From b611d9df8953619cd70ce01e527f49cf9b472686 Mon Sep 17 00:00:00 2001 From: eliselavy Date: Mon, 25 Nov 2024 16:28:45 +0100 Subject: [PATCH] tag / https://github.com/C2DH/jdh-notebook/issues/187 --- .ipynb_checkpoints/article-checkpoint.ipynb | 8102 +++++++++++++++++++ article.ipynb | 14 +- 2 files changed, 8106 insertions(+), 10 deletions(-) create mode 100644 .ipynb_checkpoints/article-checkpoint.ipynb diff --git a/.ipynb_checkpoints/article-checkpoint.ipynb b/.ipynb_checkpoints/article-checkpoint.ipynb new file mode 100644 index 0000000..c5d0f45 --- /dev/null +++ b/.ipynb_checkpoints/article-checkpoint.ipynb @@ -0,0 +1,8102 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "editable": true, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "title" + ] + }, + "source": [ + "# Prompting the Past: Large Language Models as Versatile Tools for Digital Historians\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "tags": [ + "contributor" + ] + }, + "source": [ + " ### anonym" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "editable": true, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "copyright" + ] + }, + "source": [ + "[![cc-by](https://licensebuttons.net/l/by/4.0/88x31.png)](https://creativecommons.org/licenses/by/4.0/) \n", + "©. Published by De Gruyter in cooperation with the University of Luxembourg Centre for Contemporary and Digital History. This is an Open Access article distributed under the terms of the [Creative Commons Attribution License CC-BY](https://creativecommons.org/licenses/by/4.0/)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "editable": true, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "keywords" + ] + }, + "source": [ + "Large language models, artifical intelligence, machine learning, historical methodology, optical character recognition, oral history, prompt engineering" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "editable": true, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "abstract" + ] + }, + "source": [ + "This article examines how digital historians are using large language models (LLMs) in their research and teaching, along with the critical and ethical debates surrounding their use. The article first assesses the historical capacities of LLMs as measured by machine learning benchmarks, and how such assessments can help historians understand the capacities and limits of these technologies. The utility of LLMs as digital tools are then demonstrated through a series of case studies using GPT-4 and other generative AI models for oral history transcriptions, correcting optical character recognition (OCR) errors, and metadata extraction. These case studies also demonstrate how frameworks for using LLMs, such as prompt engineering and retrieval augmented generation (RAG), are used to ground LLM outputs for consistency and greater accuracy. Acknowledging the significant ethical challenges posed by LLMs, the article emphasizes the need for critical engagement and the development of responsible frameworks for implementing these technologies in historical scholarship. By combining disciplinary expertise with innovative computational approaches, historians are discovering new ways to navigate the \"unheard-of historical abundance\" of the digital age, contributing to approaches to generative AI that enriches, rather than distorts, our understanding of the past.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "citation-manager": { + "citations": { + "5w5sr": [ + { + "id": "27937/XQYUJV5F", + "source": "zotero" + } + ], + "fgell": [ + { + "id": "27937/CJYNFHVI", + "source": "zotero" + } + ], + "hiex8": [ + { + "id": "27937/GHGWH4HI", + "source": "zotero" + } + ], + "uo7pa": [ + { + "id": "27937/L2ILKERU", + "source": "zotero" + } + ] + } + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "In 2003, Roy Rosenzweig predicted that digital historians would need to develop new techniques \"to research, write, and teach in a world of unheard-of historical abundance.\" (Rosenzweig, “Scarcity or Abundance?”) Over the past two decades historians have risen to this challenge, embracing digital mapping, network analysis, distant reading of large text collections, and machine learning as part of their growing methodological toolkit. (Graham, Milligan, and Weingart, Exploring Big Historical Data.) Generative artificial intelligence (AI) has emerged as another potential tool for historians, particularly large language models (LLMs), the most prominent form of this technology. These models possess striking capacities to generate, interpret, and manipulate data across a range of modalities. The rapidly-expanding scope of these capabilities and their limits remain intensely debated, as do their broader social, economic, cultural, and environmental impacts. Yet while still an emerging technology, historians are already demonstrating generative AI's potential as a versatile digital tool. Historians are also contributing to the critical discourse surrounding this new domain, raising key questions about how these models are created, their propensity to reinforce existing inequalities, and their potential to distort our understanding of the past. (Meadows and Sternfeld, “Artificial Intelligence and the Practice of History.”)