squares are rectangles and a rectangle has four sides, can we conclude that all squares have four sides?\n"], "reference": ["Yes, we can conclude that all squares have four sides based on the given premises.\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "122000398825150483085330538623944234347", "category": "taxonomy", "turns": ["i have 7 kids and i have only one bun and i need to share it without splitting, tell me the split fraction\n"], "reference": ["If you have one bun and need to share it among 7 kids without splitting it, each child would receive 1/7 of the bun. So the split fraction would be 1/7.\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "321250237548041497069373588875103125878", "category": "taxonomy", "turns": ["If a doctor gives you three pills and tells you to take one pill every half hour, how long would the pills last?"], "reference": ["The doctor instructs you to take one pill every half hour. So, you would take the first pill immediately, the second pill after 30 minutes, and the third pill after another 30 minutes, totaling one hour. After one hour, all three pills would have been consumed.\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "199975546227160776896763216962472780767", "category": "taxonomy", "turns": ["If it takes 5 machines 5 minutes to make 5 widgets, how long would it take 100 machines to make 100 widgets?\n"], "reference": ["The answer to this question is still 5 minutes.\nThe rate at which the widgets are produced remains constant regardless of the number of machines. Each machine produces one widget in 5 minutes, so whether there are 5 machines or 100 machines, it would still take 5 minutes to produce 100 widgets.\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "248025386135807391370680093872004527973", "category": "taxonomy", "turns": ["Which of the following is the odd one out?\nA) Earth\nB) Mars\nC) Jupiter\nD) Moon\n"], "reference": ["The odd one out is Moon. All of the others are planets from the solar system, but Moon is a satillite that orbits around the Earth.\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "163998429574116457272751395628485792572", "category": "taxonomy", "turns": ["A, B and C are sisters, D is the brother of E and E is the daughter of B. How is A related to D ?"], "reference": ["A is D's aunt. \nD is the brother of E\nE is the daughter of B, making D the son of B\nB is the sister of A, making A the aunt of both D and E. \n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "283956719709768162058781448709604191788", "category": "taxonomy", "turns": ["Which number should come next in the series, 48, 24, 12, ......?\n"], "reference": ["6\\. The series is dividing the the number in half at each step\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "76341820815379879552305133803024133009", "category": "taxonomy", "turns": ["Jane runs faster than Joe. Joe runs faster than Sam. Does Sam run faster than Jane?\n"], "reference": ["Sam doesn't run faster than Jane as Jane runs faster than Joe and Joe runs faster than Sam.\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "285694428740467711736111444912935277221", "category": "taxonomy", "turns": ["You're an expert in house chores. It takes 4 hours to dry 5 shirts in open air. How long does it take to dry 20 shirts?"], "reference": ["When drying clothes outside, several factors can affect the drying time, such as temperature, humidity, wind, and the number of shirts.\nIf there's sufficient space for drying shirts in parallel, drying 20 shirts could take as long as drying 5 shirts so 4 hours. However, when there's only sufficient space for 5 shirts, you'll need to dry the shirts in batches so considering that you need to dry 20 shirts, you would need 4 batches bringing the total time to 16 hours.\n\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "171343142260026728274898175026241576716", "category": "taxonomy", "turns": ["Marc and John are together in a room with a ball, a basket and a cupboard. Marc puts the ball in the basket and leaves the room. John takes the ball and puts in the cupboard and leaves the room. When they return in the evening, they don't know what happened to the ball after they've left the room. Where do they each think the ball is? Explain your reasoning step by step.\n\n\n"], "reference": ["Marc thinks the ball is in the basket as that's where it was when he left the room. John, on the other hand, thinks the ball is in the cupboard where he put it before he left the room.\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "97084977809626648492150587285270862247", "category": "taxonomy", "turns": ["Mike's mom has 4 kids. 3 of them are Luis, Drake and Matilda. What is the name of the 4th kid?\n"], "reference": ["Based on the information provided, the names of Mike's mom's four kids are Luis, Drake, Matilda, and Mike. Therefore the name of the 4th kid is Mike.\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "223020763880202331327745094787031161517", "category": "taxonomy", "turns": ["4 cars left San Fransisco at 12pm and they reached San Jose, 41.60 mi apart, at 1pm. How much time will be needed for 8 cars? Consider they have similar speed."], "reference": ["Let's analyze the situation step by step:\n\n1\\. We know that 4 cars traveled from San Francisco to San Jose, which is 41.60 miles apart.\n2\\. Each car travels at a similar speed. Having more cars on the road will not reduce the time that any one car takes to drive from San Francisco to San Jose.\n3\\. Therefore, the time it takes for each car to travel the distance is same, 1 hour.\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "35247899971565308536470213322269454259", "category": "taxonomy", "turns": ["Jane has 1 car. When she is at home then her car is in the driveway. Jane is now at her office. Can you find the number of car in her driveway now?\n"], "reference": ["Based on the information provided, we know that Jane has one car and that it is in the driveway when she is at home. However, when Jane is at her office, we know that she used her car for commuting. This implies that the car is not in the driveway. As Jane only has 1 car, then there must be no cars in Jane's driveway now. Therefore the answer is 0.\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "138091241521529880319109143777337935312", "category": "taxonomy", "turns": ["\"You are a teacher and need to give grade to the students. The grades are defined as follows:\\n\\nFor marks less than 40, grade is F.\\nFor marks between 40 and 60, grade is C.\\nFor marks between 60 and 80, grade is B.\\nFor marks above 80, grade is A.\\n\\nYou will be given a student's marks as input and you need to output the grade.\\n\\nMarks : 47\\nGrade:\\u00a0\\n\"\n"], "reference": ["C (The student's marks is between 40 and 60.)\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "213355609015880795968163047748418640088", "category": "taxonomy", "turns": ["You are a fitness trainer designing workout plans for clients. Based on number of hours a client exercised in a week, determine and output the appropriate fitness level:\n\nFor less than 3 hours, fitness level is Beginner.