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Pipeline for KoSBi

Neutral Contexts Generation

1. Context Generation

Before generating contexts, you need:

  • HyperCLOVA API url and API key
  • Targeted demographic category and groups
    • used in KoSBi is in pipeline/kosbi/kosbi_demographic_groups.json
    • As mentioned in the paper, we generated dataset for 72 targeted demographic groups across 15 categories.
  • Initial demonstration pool for prompting
    • Ours is in pipeline/kosbi/demo/kosbi_context_iter0.json

We used 10 demonstration samples of contexts when generating KoSBi. Among them, 5 demonstrations are from the pool with the same demographic category as what we are generating, and 3 are from the pool with the same demographic group. Of course, you can change these numbers in your generation pipeline.

Here is a code for generating contexts:

python pipeline/kosbi/generate_contexts.py \
    --api_url {API_url} \
    --api_key {API_key} \
    --demo_fpath pipeline/kosbi/demo/kosbi_context_iter0.json \
    --num_total_demos 10 \
    --num_in_cat_demos 5 \
    --num_in_grp_demos 3 \
    --demographic_list pipeline/kosbi/kosbi_demographic_groups.json \
    --output_dir data/generations \
    --output_fname output_contexts.json

2. Filtering: Remove Contexts NOT Including Target Group

We need to include target group words in the contexts we are generating, so we should remove contexts that do not have them. To remove, you need a finetuned classifier trained to classify the contexts that don't include target group words. If there is no classifier (e.g., at the first iteration), skip the argument --filter_model_ckpt.

python pipeline/kosbi/filter_context.py \
    --input_fname data/generations/output_contexts.json \
    --filter_model_ckpt {filter_model.ckpt} \
    --output_dir data/generations \
    --output_fname filtered_contexts.json

3. Human Annotation

After annotated from human annotators, each data points should contain 3 labels:

  • include? = {0,1} : whether the context contains target group
  • context_label : the label of the context is one of {safe,unsafe,undecided}
    • undecided is assigned when annotators don't agree
  • context_sub_label : if labeled unsafe, the context is further labeled as
    1. stereotype
    2. prejudice_or_discrimination
    3. other
    4. undefined : when three annotators could not decide the label through major voting, but 2 or more chose one of the unsafe sub-labels.

We remove include? label from our KoSBi because all contexts include target group.

4. Training the Filter Model

Before training your own filter model, you need to split annotated dataset into train/valid/test set.

Make sure that --task should include kosbi_incl_grp_classify.

python src/train.py \
    --task kosbi_incl_grp_classify \
    --wandb_project {wandb_prj} \
    --wandb_entity {wandb_entity} \
    --do_train_only True \
    --checkpoint_dirpath data/models/kosbi_context \
    --trn_pth {train.json} \
    --val_pth {valid.json} \
    --tst_pth {test.json} \
    --epoch {epoch} \
    --lr {lr} \
    --batch_size {bs} \
    --gradient_clip_val {gc_val}

5. Augmenting Demonstrations Pool

With annotated dataset, we augment the demonstrations pool.

Note that, at the first iteration, we replace the initial demonstrations with the annotated dataset, not augment it. (i.e., skipping --prev_demo_fname)

python pipeline/kosbi/augment_demo_context.py \
    --input_fname data/generations/filtered_contexts.json \
    --prev_demo_fname {prev_demo.json} \
    --output_dir pipeline/kosbi/demo \
    --output_demo_fname kosbi_context_iter1.json

Un-/Safe Sentence Generation

1. (Next) Sentence Generation

Before generating sentences, you need:

  • HyperCLOVA API url and API key (as same as in context generation)
  • Annotated contexts dataset
  • Initial demonstration pool for prompting
    • Ours is in pipeline/kosbi/demo/kosbi_sentence_iter0.json

We used 10 demonstration samples of sentences when generating KoSBi. Among them, 5 demonstrations are from the pool with the same context label as what we are generating. Of course, you can change these numbers in your generation pipeline.

Here is a code for generating sentences:

python pipeline/kosbi/generate_sentence.py \
    --api_url {API_url} \
    --api_key {API_key} \
    --demo_fpath pipeline/kosbi/demo/kosbi_sentence_iter0.json \
    --num_total_demos 10 \
    --num_in_context_label 5 \
    --context_fpath data/generations/filtered_contexts.json \
    --output_dir data/generations \
    --output_fname output_sentences.json

2. Filtering: Select Ambiguous Data

To make our benchmark more challenging, we select ambiguous datapoints among generated. In the paper, we used 5 checkpoints and calculated estimated max variability.

If there are no classifiers (e.g., at the first iteration), skip the argument --filter_model_ckpt_dir.

python pipeline/kosbi/filter_sentence.py \
    --input_fname data/generations/output_sentences.json \
    --filter_model_ckpt_dir {filter_ckpt_dir} \
    --filter_model_train_epochs 5 \
    --output_dir data/generations \
    --output_fname filtered_sentences.json

3. Human Annotation

After annotated from human annotators, each data points should contain 2 labels:

  • sentence_label : the label of the sentence is one of {safe,unsafe}
  • sentence_sub_label : if labeled unsafe, the sentence is further labeled as
    1. stereotype_{explicit,implicit}
    2. prejudice_{explicit,implicit}
    3. discrimination_{explicit,implicit}
    4. other
    5. undefined : when three annotators could not decide the label through major voting, but 2 or more chose one of the unsafe sub-labels.

You can see examples in our KoSBi dataset.

4. Training the Filter Model

Before training your own filter model, you must split annotated dataset into train/valid/test sets.

It is recommended to set checkpoint_save_top_k == epoch and earlystop_patience == epoch to save all checkpoints. These checkpoints will be used for variability calculation.

Make sure that --task should include kosbi_unsafe_sent_classify.

python src/train.py \
    --task kosbi_unsafe_sent_classify \
    --wandb_project {wandb_prj} \
    --wandb_entity {wandb_entity} \
    --do_train_only True \
    --checkpoint_dirpath data/models/kosbi_sentence \
    --trn_pth {train.json} \
    --val_pth {valid.json} \
    --tst_pth {test.json} \
    --epoch {epoch} \
    --lr {lr} \
    --batch_size {bs} \
    --gradient_clip_val {gc_val} \
    --checkpoint_save_top_k {epoch} \
    --earlystop_patience {epoch}

5. Augmenting Demonstrations Pool

With annotated dataset, we augment the demonstrations pool. To be used as a demonstration, each data should contain an sentence_label, an context_label, and an context_sub_label label.

As mentioned in the paper, we updated the demonstrations pool with the annotated dataset, to which all annotators agree. Therefore, raw annotations are also needed at this step. Examples of raw annotations can be found in data/SQuARe/with_raw_annotations. While Q2: Acceptable or Non-acceptable annotations are used in SQuARe, in KoSBi, we need (raw) annotations for Q2: Safe or Unsafe.

Note that, at the first iteration, we replace the initial demonstrations with the annotated dataset, not augment it. (i.e., skipping --prev_demo_fname)

python pipeline/kosbi/augment_demo_sentence.py \
    --input_fname data/generations/filtered_sentences.json \
    --prev_demo_fname {prev_demo.json} \
    --filter_model_ckpt_dir data/models/kosbi_sentence \
    --filter_model_train_epochs 5 \
    --output_dir pipeline/kosbi/demo \
    --output_demo_fname kosbi_sentence_iter1.json \
    --most_ambiguous_ratio 0.25