\n", + "\n", + "This article contributes to these debates by demonstrating how digital historians are using generative AI to explore the past and the disciplinary contributions historians can offer in these broader debates concerning this technology. (Dzieza, “What AI Can Do for Historians.”) We begin by assessing the metrics commonly used to measure the historical knowledge of LLMs, and examine how such metrics can give us insights into the capacities and limits this technology. We then examine how generative AI can be used in tasks as varied as preparing datasets, exploring text collections, and offering novel (and controversial) methods of representing the past. We conclude with a call to historians to contribute to ongoing research and debates concerning the ethical use of generative AI. Given the rapid pace of innovation in this field, it is crucial that the profession addresses the implications of this technology for our research and teaching. Historians will have much to offer in contextualizing these technologies and their potential impacts on society." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## What Do AIs Know About History? Assessing LLMs for Historical Knowledge" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "citation-manager": { + "citations": { + "1j6sv": [ + { + "id": "27937/9T2I7QLM", + "source": "zotero" + } + ], + "2de4f": [ + { + "id": "27937/F3XT4XAQ", + "source": "zotero" + } + ], + "3fk4h": [ + { + "id": "27937/KNEK45E4", + "source": "zotero" + } + ], + "mptkr": [ + { + "id": "27937/QD3X7XMD", + "source": "zotero" + } + ], + "ors1l": [ + { + "id": "27937/56EE9N63", + "source": "zotero" + } + ], + "x9a26": [ + { + "id": "27937/H9BUWE28", + "source": "zotero" + } + ] + } + }, + "collapsed": false, + "editable": true, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "As historians explore the possibilities of generative AI, it is important to understand how these technologies are created and assessed. With this knowledge we can better evaluate their potential utility and their limits.\n", + "\n", + "At the most fundamental level, generative AI models are statistical representations of the datasets on which they are trained. Machine learning techniques like deep learning and recent innovations like the Transformer network architecture (Vaswani et al., “Attention Is All You Need.”) have enabled the creation of models capable of mimicking the data on which they are trained with a high degree of fidelity. But researchers have also discovered that with sufficient time and the application of (often immense) computational power, these models exhibit a range of “emergent” capabilities. (Wei et al., “Emergent Abilities of Large Language Models.”) For example, LLMs can summarize texts, perform language translation, write working computer code, and compose informative responses on a wide array of subjects - all without specific training on how to perform such tasks. (Brown et al., “Language Models Are Few-Shot Learners.”) Moreover, these emergent capacities seem to \"scale\", meaning new models exhibit enhanced performance through training on ever-greater quantities of data and computation. (Kaplan et al., “Scaling Laws for Neural Language Models.”) The nature of these emergent capacities remains a matter of intense research and debate, as do the ethical and legal questions surrounding their use. However, it is clear that LLMs can both interpret and generate data in ways that rival previous machine learning methods. Scholars studying these AI systems have labeled them \"foundational models\" due to their potential to enable new domains of computational analysis. (Bommasani et al., “On the Opportunities and Risks of Foundation Models.”) Indeed, the remarkable versatility of LLMs is stimulating broader discussions about the potential implications of these technologies on society at large. (Eloundou et al., “GPTs Are GPTs.”)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "citation-manager": { + "citations": { + "nppps": [ + { + "id": "27937/U534FF7L", + "source": "zotero" + } + ], + "r8lyr": [ + { + "id": "27937/UYVGUT4C", + "source": "zotero" + } + ] + } + }, + "collapsed": false, + "editable": true, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hermeneutics" + ] + }, + "source": [ + "While the Generative Pre-trained Transformer (GPT) series from OpenAI is the best known of these foundational models, there has been a rapid proliferation of commercial and open-source alternatives. Notable recent LLMs include Google’s Gemini, Anthropic’s Claude, and open-source models offered by Meta and Mistral.