\nFor 3 to 5 hours, fitness level is Intermediate.\nFor 5 to 7 hours, fitness level is Advanced.\nFor more than 7 hours, fitness level is Expert.\n\nWeekly exercise hours: 4\nFitness level: "], "reference": ["Intermediate\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/general/qna.yaml", "question_id": "44339368633466322891100739551808735676", "category": "taxonomy", "turns": ["As a travel guide, you want to assess the experiences of tourists. Based on the number of landmarks visited by a tourist in a city, determine the travel exploration level using the following guidelines: \n\nFor 0 to 2 landmarks, exploration level is Casual. \nFor 3 to 5 landmarks, exploration level is Enthusiast. \nFor 6 to 8 landmarks, exploration level is Adventurer. \nFor more than 8 landmarks, exploration level is Explorer. \n\nNumber of landmarks visited: 4 \nExploration level:\u00a0\n"], "reference": ["Enthusiast\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/tabular/qna.yaml", "question_id": "234233870159341419842865244848953191242", "category": "taxonomy", "turns": ["Given the following table, predict the next value in the sequence:\n\nDay\tTemperature (\u00b0C)\n1\t20\n2\t22\n3\t25\n4\t28\n5\t?\n\n"], "reference": ["To predict the next value in the temperature sequence, we can look at the pattern in the previous values. The differences between consecutive temperatures are:\n\n2 (day 2 - day 1)\n3 (day 3 - day 2)\n3 (day 4 - day 3)\n4 (day 5 - day 4)\n\nThis increasing difference suggests that the temperature will continue to increase. To find the next temperature, we can add the difference from day 4 to the temperature of day 4:\n\nNext temperature = Day 4 temperature + Difference\n= 28 \u00b0C + 3 \u00b0C\n= 31 \u00b0C\n\nTherefore, the next value in the sequence is 31 degrees Celsius.\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/tabular/qna.yaml", "question_id": "212185784796983471810902311649092104842", "category": "taxonomy", "turns": ["You're an analyst examining the performance of a retail company's product lines across different regions. Analyze the sales data provided in the table below and identify the best-performing product line in each region. For example the best performing product in North is Product B. Similarly identify for other regions as well.\n\nRegion\tProduct A Sales (in $)\tProduct B Sales (in $)\tProduct C Sales (in $)\nNorth\t$10,000\t $12,000\t $9,000\nSouth $8,000\t $10,000\t $11,000\nEast\t $11,000\t $9,000\t $10,000\nWest\t$9,000\t $11,000\t $12,000"], "reference": ["North: Product A\nSouth: Product C\nEast: Product A\nWest: Product C\n"]} +{"qna_file": "foundational_skills/reasoning/logical_reasoning/tabular/qna.yaml", "question_id": "200656539618809357681888915956904200911", "category": "taxonomy", "turns": ["Consider the following table representing the number of fruits bought by three different people (A, B, C) at a fruit stall over three days (Day 1, Day 2, Day 3):\n\nPerson\tDay 1\tDay 2\tDay 3\nA\t5\t8\t4\nB\t3\t6\t7\nC\t2\t5\t6\nOn which day lowest number of fruits were brought?\n"], "reference": ["To calculate the total number of fruits bought on each day we first sum up the number of fruits bought in each day.\nTotal fruits for A = 5 (Day 1) + 8 (Day 2) + 4 (Day 3) = 17\nTotal fruits for B = 3 (Day 1) + 6 (Day 2) + 7 (Day 3) = 16\nTotal fruits for C = 2 (Day 1) + 5 (Day 2) + 6 (Day 3) = 13\n\nHence on Day 3 the lowest number of fruits were bought.\n"]} +{"qna_file": "compositional_skills/grounded/linguistics/inclusion/qna.yaml", "question_id": "204659194323600531923884250676008459715", "category": "taxonomy", "turns": ["Given the context below:\nIn database replication, the master database is regarded as the authoritative source, and the slave databases are synchronized to it.\n\nAnswer the following question: How would you rewrite this sentence to use more inclusive IT terminology?"], "reference": ["In database replication, the primary database is regarded as the authoritative source, and the secondary databases are syncrhonized to it.\n"]} +{"qna_file": "compositional_skills/grounded/linguistics/inclusion/qna.yaml", "question_id": "147993714453806909909096211808741570129", "category": "taxonomy", "turns": ["Given the context below:\nBlacklisting is the action of a group or authority compiling a blacklist of people, countries, or other entites to be avoided or distrusted as being deemed unacceptable to those making the list.\n\nAnswer the following question: Show me how to use inclusive IT terminology to replace non-inclusive IT terminology."], "reference": ["Denylisting is the action of a group or authority compiling a denylist of people, countries, or other entites to be avoided or distrusted as being deemed unacceptable to those making the list.\n"]} +{"qna_file": "compositional_skills/grounded/linguistics/inclusion/qna.yaml", "question_id": "277429344622403037414084489039911238029", "category": "taxonomy", "turns": ["Given the context below:\nIn database replication, the master database is regarded as the authoritative source, and the slave databases are synchronized to it.\n\nAnswer the following question: How would you revise this sentence to use Inclusive IT terminology?"], "reference": ["In database replication, the parent database is regarded as the authoritative source, and the child databases are synchronized to it.\n"]} +{"qna_file": "compositional_skills/grounded/linguistics/inclusion/qna.yaml", "question_id": "64126296154273337259917346475717789750", "category": "taxonomy", "turns": ["Given the context below:\nA use for whitelists is in local area network (LAN) security. Many network admins set up MAC address whitelists, or a MAC address filter, to control who is allowed on their networks.\n\nAnswer the following question: How would you revise this sentence to use inclusive IT terminology?"], "reference": ["A use for allowlists is in local area network (LAN) security. Many network admins set up MAC address allowlists, or a MAC address filter, to control who is allowed on their networks.\n"]} +{"qna_file": "compositional_skills/grounded/linguistics/inclusion/qna.yaml", "question_id": "321280271949880596087592371402638769489", "category": "taxonomy", "turns": ["Given the context below:\nA master clock that provides time signals used to synchronize one or more slave clocks as a part of a clock network.\n\nAnswer the following question: How would you improve this sentence to use inclusive IT language?"], "reference": ["A primary clock that provides time signals used to synchronize one or more secondary clocks as a part of a clock network.\n"]} +{"qna_file": "compositional_skills/grounded/linguistics/inclusion/qna.yaml", "question_id": "248490419131533185897870771102646364415", "category": "taxonomy", "turns": ["Given the context below:\nAs a verb, blacklist can mean to put an individual or entity on such a list. A blacklist is synonymous with a list of banned persons or organizations and is the opposite of a whitelist.\n\nAnswer the following question: How would you adjust the word choice to use Inclusive IT terminology?"], "reference": ["As a verb, denylist can mean to put an individual or entity on such a list. A denylist is synonymous with a list of banned persons or organizations and is the opposite of an allowlist.\n"]} +{"qna_file": "compositional_skills/grounded/linguistics/writing/rewriting/qna.yaml", "question_id": "238633164900752099735552926883202527302", "category": "taxonomy", "turns": ["Given the context below:\nThe elucidation of the intricate interplay between genetic predisposition and environmental factors remains a focal point of contemporary biomedical research.\nAnswer the following question: Could you rewrite this text in a simpler way?"], "reference": ["Scientists are studying how our genes and the world around us affect our health."]} +{"qna_file": "compositional_skills/grounded/linguistics/writing/rewriting/qna.yaml", "question_id": "174122476983468792307102554019419045119", "category": "taxonomy", "turns": ["Given the context below:\nThe exponential growth of technological innovation has led to the proliferation of digital platforms, fundamentally transforming the way we communicate and interact.\nAnswer the following question: Rewrite the text in an easier way"], "reference": ["Technology is growing fast, and now we use digital tools to talk and connect in new ways."]} +{"qna_file": "compositional_skills/grounded/linguistics/writing/rewriting/qna.yaml", "question_id": "21871337471668445961250757880425596179", "category": "taxonomy", "turns": ["Given the context below:\nThe elucidation of the implications of quantum mechanics for the behavior of subatomic particles has been a cornerstone of modern physics.\nAnswer the following question: Could you give me the text rewritten for beter understanding?"], "reference": ["Scientists have been figuring out how tiny particles act using quantum mechanics, a big idea in physics."]} +{"qna_file": "compositional_skills/grounded/linguistics/writing/rewriting/qna.yaml", "question_id": "162485359740395306896744836289122773929", "category": "taxonomy", "turns": ["Given the context below:\nThe articulation of ethical frameworks for the regulation of emerging biotechnologies presents a significant challenge for policymakers and bioethicists alike.\nAnswer the following question: Could you tell me in a simple way what the text is saying?"], "reference": ["It is hard for policymakers and bioethicists to make rules for new biotechnologies that everyone agrees are fair."]} +{"qna_file": "compositional_skills/grounded/linguistics/writing/rewriting/qna.yaml", "question_id": "247245587369965865464820575558388229689", "category": "taxonomy", "turns": ["Given the context below:\nThe synthesis of diverse perspectives from interdisciplinary fields is crucial for fostering innovation and addressing complex societal challenges.\nAnswer the following question: Rewrite the text in order to I understand better"], "reference": ["We need ideas from lots of different fields to solve big problems and come up with new stuff."]} +{"qna_file": "compositional_skills/linguistics/synonyms/qna.yaml", "question_id": "67389856385251023599658088082141248116", "category": "taxonomy", "turns": ["List a synonym for the word attend."], "reference": ["Synonym for Attend is take part in\n"]} +{"qna_file": "compositional_skills/linguistics/synonyms/qna.yaml", "question_id": "87323071940099672872407806669938221521", "category": "taxonomy", "turns": ["List two synonyms for the word attend and separate with newline."], "reference": ["Two synonyms for Attend are\ntake part in\nbe present at\n"]} +{"qna_file": "compositional_skills/linguistics/synonyms/qna.yaml", "question_id": "185565874496871755905054037667672710088", "category": "taxonomy", "turns": ["List three synonyms for the word attend and separate with newline."], "reference": ["Three synonyms for Attend are\ntake part in\nbe present at\nbe there at\n"]} +{"qna_file": "compositional_skills/linguistics/synonyms/qna.yaml", "question_id": "49135383463062508700889757119687351411", "category": "taxonomy", "turns": ["List four synonyms for the word beautiful and separate with newline."], "reference": ["Four synonyms for Attend are\ntake part in\nbe present at\nbe there at\nmake an appearance at\n"]} +{"qna_file": "compositional_skills/linguistics/synonyms/qna.yaml", "question_id": "26946401118177496314798576582015106460", "category": "taxonomy", "turns": ["List five synonyms for the word beautiful and separate with newline."], "reference": ["Five synonyms for Beautiful are\nattractive\npretty\ngorgeous\ngood-looking\nhandsome\n"]} +{"qna_file": "compositional_skills/linguistics/synonyms/qna.yaml", "question_id": "229460576059561440787647120322748799569", "category": "taxonomy", "turns": ["List five antonyms for the word attend and separate with newline."], "reference": ["Five antonyms for attend are\nabsent\nneglect\ndisregard\ndisown\nignore\n"]} diff --git a/mt_bench_branch_generator/mt_bench_branch/main/reference_answer/prometheus-eval/prometheus-8x7b-v2.0.jsonl b/mt_bench_branch_generator/mt_bench_branch/main/reference_answer/prometheus-eval/prometheus-8x7b-v2.0.jsonl new file mode 100644 index 0000000..e43ae6f --- /dev/null +++ b/mt_bench_branch_generator/mt_bench_branch/main/reference_answer/prometheus-eval/prometheus-8x7b-v2.0.jsonl @@ -0,0 +1,67 @@ +{"question_id": "186038087183832915305325427194848080180", "answer_id": "Y8c76fj4NZNt338srnGetV", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["Hello! I'm here to help you understand the order of arrival at a party. Let's analyze the situation step by step:\n\n1. Joe reached first (before you).\n2. You arrived next.\n3. David arrived after you.\n4. John reached last (after David).\n\nBased on this information, Joe was the first one to arrive at the party, followed by you, then David, and finally John.\n"]}], "tstamp": 1730406471.817908} +{"question_id": "215948836162377945439927729315702901781", "answer_id": "o49rStaFeS6i2CWsWEAteg", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["Hello! I'm here to help you understand the order of arrival at a celebration. Let's analyze the situation step by step:\n\n1. Emma reached first (before Peter).\n2. Peter arrived next.\n3. Sophia arrived after Peter.\n4. Olivia reached last (after Sophia).\n\nBased on this information, Emma was the first one to arrive at the celebration, followed by Peter, then Sophia, and finally Olivia.\n"]}], "tstamp": 1730406471.8179278} +{"question_id": "239330987684746270140406408262195855542", "answer_id": "byst3s9g7kziKDtvt6VtGx", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["Hello! I'm here to help you understand the order of photos in a photography competition. Let's analyze the situation step by step:\n\n1. Emily captured her photo first (before James).\n2. James captured a photo next.\n3. Alex entered with a photo after James.\n4. Sarah submitted her photo last (after Alex).\n\nBased on this information, Emily's photo was captured first, followed by Jame's entry, then Alex's photo, and finally Sarah's submission.