\n", + "\n", + "Foundational models are also emerging in other domains, such as image, video, and audio synthesis. Architectures like CLIP (Radford et al., “Learning Transferable Visual Models From Natural Language Supervision.”) enable the creation of synthetic imagery in models like OpenAI’s DALL-E, Midjourney, and the open-source community behind Stable Diffusion. Similar approaches for generating video, speech, and music have been developed by firms like Runway-XL, ElevenLabs, and Suno, along with open-source alternatives hosted on sites likes HuggingFace. Most notably, new forms of LLM-training have enabled the combination of these capacities in multi-modal models capable of working across multiple domains, such as OpenAI’s GPT-4 series. (OpenAI, “GPT-4 Technical Report.”)\n", + "\n", + "An accessible way to stay abreast of recent innovations in this field is by following the leaderboards used to measure performance on standard LLM benchmarks. [LLMArena’s Chatbot Arena](https://lmarena.ai/) offers an overview of leading contemporary models, while [HuggingFace’s Open LLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) and the [Open Multilingual LLM Evaluation Leaderboard](https://huggingface.co/spaces/uonlp/open_multilingual_llm_leaderboard) offer specialized metrics for particular domains and use-cases. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "citation-manager": { + "citations": { + "4lr4q": [ + { + "id": "27937/FMW5DCWM", + "source": "zotero" + } + ], + "e4unb": [ + { + "id": "27937/EZNK3CE3", + "source": "zotero" + } + ], + "g0kqk": [ + { + "id": "27937/MVDFMR8K", + "source": "zotero" + } + ], + "v35u4": [ + { + "id": "27937/78DL3V96", + "source": "zotero" + } + ] + } + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "While such claims have sparked both excitement and alarm, any assessment of LLMs must first be tempered with humility. LLMs are often described as possessing “knowledge” and “understanding,” yet direct engagement with these models can quickly reveal both their remarkable breadth and their narrow limits. Incisive critics of this technology characterize LLMs as “stochastic parrots” that excel at uncanny mimicry of human intelligence. (Bender et al., “On the Dangers of Stochastic Parrots | Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency.”) A form of this mimicry has proven convincing in the past. The first attribution of true artificial intelligence to a computer program occurred in 1966 with a scripted chatbot named ELIZA, developed by AI pioneer Joseph Weizenbaum. (McCorduck, Machines Who Think a Personal Inquiry into the History and Prospects of Artificial Intelligence.) A recent replication of this phenomenon occurred in June 2022 when a Google AI engineer declared the LLM he was training had become sentient. Such attributions will likely increase as newer LLMs demonstrate increasing proficiency in seemingly distinct human qualities, like humor. (Chowdhery et al., “PaLM.”) The means by which LLMs process, interpret, and generate information is a highly technical field requiring specialization in natural language processing, statistics, computational linguistics, and machine learning. While many historians may lack the technical knowledge to effectively evaluate the merits of these debates, when it comes to our own domain we are well equipped to offer informed insights.\n", + "\n", + "Indeed, the standard measurement for a LLM’s historical knowledge was inadvertently created by historians. One widely-used measure for LLM performance is the Massive Multitask Language Understanding (MMLU) benchmark, developed in 2021 by researchers led by Dan Hendryks. This benchmark contains nearly 16,000 questions from 57 academic disciplines ranging in difficulty from an elementary educational level to postgraduate curricula in professional domains like law and medicine. History is measured in this benchmark through some six hundred questions taken from the Advanced Placement (A.P.) curricula for U.S., European, and World history. Hundreds of thousands of secondary students across the globe annually enroll in these curricula, which are designed to replicate the rigors of an introductory, university-level history course. The educators who developed and refined these programs likely never imagined their work would serve as a technical benchmark, and the appropriateness of such a standard can be debated. (Marshall, “The Strange World of AP U.S. History.”) Yet this benchmark, however imperfect, offers historians an accessible means to evaluate this highly technical domain." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "In this benchmark, LLMs are given an excerpt from a historical source followed by a multiple-choice question, and are then instructed to identify the correct answer. Below is an example question drawn from the U.S. History curriculum:\n", + "\n", + "**U.S. History Benchmark, Question 5:**\n", + "\n", + "This question refers to the following information.\n", + "\n", + "> “I was once a tool of oppression \n", + "> And as green as a sucker could be \n", + "> And monopolies banded together \n", + "> To beat a poor hayseed like me.” \n", + "> \n", + "> “The railroads and old party bosses \n", + "> Together did sweetly agree; \n", + "> And they thought there would be little trouble \n", + "> In working a hayseed like me. . . .”\n", + "\n", + "*The Hayseed*\n", + "\n", + "The song, and the movement that it was connected to, highlight which of the following developments in the broader society in the late 1800s?\n", + "\n", + "**A**: Corruption in government, especially as it related to big business, energized the public to demand increased popular control and reform of local, state, and national governments. \n", + "**B**: A large-scale movement of struggling African American and white farmers, as well as urban factory workers, was able to exert a great deal of leverage over federal legislation. \n", + "**C**: The two-party system of the era broke down and led to the emergence of an additional major party that was able to win control of Congress within ten years of its founding. \n", + "**D**: Continued skirmishes on the frontier in the 1890s with American Indians created a sense of fear and bitterness among western farmers.\n", + "\n", + "**Correct Answer: A**\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "citation-manager": { + "citations": { + "wfmit": [ + { + "id": "27937/ZS9JDNGD", + "source": "zotero" + } + ] + } + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "The MMLU benchmarks were first tested in 2021 against the then-leading LLM, OpenAI’s GPT-3. Twenty-five percent accuracy represented random chance; ninety percent performance reflected expert-level accuracy. GPT-3 initially achieved over fifty percent accuracy on all three A.P. curricula, and its performance in these subfields numbered among the top third of all the academic disciplines in the benchmarks. However, in no field did GPT-3 achieve expert-level accuracy, and the model demonstrated particularly poor performance in the fields of “Moral Questions” and “Professional Law.” As the authors note, this “weakness is particularly concerning because it will be important for future models to have a strong understanding of what is legal and what is ethical.” (Hendrycks et al., “Measuring Massive Multitask Language Understanding.”)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "citation-manager": { + "citations": { + "tbd8o": [ + { + "id": "27937/A834FRJL", + "source": "zotero" + } + ] + } + }, + "collapsed": false, + "editable": true, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hermeneutics" + ] + }, + "source": [ + "The specific accuracy rates for GPT-3 for the initial Hendryks study: US History, 52.9%; European History, 53.9%; and World History, 56.1%. Full data for questions for history and other disciplines can be found at: (Hendrycks, Measuring Massive Multitask Language Understanding.) Many thanks to Dan Hendrycks for sharing the discipline-specific accuracy rates for these fields." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "However, rapid advances in model development have occurred since 2021. Subsequent tests on newer models “scaled” on ever greater amounts of data and computation demonstrate substantial gains in performance on these historical benchmarks. Below are results from a replication study conducted in September 2024 across a series of leading LLMs, along with the initial Hendryks test:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "editable": true, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "table-1-*" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import Image\n", + "from IPython.display import display\n", + "from IPython.display import Markdown\n", + "\n", + "table_1_url = 'media/Table 1 - MMLU Benchmark Performance.png'\n", + "display(Image(url=table_1_url, width=850))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "citation-manager": { + "citations": { + "s177q": [ + { + "id": "27937/5AL5LZ2K", + "source": "zotero" + } + ], + "s4luc": [ + { + "id": "27937/GSIXPJ7P", + "source": "zotero" + } + ] + } + }, + "collapsed": false, + "editable": true, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hermeneutics" + ] + }, + "source": [ + "Data from this replication study can be accessed via the HELM Leaderboard for the MMLU Benchmark, hosted by the Center for Research on Foundation Models at Stanford University. (Mai and Liang, “Massive Multitask Language Understanding (MMLU) on HELM.”) You can directly experiment with LLM performance on these benchmarks via a digital history project accompanying this article, “What Do AIs Know About History?” (Hutchinson.)