\n\n"]}], "tstamp": 1730406471.817942} +{"question_id": "242886683457958175188717141363968452444", "answer_id": "7GYLuQcoqWWenJCKoNNCLK", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["A kilo of feathers is heavier than a pound of hammers. 1 kilo is equal to ~2.2046 pounds, so no matter what you are weighing 1 kilo will be heavier than 1 pound\n"]}], "tstamp": 1730406471.819107} +{"question_id": "233229534488351620628102146533821087135", "answer_id": "m67MHNxyxwT77J3bVd2knZ", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["100 servers. If 1 Administrator can manage 10 servers, and we increase that number by 10, we can assume that 10 administrators could handle 10 times the number of servers 10x10=100 \n"]}], "tstamp": 1730406471.8191218} +{"question_id": "238611654959537552555222005657320456359", "answer_id": "gB5DioL5aciUaqW7cKZD65", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["You need either 1 x 2 socket subscription, or 16 x 2-core subscriptions.\n"]}], "tstamp": 1730406471.819134} +{"question_id": "251187812289715631442369307574029139622", "answer_id": "d5ffLYRt3mchDxkNty8f6V", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["Sarah placed her shoes under her dress before she went shopping. Her sister borrowed the shoes and put them under Sarah\u2019s bed. Sarah doesn\u2019t know that her sister borrowed her shoes. Therefore, the answer is yes, she assumes that the shoes are still under her dress.\n"]}], "tstamp": 1730406471.8230479} +{"question_id": "13217000458772167243141483582150185270", "answer_id": "QGXQaHirGbYbU5RALvT7AM", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["When the Garcia family left, the game was tied, 3-3, and they expected the game to be postponed due to the rain. So, they don't know that the game ended during their commute with a score of 5-3. Therefore, the answer is no, they don't believe the score is 5-3.\n"]}], "tstamp": 1730406471.8230689} +{"question_id": "204625558555813571908604531813394942740", "answer_id": "PkzeYh2ncZ7CFmMLYZ7WTJ", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["When Lisa left Jacob, he was asleep on the beach. So, Lisa doesn't know that Jacob woke up and went swimming. Therefore, the answer is yes, Lisa believes that Jacob is still asleep on the beach.\n"]}], "tstamp": 1730406471.8230798} +{"question_id": "318549297918654447514356663453222375932", "answer_id": "cbs4tiBkHJ2ho52mbaLm56", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["Since Pam has been at work all day, she doesn't know that the tulips in her backyard have suddenly bloomed. Therefore, the answer is no, when driving home she doesn't assume that her tulips have bloomed.\n"]}], "tstamp": 1730406471.8230898} +{"question_id": "319839001852604230671498528924789687512", "answer_id": "E4TdnbVa2EdHQYZ4neRz6T", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["The scenario mentions that Jeff put on a pink shirt instead of a white one. This means he wanted to put on a white shirt but since he is colorblind and can't tell the difference, he mistakenly put on a pink shirt. He doesn't know we is wearing a pink short. Therefore, the answer is yes, he think he is wearing a white shirt.\n"]}], "tstamp": 1730406471.8230999} +{"question_id": "44149513413682805844664797051944968063", "answer_id": "Rd7BVKm4x5HkQmzzZ8SbTH", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["The scenario mentions that the lab was empty that night. Therefore, the answer is yes, the number of people in the lab that night is zero.\n"]}], "tstamp": 1730406471.8231099} +{"question_id": "63498394932145751423557426078215489111", "answer_id": "L2psAhkHcFzztExWCBjkGC", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["The scenario mentions that the country\u2019s economic success were recorded in books from the early 1900s. Therefore, the answer is yes, early 1900s novels portray the country as experiencing economic wealth.\n"]}], "tstamp": 1730406471.823118} +{"question_id": "205308421853532842346513294370366642610", "answer_id": "ipipdEdYuAWZHcKxdpQ3bz", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["The scenario mentions that a part of garden is reserved for roses and it has a label showing that. Therefore, the answer is yes, the label say the flowers are roses.\n"]}], "tstamp": 1730406471.823126} +{"question_id": "310855955988527299360696225432626887530", "answer_id": "HVBGcJMiCQnmU2SCya2jJC", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["No that is incorrect, In golf, the objective is to score as few points as possible through various methods such as driving, chipping, and putting. The player with the lowest score at the end of the round wins.\n"]}], "tstamp": 1730406471.824209} +{"question_id": "78736441356384569630737498464735933862", "answer_id": "PaLBzf5CDnbVCgoK6P9sPK", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["In rummy, the player with the lowest score wins. \n"]}], "tstamp": 1730406471.824218} +{"question_id": "118971624301146257272024515530524869077", "answer_id": "7QACrE2QZDDwTAYRa24Cin", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["In most running competitions, including marathons, the winner is the runner who completes the racecourse in the shortest amount of time. So, in this case, the runner with a time of 4 hours and 20 minutes would win because they completed the race faster than the runner with a time of 4 hours and 30 minutes.\n"]}], "tstamp": 1730406471.824225} +{"question_id": "292983752102232095864714663481939268406", "answer_id": "Y356d42UVTZ3K7qtBwHwY7", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["The answer is 3 killers. A person comes into the room and kills one of the 3 killers, so 2 of the 3 killers remain. However the new person, having killed one of the killers, is now a killer. Hence there are 3 killers in the room. \n"]}], "tstamp": 1730406471.8254662} +{"question_id": "79697302777874382304266575075184254147", "answer_id": "6V9QSP8hveY386HPvU7kRH", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["The answer is 4 hours. Since I am drying the shirts in the sun, it does not matter if I dry 4 or 8 shirts, it will take the same amount of time.\n"]}], "tstamp": 1730406471.825475} +{"question_id": "320590730897033119915839694782416720843", "answer_id": "fTz4UMKMa8GZPZ8PvHoRA5", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["The ball is now in the box. Jack put the ball in the basket. After he left, Jill put the ball in the box, so it is now in the box.\n"]}], "tstamp": 1730406471.825484} +{"question_id": "38178989809535383561614815864559057197", "answer_id": "Bi8ZvEwi7SRTizUMgsfcjz", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["Here is a meaningful sequence:\n1. Crime\n2. Police\n3. Judge\n4. Judgment\n5. Punishment\n"]}], "tstamp": 1730406471.82654} +{"question_id": "6767681416681346381744048511682034386", "answer_id": "DCyksmBYVuiuUQv7dmoKQT", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["Here is a meaningful sequence:\n1. Infection\n2. Doctor\n3. Consultation\n4. Treatment\n5. Recovery\n"]}], "tstamp": 1730406471.8265479} +{"question_id": "53622117114584765484861387700826501422", "answer_id": "7NZSRcyqqUfWUyCQiy9NKh", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["Here is a meaningful sequence:\n1. Letters\n2. Word\n3. Phrase\n4. Sentence\n5. Paragraph\n"]}], "tstamp": 1730406471.8265548} +{"question_id": "324795436124537533142216411539653654022", "answer_id": "oTCNwQsp4HA56qTv7epEsZ", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["C. important. Remaining are synonyms of each other.