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "citation-manager": { + "citations": { + "3unok": [ + { + "id": "27937/YVTAGDKZ", + "source": "zotero" + } + ], + "55r4n": [ + { + "id": "27937/KNEK45E4", + "source": "zotero" + } + ], + "5a9qa": [ + { + "id": "27937/BD8996H7", + "source": "zotero" + } + ], + "6cssb": [ + { + "id": "27937/9GQG6VFM", + "source": "zotero" + } + ], + "6ph9l": [ + { + "id": "27937/5YDNQS4V", + "source": "zotero" + } + ], + "8fjtz": [ + { + "id": "27937/VEDFUUBA", + "source": "zotero" + } + ], + "97pas": [ + { + "id": "27937/X4D92B7V", + "source": "zotero" + } + ], + "ahtmn": [ + { + "id": "27937/BVBZMR66", + "source": "zotero" + } + ], + "c6t3w": [ + { + "id": "27937/TPGPSRAI", + "source": "zotero" + } + ], + "fpott": [ + { + "id": "27937/IEQ8GAVU", + "source": "zotero" + } + ], + "j1kj5": [ + { + "id": "27937/KNEK45E4", + "source": "zotero" + } + ], + "jm1mt": [ + { + "id": "27937/5GTQD5W9", + "source": "zotero" + } + ], + "kba8r": [ + { + "id": "27937/U534FF7L", + "source": "zotero" + } + ], + "r1ql3": [ + { + "id": "27937/TGPDB8WX", + "source": "zotero" + } + ], + "vuott": [ + { + "id": "27937/MVDFMR8K", + "source": "zotero" + } + ], + "xndfm": [ + { + "id": "27937/NYNDVYMM", + "source": "zotero" + } + ], + "yh6j9": [ + { + "id": "27937/S3ADX5DD", + "source": "zotero" + } + ], + "zsn16": [ + { + "id": "27937/MHRIEHH8", + "source": "zotero" + } + ] + } + }, + "collapsed": false, + "editable": true, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Rapid improvement on this benchmark have been made in just a few years, with a variety of commercial and open-source LLMs now demonstrating expert-level accuracy on all three of the subject exams. These findings mirror the striking performance of models like GPT-4 in other knowledge domains such as medical school curricula (Nori et al., “Capabilities of GPT-4 on Medical Challenge Problems.”), American bar exams, (Katz, “GPT Takes the Bar Exam.”), and a host of other standardized assessments. (OpenAI, “GPT-4 Technical Report.”)\n", + "\n", + "Yet, why do some LLMs perform better in some knowledge domains than others? How can a model get one question right, while other questions generate errors? There is a temptation to parse the model’s performance in ways relatable to our human perspective. The human test taker might approach the question by assessing what types of historical thinking each question requires, what sort of knowledge is offered by the options, and how the historical source relates to the question. But, of course, LLMs aren’t human - and unlike the human test taker, these models have already seen the questions in advance. In 2022 alone, over 800,000 students took A.P. History exams. (“Program Summary Report.”) Significant online resources have emerged to serve the sizable population of students and instructors participating in this international curriculum. Hundreds of exam questions have migrated online via the collective efforts of the test prep publishing industry, various study apps, and uploaded example tests.\n", + "\n", + "Thus the capabilities of LLMs on these benchmarks directly relates to the vast dataset used to train them: the Internet itself. The data collection built for training GPT-3 encompassed the majority of English-language Wikipedia, Reddit’s thousands of discussion forums, extensive corpora of digitized books, and the billions of web pages contained in the Common Crawl repository. (Brown et al., “Language Models Are Few-Shot Learners.”) The training sets used for subsequent LLMs remains largely unknown, as AI firms keep their data a closely guarded and proprietary asset; indeed, the future of LLMs may depend on pending litigation concerning copyright infringement in the use of this data. Yet given the scale of such datasets, many of the A.P. History questions used in these benchmarks have likely ended up in LLM training data. If those who critique LLMs as “stochastic parrots” are correct, these gains in performance come from improvements in models memorizing this data, and not through any analytical process. (Bender et al., “On the Dangers of Stochastic Parrots | Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency.”) When LLMs encounter questions outside of their training, their accuracy is likely to suffer.\n", + "\n", + "Yet such inaccuracies can be difficult to detect. LLMs tend to confidently assert error as fact, a phenomenon described by AI researchers as “hallucinations.” Such hallucinations represent a major challenge in LLM research and for many practical applications of this technology, particularly given the remarkable effectiveness of these models in generating convincing and otherwise accurate prose. (Ji et al., “Survey of Hallucination in Natural Language Generation.”) Initial testing by OpenAI on the GPT series demonstrated that human readers often struggle to identify text generated by LLMs. (Brown et al., “Language Models Are Few-Shot Learners.”) Rectifying such hallucinations is a significant area of LLM research. However, some scholars, like computational linguist Emily Bender, argue that such behaviors are inherent flaws in LLMs. (Bender, “On NYT Magazine on AI.”)