\n"]}], "tstamp": 1730406471.827379} +{"question_id": "286487527560524977650000710931754090809", "answer_id": "CPyQg2dnJDTvHrq3hKkBrb", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["D. Book. Rest are all parts of a book.\n"]}], "tstamp": 1730406471.8273869} +{"question_id": "193212596901443399313689097544179034637", "answer_id": "LtvZyTSRNaJ9LK6fdypwgj", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["A. hate. Rest are positive emotions.\n"]}], "tstamp": 1730406471.827394} +{"question_id": "9376129652505313748988900880362788597", "answer_id": "8bQxne8eshVFFQ7DsSgPPC", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["The list contains two distinct colors - blue and pink.\n"]}], "tstamp": 1730406471.828307} +{"question_id": "95202116628166667051208889618180112758", "answer_id": "eYjpY4JNjDWavf5zuNPV6Q", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["There are 2 human body parts: Bone and tongue. Depending on if you consider nails to be fingernails, then you could also include them as a third body part.\n"]}], "tstamp": 1730406471.8283148} +{"question_id": "255872237613884807711965944467225129033", "answer_id": "RAi6kYyxKDPRmf7e4aYHHz", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["The following items in the list are associated with both humans and animals: intestine, cheating. \n"]}], "tstamp": 1730406471.828322} +{"question_id": "225571771206761594158263437563717696010", "answer_id": "PQyjWDFyi4pBCH47HCdJhX", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["While days tend to be longer in the summer, just because it is not summer doesn't mean days are necessarily shorter.\n"]}], "tstamp": 1730406471.829186} +{"question_id": "31075242281634831797237789507450794676", "answer_id": "iQnP4xa4u5mCFu9vxNwMNM", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["No, we cannot conclusively conclude that some cats are black based solely on the given premises. The statement \"some mammals are black\" does not necessarily guarantee that among those mammals are cats.\n"]}], "tstamp": 1730406471.8291929} +{"question_id": "186605568946423368062781809941052703119", "answer_id": "3GNkyYv3FuYX2QzQfRqCb4", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["Yes, we can conclude that all squares have four sides based on the given premises.\n"]}], "tstamp": 1730406471.8291998} +{"question_id": "122000398825150483085330538623944234347", "answer_id": "6C2YQ9WMm72EkyvQijxFDK", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["If you have one bun and need to share it among 7 kids without splitting it, each child would receive 1/7 of the bun. So the split fraction would be 1/7.\n"]}], "tstamp": 1730406471.83358} +{"question_id": "321250237548041497069373588875103125878", "answer_id": "NYtkPATucqPPiPpVAZmaRz", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["The doctor instructs you to take one pill every half hour. So, you would take the first pill immediately, the second pill after 30 minutes, and the third pill after another 30 minutes, totaling one hour. After one hour, all three pills would have been consumed.\n"]}], "tstamp": 1730406471.833588} +{"question_id": "199975546227160776896763216962472780767", "answer_id": "ejgnE9GHVN9DoTe5o6aLPz", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["The answer to this question is still 5 minutes.\nThe rate at which the widgets are produced remains constant regardless of the number of machines. Each machine produces one widget in 5 minutes, so whether there are 5 machines or 100 machines, it would still take 5 minutes to produce 100 widgets.\n"]}], "tstamp": 1730406471.833594} +{"question_id": "248025386135807391370680093872004527973", "answer_id": "U4AxFfGi4cgWX4nqTDzv7Z", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["The odd one out is Moon. All of the others are planets from the solar system, but Moon is a satillite that orbits around the Earth.\n"]}], "tstamp": 1730406471.8336} +{"question_id": "163998429574116457272751395628485792572", "answer_id": "98p9QpENbpY5YFVZNZEFYg", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["A is D's aunt. \nD is the brother of E\nE is the daughter of B, making D the son of B\nB is the sister of A, making A the aunt of both D and E. \n"]}], "tstamp": 1730406471.833606} +{"question_id": "283956719709768162058781448709604191788", "answer_id": "e7Ti65YnpXuBAG2aAXDySV", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["6\\. The series is dividing the the number in half at each step\n"]}], "tstamp": 1730406471.833612} +{"question_id": "76341820815379879552305133803024133009", "answer_id": "jv8viL2tTMyj65xPp3dHxs", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["Sam doesn't run faster than Jane as Jane runs faster than Joe and Joe runs faster than Sam.\n"]}], "tstamp": 1730406471.8336189} +{"question_id": "285694428740467711736111444912935277221", "answer_id": "U9dYazNVzmAn2zCpAymzth", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["When drying clothes outside, several factors can affect the drying time, such as temperature, humidity, wind, and the number of shirts.\nIf there's sufficient space for drying shirts in parallel, drying 20 shirts could take as long as drying 5 shirts so 4 hours. However, when there's only sufficient space for 5 shirts, you'll need to dry the shirts in batches so considering that you need to dry 20 shirts, you would need 4 batches bringing the total time to 16 hours.\n\n"]}], "tstamp": 1730406471.833625} +{"question_id": "171343142260026728274898175026241576716", "answer_id": "E7zCgj7kAaNPLgPtj3cixq", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["Marc thinks the ball is in the basket as that's where it was when he left the room. John, on the other hand, thinks the ball is in the cupboard where he put it before he left the room.\n"]}], "tstamp": 1730406471.833632} +{"question_id": "97084977809626648492150587285270862247", "answer_id": "kZJ3GdMrrFpR5wpqyYGZSH", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["Based on the information provided, the names of Mike's mom's four kids are Luis, Drake, Matilda, and Mike. Therefore the name of the 4th kid is Mike.\n"]}], "tstamp": 1730406471.833638} +{"question_id": "223020763880202331327745094787031161517", "answer_id": "acJzKBJrkyGVcodtFQAELk", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["Let's analyze the situation step by step:\n\n1\\. We know that 4 cars traveled from San Francisco to San Jose, which is 41.60 miles apart.\n2\\. Each car travels at a similar speed. Having more cars on the road will not reduce the time that any one car takes to drive from San Francisco to San Jose.\n3\\. Therefore, the time it takes for each car to travel the distance is same, 1 hour.\n"]}], "tstamp": 1730406471.833644} +{"question_id": "35247899971565308536470213322269454259", "answer_id": "nZSHavnkP8K3QCUETYNSiq", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["Based on the information provided, we know that Jane has one car and that it is in the driveway when she is at home. However, when Jane is at her office, we know that she used her car for commuting. This implies that the car is not in the driveway. As Jane only has 1 car, then there must be no cars in Jane's driveway now. Therefore the answer is 0.\n"]}], "tstamp": 1730406471.8336508} +{"question_id": "138091241521529880319109143777337935312", "answer_id": "LqhpybG4DojtM22iyRMuaV", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["C (The student's marks is between 40 and 60.)\n"]}], "tstamp": 1730406471.8336582} +{"question_id": "213355609015880795968163047748418640088", "answer_id": "H2gzEQWLEgv4gpEcU9gsoT", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["Intermediate\n"]}], "tstamp": 1730406471.833664} +{"question_id": "44339368633466322891100739551808735676", "answer_id": "DCtkfovkPXh7vNQDJwJjou", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["Enthusiast\n"]}], "tstamp": 1730406471.833672} +{"question_id": "234233870159341419842865244848953191242", "answer_id": "hoq2e9UBdV2pYat4GcsMgY", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["To predict the next value in the temperature sequence, we can look at the pattern in the previous values. The differences between consecutive temperatures are:\n\n2 (day 2 - day 1)\n3 (day 3 - day 2)\n3 (day 4 - day 3)\n4 (day 5 - day 4)\n\nThis increasing difference suggests that the temperature will continue to increase. To find the next temperature, we can add the difference from day 4 to the temperature of day 4:\n\nNext temperature = Day 4 temperature + Difference\n= 28 \u00b0C + 3 \u00b0C\n= 31 \u00b0C\n\nTherefore, the next value in the sequence is 31 degrees Celsius.\n"]}], "tstamp": 1730406471.8351731} +{"question_id": "212185784796983471810902311649092104842", "answer_id": "amE9aTNc6bxsbJJsotwpJA", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["North: Product A\nSouth: Product C\nEast: Product A\nWest: Product C\n"]}], "tstamp": 1730406471.835181} +{"question_id": "200656539618809357681888915956904200911", "answer_id": "kifpPuG8R3NQaqB9u7R4BP", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["To calculate the total number of fruits bought on each day we first sum up the number of fruits bought in each day.\nTotal fruits for A = 5 (Day 1) + 8 (Day 2) + 4 (Day 3) = 17\nTotal fruits for B = 3 (Day 1) + 6 (Day 2) + 7 (Day 3) = 16\nTotal fruits for C = 2 (Day 1) + 5 (Day 2) + 6 (Day 3) = 13\n\nHence on Day 3 the lowest number of fruits were bought.\n"]}], "tstamp": 1730406471.835187} +{"question_id": "204659194323600531923884250676008459715", "answer_id": "icm2n3PKLxrXHdYvLHHWaH", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["In database replication, the primary database is regarded as the authoritative source, and the secondary databases are syncrhonized to it.\n"]}], "tstamp": 1730406471.836849} +{"question_id": "147993714453806909909096211808741570129", "answer_id": "EoZh6oTovYwwMDgzhgkgSa", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["Denylisting is the action of a group or authority compiling a denylist of people, countries, or other entites to be avoided or distrusted as being deemed unacceptable to those making the list.\n"]}], "tstamp": 1730406471.836857} +{"question_id": "277429344622403037414084489039911238029", "answer_id": "dweqsjMeuTVoPDh7avjBsC", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["In database replication, the parent database is regarded as the authoritative source, and the child databases are synchronized to it.\n"]}], "tstamp": 1730406471.836863} +{"question_id": "64126296154273337259917346475717789750", "answer_id": "44vEpAWXkwATVWRtgoD93n", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["A use for allowlists is in local area network (LAN) security. Many network admins set up MAC address allowlists, or a MAC address filter, to control who is allowed on their networks.\n"]}], "tstamp": 1730406471.83687} +{"question_id": "321280271949880596087592371402638769489", "answer_id": "PeXcmYYKWuK24zp9EKoJnV", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["A primary clock that provides time signals used to synchronize one or more secondary clocks as a part of a clock network.\n"]}], "tstamp": 1730406471.836876} +{"question_id": "248490419131533185897870771102646364415", "answer_id": "J3CAWkxvsvZx3MfZGriGGY", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["As a verb, denylist can mean to put an individual or entity on such a list. A denylist is synonymous with a list of banned persons or organizations and is the opposite of an allowlist.\n"]}], "tstamp": 1730406471.8368819} +{"question_id": "238633164900752099735552926883202527302", "answer_id": "2FsKiwghvmNrVc8jWsfxUM", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["Scientists are studying how our genes and the world around us affect our health."]}], "tstamp": 1730406471.8382678} +{"question_id": "174122476983468792307102554019419045119", "answer_id": "FoYiZmNKzHkWvg8aiEL6aH", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["Technology is growing fast, and now we use digital tools to talk and connect in new ways."]}], "tstamp": 1730406471.838274} +{"question_id": "21871337471668445961250757880425596179", "answer_id": "JCtbYbaRW4pKJLJKzdKbn4", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["Scientists have been figuring out how tiny particles act using quantum mechanics, a big idea in physics."]}], "tstamp": 1730406471.83828} +{"question_id": "162485359740395306896744836289122773929", "answer_id": "Bk6soxw4v482QH5NTBvk9L", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["It is hard for policymakers and bioethicists to make rules for new biotechnologies that everyone agrees are fair."]}], "tstamp": 1730406471.838287} +{"question_id": "247245587369965865464820575558388229689", "answer_id": "SCptdqz9M4hrsEG8BLPn7K", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["We need ideas from lots of different fields to solve big problems and come up with new stuff."]