\n", + "\n", + "Additional risks confront historians using these technologies. While AI firms seek to remove potentially offensive texts from their training sets, the sheer scale of this data make selective curation very challenging. LLMs thus generate responses reflecting both the best and the worst of our online world. This reality has troubled previous AI implementations. Well-intentioned researchers have created chatbots that spew hateful invective, human resources applications that refuse to hire female applicants, and algorithms based on criminal justice sentencing guidelines that starkly reinforce racial disparities already prevalent in the carceral system. (Barton, “Algorithmic Bias Detection and Mitigation.”) Early models in the GPT series have been known to unexpectedly generate responses in innocuous contexts containing violent imagery, sexually explicit language, and racial, ethnic, and religious slurs. (Strickland, “OpenAI’s GPT-3 Speaks! (Kindly Disregard Toxic Language) - IEEE Spectrum.”) These findings further confirm the prescient warnings offered by scholars such as Safiya Umoja Noble (Noble, Algorithms of Oppression.), Timnit Gebru (Gebru, “Race and Gender.”), Ruha Benjamin (Benjamin, Race After Technology.), Kate Crawford (Crawford, Atlas of AI.), and Trevor Paglen (Crawford and Paglen, “Excavating AI.”) on digital practices that reinforce analog inequalities. Some AI researchers consider such behaviors as lamentable but solvable problems through further technical advances, particularly with the use of methods like Reinforcement Learning from Human Feedback (RLHF). (Christiano et al., “Deep Reinforcement Learning from Human Preferences.”) Reducing the impact of such biases is a significant research area, particularly through the creation of smaller, more carefully curated datasets for AI training. However, many historians will likely share the skepticism of some researchers concerning such mitigations. (Gehman et al., “RealToxicityPrompts.”) Bias emerges from more than just explicit language or imagery but from the very structures of societies. Can any historical source be separated from its context as a neutral artifact, free of its creator’s perspective and the influences of its time? What about the untold millions of sources that make up the scale of an LLM’s training set?\n", + "\n", + "To be sure, LLMs are imperfect digital tools, and given these flaws historians must exercise caution when employing this technology. Yet scholars are finding that within the confines of these imperfections there is real potential to advance historical research. While a LLM’s facility with multiple-choice questions might be the product of memorization, such knowledge has long been a springboard for more advanced forms of inquiry. And A.P. study guides are not the only historical texts LLMs are trained on. Primary source collections, academic monographs, open-source scholarly journals - these too inform an LLM’s training. \n", + "\n", + "The influence of these sources can be found when LLMs are posed more complex questions in a structured prompt. Let’s return to the earlier A.P. question above featuring the Populist-era campaign song “The Hayseed.” In the code blocks below, GPT-4 is given the lyrics and publication history of the song. GPT-4 is then prompted to identify the larger historical context of the source, the song’s intended purpose and audience, and how the source might be interpreted via different historiographical approaches." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "editable": true, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "figure-hayseed-*" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": { + "image/png": { + "width": 600 + }, + "jdh": { + "object": { + "source": [ + "Arthur L. Kellog, “The Hayseed,” *Farmers Alliance* (4 October 1890). Nebraska Newspapers (University of Nebraska Libraries), https://nebnewspapers.unl.edu/lccn/2017270209/1890-10-04/ed-1/seq-1/. \n\nOriginal citation found in: John Donald Hicks, *The Populist Revolt: A History of the Farmers' Alliance and the People's Party* (University of Minnesota Press, 1931), 168, fn. 30." + ], + "type": "image" + } + } + }, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import Image, display\n", + "\n", + "metadata={\n", + " \"jdh\": {\n", + " \"object\": {\n", + " \"type\":\"image\",\n", + " \"source\": [\n", + " \"Arthur L. Kellog, “The Hayseed,” *Farmers Alliance* (4 October 1890). Nebraska Newspapers (University of Nebraska Libraries), https://nebnewspapers.unl.edu/lccn/2017270209/1890-10-04/ed-1/seq-1/. \\n\\nOriginal citation found in: John Donald Hicks, *The Populist Revolt: A History of the Farmers' Alliance and the People's Party* (University of Minnesota Press, 1931), 168, fn. 30.