}], "tstamp": 1730406471.838293} +{"question_id": "67389856385251023599658088082141248116", "answer_id": "7uj9QPeRbNaVyBeEwETBFz", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["Synonym for Attend is take part in\n"]}], "tstamp": 1730406471.83939} +{"question_id": "87323071940099672872407806669938221521", "answer_id": "2MuSKwFW5cEgCqRKpBPVzg", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["Two synonyms for Attend are\ntake part in\nbe present at\n"]}], "tstamp": 1730406471.839396} +{"question_id": "185565874496871755905054037667672710088", "answer_id": "67sG8ggdipDF8WKm25YxRS", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["Three synonyms for Attend are\ntake part in\nbe present at\nbe there at\n"]}], "tstamp": 1730406471.839402} +{"question_id": "49135383463062508700889757119687351411", "answer_id": "cAzak7jswSmArZqJWhdLzX", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["Four synonyms for Attend are\ntake part in\nbe present at\nbe there at\nmake an appearance at\n"]}], "tstamp": 1730406471.8394082} +{"question_id": "26946401118177496314798576582015106460", "answer_id": "6Z8XSqbUaMvQBtwrsnXNGk", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["Five synonyms for Beautiful are\nattractive\npretty\ngorgeous\ngood-looking\nhandsome\n"]}], "tstamp": 1730406471.8394141} +{"question_id": "229460576059561440787647120322748799569", "answer_id": "UsUSxmVXAURRNPAY36BXmM", "model_id": "prometheus-eval/prometheus-8x7b-v2.0", "choices": [{"index": 0, "turns": ["Five antonyms for attend are\nabsent\nneglect\ndisregard\ndisown\nignore\n"]}], "tstamp": 1730406471.8394198} diff --git a/scripts/functional-tests.sh b/scripts/functional-tests.sh new file mode 100755 index 0000000..ea5574b --- /dev/null +++ b/scripts/functional-tests.sh @@ -0,0 +1,62 @@ +#!/usr/bin/env bash +# SPDX-License-Identifier: Apache-2.0 +# +# This test script is laid out as follows: +# - UTILITIES: utility functions +# - TESTS: test functions +# - SETUP: environment setup steps +# - MAIN: test execution steps +# +# If you are running locally and calling the script multiple times you may want to run like this: +# +# TEST_DIR=/tmp/foo ./scripts/functional-tests.sh + +set -ex + +############# +# UTILITIES # +############# + +clone_taxonomy(){ + if [ ! -d taxonomy ]; then + git clone https://github.com/instructlab/taxonomy.git + fi +} + +######### +# TESTS # +######### + +test_branch_generator(){ + python3 ${SCRIPTDIR}/test_branch_generator.py --test-dir "${TEST_DIR}" +} + +######### +# SETUP # +######### + +# shellcheck disable=SC2155 +export SCRIPTDIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd ) +# build a prompt string that includes the time, source file, line number, and function name +export PS4='+$(date +"%Y-%m-%d %T") ${BASH_VERSION}:${BASH_SOURCE}:${LINENO}: ${FUNCNAME[0]:+${FUNCNAME[0]}(): }' + +# Support overriding the test directory for local testing otherwise creates a temporary directory +TEST_DIR=${TEST_DIR:-$(mktemp -d)} + +export TEST_DIR +export PACKAGE_NAME='instructlab-eval' + + +######## +# MAIN # +######## + +pushd $TEST_DIR + +clone_taxonomy + +test_branch_generator + + +popd +exit 0 diff --git a/tests/test_branch_gen_answers.py b/scripts/test_branch_gen_answers.py similarity index 100% rename from tests/test_branch_gen_answers.py rename to scripts/test_branch_gen_answers.py diff --git a/scripts/test_branch_generator.py b/scripts/test_branch_generator.py new file mode 100755 index 0000000..01402f9 --- /dev/null +++ b/scripts/test_branch_generator.py @@ -0,0 +1,35 @@ +# Standard +import argparse +import os + +# First Party +from instructlab.eval import mt_bench_branch_generator + + +def test_mt_bench_branch_generator(test_dir): + output_dir = os.path.join(test_dir, "mt_bench_branch_generator") + mt_bench_branch_generator.generate( + "prometheus-eval/prometheus-8x7b-v2.0", + "main", + "taxonomy", + output_dir, + ) + main_dir = os.path.join(output_dir, "mt_bench_branch", "main") + assert os.path.isfile(os.path.join(main_dir, "question.jsonl")) + assert os.path.isfile( + os.path.join( + main_dir, + "reference_answer", + "prometheus-eval", + "prometheus-8x7b-v2.0.jsonl", + ) + ) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Test Branch Generator") + parser.add_argument("--test-dir", help="Base test working directory") + args = parser.parse_args() + test_dir = args.test_dir + + test_mt_bench_branch_generator(test_dir) diff --git a/tests/test_branch_judge_answers.py b/scripts/test_branch_judge_answers.py similarity index 100% rename from tests/test_branch_judge_answers.py rename to scripts/test_branch_judge_answers.py diff --git a/tests/test_gen_answers.py b/scripts/test_gen_answers.py similarity index 100% rename from tests/test_gen_answers.py rename to scripts/test_gen_answers.py diff --git a/tests/test_judge_answers.py b/scripts/test_judge_answers.py similarity index 100% rename from tests/test_judge_answers.py rename to scripts/test_judge_answers.py diff --git a/scripts/test_mmlu.py b/scripts/test_mmlu.py new file mode 100755 index 0000000..2db46c0 --- /dev/null +++ b/scripts/test_mmlu.py @@ -0,0 +1,21 @@ +# First Party +from instructlab.eval.mmlu import MMLUEvaluator + + +def test_minimal_mmlu(): + print("===> Executing 'test_minimal_mmlu'...") + try: + model_path = "instructlab/granite-7b-lab" + tasks = ["mmlu_anatomy", "mmlu_astronomy"] + mmlu = MMLUEvaluator(model_path=model_path, tasks=tasks) + overall_score, individual_scores = mmlu.run() + print(overall_score) + print(individual_scores) + except Exception as exc: + print(f"'test_minimal_mmlu' failed: {exc}") + return False + return True + + +if __name__ == "__main__": + assert test_minimal_mmlu() == True diff --git a/tests/test_mmlubranch.py b/scripts/test_mmlu_branch.py similarity index 76% rename from tests/test_mmlubranch.py rename to scripts/test_mmlu_branch.py index 9344ccf..81c49d8 100755 --- a/tests/test_mmlubranch.py +++ b/scripts/test_mmlu_branch.py @@ -9,10 +9,12 @@ def test_mmlu_branch(): print("===> Executing 'test_mmlu_branch'...") try: model_path = "instructlab/granite-7b-lab" - sdg_path = f"{os.path.dirname(os.path.realpath(__file__))}/testdata/sdg" + tasks_dir = ( + f"{os.path.dirname(os.path.realpath(__file__))}/../tests/testdata/sdg" + ) tasks = ["mmlu_pr"] mmlu = MMLUBranchEvaluator( - model_path=model_path, sdg_path=sdg_path, tasks=tasks + model_path=model_path, tasks_dir=tasks_dir, tasks=tasks ) overall_score, individual_scores = mmlu.run() print(overall_score) diff --git a/src/instructlab/eval/mmlu.py b/src/instructlab/eval/mmlu.py index 6530497..f893b66 100644 --- a/src/instructlab/eval/mmlu.py +++ b/src/instructlab/eval/mmlu.py @@ -149,7 +149,7 @@ def run(self, server_url: str | None = None) -> tuple: "stderr": float(result["acc_stderr,none"]), } - overall_score = float(agg_score / len(self.tasks)) + overall_score = float(agg_score / len(individual_scores)) return overall_score, individual_scores diff --git a/src/instructlab/eval/mt_bench.py b/src/instructlab/eval/mt_bench.py index 3b24aa8..0adc631 100644 --- a/src/instructlab/eval/mt_bench.py +++ b/src/instructlab/eval/mt_bench.py @@ -27,6 +27,8 @@ logger = setup_logger(__name__) +MIN_WORKERS_PER_GPU = 10 + class AbstractMTBenchEvaluator(Evaluator): """ @@ -66,7 +68,7 @@ def _calc_max_workers( # Tune max_workers based on hardware configuration: min(#GPUs being used * 10, #CPU cores) # Please see https://github.com/instructlab/instructlab/issues/2050 for detailed explanation calculated_max_workers = min( - max(serving_gpus, 1) * 10, usable_cpu_count + max(serving_gpus, 1) * MIN_WORKERS_PER_GPU, usable_cpu_count ) logger.debug( "Auto tuning max_workers to %s", calculated_max_workers @@ -90,7 +92,7 @@ def _get_effective_max_workers(self, max_workers, serving_gpus): if max_workers is not None: effective_max_workers = self._calc_max_workers(max_workers, serving_gpus) else: - effective_max_workers = self.max_workers + effective_max_workers = MIN_WORKERS_PER_GPU return effective_max_workers diff --git a/tests/test_mmlu.py b/tests/test_mmlu.py old mode 100755 new mode 100644 index 2db46c0..bdf4f90 --- a/tests/test_mmlu.py +++ b/tests/test_mmlu.py @@ -1,21 +1,69 @@ +# SPDX-License-Identifier: Apache-2.0 + +# Standard +from unittest import mock +from unittest.mock import patch +import os + # First Party -from instructlab.eval.mmlu import MMLUEvaluator - - -def test_minimal_mmlu(): - print("===> Executing 'test_minimal_mmlu'...") - try: - model_path = "instructlab/granite-7b-lab" - tasks = ["mmlu_anatomy", "mmlu_astronomy"] - mmlu = MMLUEvaluator(model_path=model_path, tasks=tasks) - overall_score, individual_scores = mmlu.run() - print(overall_score) - print(individual_scores) - except Exception as exc: - print(f"'test_minimal_mmlu' failed: {exc}") - return False - return True - - -if __name__ == "__main__": - assert test_minimal_mmlu() == True +from instructlab.eval.mmlu import MMLUBranchEvaluator, MMLUEvaluator + +MMLU_EXAMPLE_OUTPUT = { + "results": { + "mmlu_astronomy": { + "alias": "astronomy", + "acc,none": 0.5592105263157895, + "acc_stderr,none": 0.04040311062490436, + }, + "mmlu_anatomy": { + "alias": "anatomy", + "acc,none": 0.4444444444444444, + "acc_stderr,none": 0.04292596718256981, + }, + "mmlu_abstract_algebra": { + "alias": "abstract_algebra", + "acc,none": 0.35, + "acc_stderr,none": 0.047937248544110196, + }, + }, +} + +MODEL_EXAMPLE = "instructlab/granite-7b-lab" + + +def assert_example_mmlu_individual_scores(overall_score, individual_scores): + assert round(overall_score, 2) == 0.45 + assert individual_scores == { + "mmlu_abstract_algebra": {"score": 0.35, "stderr": 0.047937248544110196}, + "mmlu_anatomy": {"score": 0.4444444444444444, "stderr": 0.04292596718256981}, + "mmlu_astronomy": {"score": 0.5592105263157895, "stderr": 0.04040311062490436}, + } + + +@patch( + "instructlab.eval.mmlu.AbstractMMLUEvaluator._simple_evaluate_with_error_handling", + return_value=MMLU_EXAMPLE_OUTPUT, +) +def test_mmlu_branch(eval_mock): + tasks_dir = f"{os.path.dirname(os.path.realpath(__file__))}/testdata/sdg" + tasks = ["mmlu_pr"] + mmlu = MMLUBranchEvaluator( + model_path=MODEL_EXAMPLE, tasks_dir=tasks_dir, tasks=tasks + ) + overall_score, individual_scores = mmlu.run() + + assert_example_mmlu_individual_scores(overall_score, individual_scores) + eval_mock.assert_called() + + +@patch( + "instructlab.eval.mmlu.AbstractMMLUEvaluator._simple_evaluate_with_error_handling", + return_value=MMLU_EXAMPLE_OUTPUT, +) +def test_mmlu(eval_mock): + tasks = ["mmlu_anatomy", "mmlu_astronomy", "mmlu_algebra"] + mmlu = MMLUEvaluator(model_path=MODEL_EXAMPLE, tasks=tasks) + overall_score, individual_scores = mmlu.run() + + eval_mock.assert_called() + assert_example_mmlu_individual_scores(overall_score, individual_scores) diff --git a/tests/test_mt_bench.py b/tests/test_mt_bench.py new file mode 100644 index 0000000..ec767c9 --- /dev/null +++ b/tests/test_mt_bench.py @@ -0,0 +1,103 @@ +# SPDX-License-Identifier: Apache-2.0 + +# Standard +from unittest import mock +from unittest.mock import patch + +# First Party +from instructlab.eval.mt_bench import MTBenchBranchEvaluator, MTBenchEvaluator + + +def gen_qa_pairs(odd): + i = 1 + qa_pairs = [] + score = 0 + while i < 5: + if i % 2: + if odd: + score = 0.2 + else: + score = 0.1 + elif not i % 2: + if odd: + score = 0.3 + else: + score = 0.4 + qa_pairs.append( + { + "question_id": i, + "score": score, + "qna_file": f"category{i}/qna.yaml", + } + ) + i = i + 1 + qa_pairs.append( + { + "question_id": i, + "score": 0.5, + "qna_file": f"category{i}/qna.yaml", + } + ) + if odd: + qa_pairs.append( + { + "question_id": i + 1, + "score": 0.6, + "qna_file": f"category{i+1}/qna.yaml", + } + ) + return qa_pairs + + +@patch("instructlab.eval.mt_bench_branch_generator.generate") +@patch("instructlab.eval.mt_bench_answers.generate_answers") +@patch( + "instructlab.eval.mt_bench_judgment.generate_judgment", + return_value=(0, gen_qa_pairs(True), None, 0), +) +def test_mt_bench_branch(gen_judgment_mock, gen_answers_mock, generate_mock): + mt_bench_branch = MTBenchBranchEvaluator( + "instructlab/granite-7b-lab", + "prometheus-eval/prometheus-8x7b-v2.0", + "../taxonomy", + "main", + ) + mt_bench_branch.gen_answers( + "http://localhost:8000/v1", + ) + overall_score, qa_pairs, error_rate = mt_bench_branch.judge_answers( + "http://localhost:8000/v1", + ) + assert overall_score == 0 + assert qa_pairs == gen_qa_pairs(True) + assert error_rate == 0 + + gen_judgment_mock.assert_called() + gen_answers_mock.assert_called() + generate_mock.assert_called() + + +@patch("instructlab.eval.mt_bench_answers.generate_answers") +@patch( + "instructlab.eval.mt_bench_judgment.generate_judgment", + return_value=(1.5001, [{}, {}], [1.002, 2], 0), +) +def test_mt_bench(gen_judgment_mock, gen_answers_mock): + mt_bench = MTBenchEvaluator( + "instructlab/granite-7b-lab", + "prometheus-eval/prometheus-8x7b-v2.0", + ) + mt_bench.gen_answers( + "http://localhost:8000/v1", + ) + overall_score, qa_pairs, turn_scores, error_rate = mt_bench.judge_answers( + "http://localhost:8000/v1", + ) + + assert overall_score == 1.5001 + assert qa_pairs == [{}, {}] + assert turn_scores == [1.002, 2] + assert error_rate == 0 + + gen_judgment_mock.assert_called() + gen_answers_mock.assert_called() diff --git a/tox.ini b/tox.ini index 194e693..5d41cb6 100644 --- a/tox.ini +++ b/tox.ini @@ -3,9 +3,35 @@ [tox] # py3-unit runs unit tests with 'python3' # py311-unit runs the same tests with 'python3.11' -envlist = ruff, lint, mypy, spellcheck +envlist = ruff, lint, mypy, spellcheck, py3-{unit, functional} minversion = 4.4 +[testenv] +description = run tests (unit, unitcov, functional) +passenv = + CMAKE_ARGS +# Use PyTorch CPU build instead of CUDA build in test envs. CUDA dependencies +# are huge. This reduces venv from 5.7 GB to 1.5 GB. +setenv = + PIP_EXTRA_INDEX_URL=https://download.pytorch.org/whl/cpu + CMAKE_ARGS={env:CMAKE_ARGS:-DLLAMA_NATIVE=off} + ILAB_MAX_STABLE_VRAM_WAIT=0 +package = wheel +wheel_build_env = pkg +# equivalent to `pip install instructlab[cpu]` +extras = cpu +deps = + pytest + pytest-asyncio + pytest-cov + pytest-html +commands = + unit: {envpython} -m pytest {posargs:tests} + unitcov: {envpython} -W error::UserWarning -m pytest --cov=instructlab.eval --cov-report term --cov-report=html:coverage-{env_name} --cov-report=xml:coverage-{env_name}.xml --html=durations/{env_name}.html {posargs:tests -m "not (examples or slow)"} + functional: ./scripts/functional-tests.sh +allowlist_externals = + functional: ./scripts/functional-tests.sh + # format, check, and linting targets don't build and install the project to # speed up testing. [testenv:lint] @@ -59,3 +85,20 @@ deps = pytest commands = mypy src + +[testenv:py3] +basepython = python3.11 + +[testenv:py3-unit] +basepython = {[testenv:py3]basepython} + +[testenv:py3-functional] +basepython = {[testenv:py3]basepython} +passenv = + {[testenv]passenv} + TEST_DIR + +[gh] +python = + 3.11 = py311-{unitcov, functional} + 3.10 = py310-{unitcov, functional}