\"\n", + " ]\n", + " }\n", + " }\n", + "}\n", + "\n", + "\n", + "# Display the image\n", + "display(Image('./media/hayseed.png', width=600), metadata=metadata)\n", + "\n", + "# Create formatted citation text \n", + "citation_text = (\n", + " \"Arthur L. Kellog, “The Hayseed,” [italic]Farmers Alliance[/italic] (4 October [not bold]1890[/not bold]). \"\n", + " \"[italic]Nebraska Newspapers[/italic] (University of Nebraska Libraries), \"\n", + " \"https://nebnewspapers.unl.edu/lccn/2017270209/[not bold]1890[/not bold]-10-04/ed-[not bold]1[/not bold]/seq-[not bold]1[/not bold]/.\\n\\n\"\n", + " \"Original citation found in: John Donald Hicks, [italic][bold]The Populist Revolt: A History of the Farmers' Alliance and the People's Party[/bold][/italic] \"\n", + " \"(University of Minnesota Press, [not bold]1931[/not bold]), [not bold]168[/not bold], fn. [not bold]30[/not bold].\"\n", + ")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/markdown": [ + "**Primary Source Analysis Prompt fed to GPT-4o:**\n", + "\n", + "Your task is to perform historical source analysis and historiographical interpretation. When given a historical source, you will provide a detailed and substantive analysis of that source based on the method and source information below.\n", + "\n", + "Step 1 - Contextualization: Apply the source information to provide a detailed and substantive analysis of how the historical source reflects the larger period in which it was created. In composing this analysis, note specific events, personalities, and ideologies that shaped the period noted in the source information.\n", + "\n", + "Step 2 - Purpose: Offer a substantive exploration of the purpose of the historical source, interpreting the author’s arguments through the broader contextualization of the period.\n", + "\n", + "Step 3 - Audience: Compose a substantive assessment of the intended audience of the historical source. Note how this audience would shape the source's reception and historical impact.\n", + "\n", + "Step 4 - Historiographical Interpretation: Provide a substantive and incisive interpretation of how at least three specific schools of historiographical thought would interpret this source, comparing and contrasting each approach. Different historiographical approaches could include: Progressive, Consensus, Marxist, postmodern, social history, religious history, political history, gender history, and cultural history.\n", + "\n", + "Source Information: \"The Hayseed.\" By Arthur L. Kellog.\n", + "Farmers Alliance (Nebraska, 4 October 1890)\n", + "\n", + "Tune: Save a Poor Sinner Like Me\n", + "\n", + "I was once a tool of oppression\n", + "And as green as a sucker could be\n", + "And monopolies banded together\n", + "To beat a poor hayseed like me.\n", + "The railroads and old party bosses\n", + "Together did sweetly agree;\n", + "And they thought there would be little trouble\n", + "In working a hayseed like me. . . .\"\n", + "\n", + "Instructions: Based on the method outlined above, provide a substantive and detailed analysis of the historical source in the manner of an academic historian.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import requests\n", + "\n", + "\n", + "\n", + "\n", + "# URL of the raw text file on GitHub\n", + "file_url = 'https://raw.githubusercontent.com/Dr-Hutchinson/jdh_submission/refs/heads/main/media/prompts/primary_source_analysis.txt'\n", + "\n", + "# Fetch the content of the file\n", + "response = requests.get(file_url)\n", + "\n", + "# Format the prompt text for primary source analysis\n", + "primary_source_analysis_prompt = response.text.replace('\\\\n', '\\n')\n", + "display(Markdown(\"**Primary Source Analysis Prompt fed to GPT-4o:**\\n\\n\" + primary_source_analysis_prompt))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Defaulting to user installation because normal site-packages is not writeable\n", + "\u001b[33mWARNING: Ignoring invalid distribution -andas (/usr/local/lib/python3.10/dist-packages)\u001b[0m\u001b[33m\n", + "\u001b[0mRequirement already satisfied: openai in /usr/local/lib/python3.10/dist-packages (1.44.0)\n", + "Requirement already satisfied: anyio<5,>=3.5.0 in /usr/lib/python3/dist-packages (from openai) (3.5.0)\n", + 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\u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Defaulting to user installation because normal site-packages is not writeable\n", + "\u001b[33mWARNING: Ignoring invalid distribution -andas (/usr/local/lib/python3.10/dist-packages)\u001b[0m\u001b[33m\n", + "\u001b[0mRequirement already satisfied: jiwer in /home/user/.local/lib/python3.10/site-packages (3.0.4)\n", + "Requirement already satisfied: click<9.0.0,>=8.1.3 in /usr/local/lib/python3.10/dist-packages (from jiwer) (8.1.7)\n", + "Requirement already satisfied: rapidfuzz<4,>=3 in /usr/local/lib/python3.10/dist-packages (from jiwer) (3.5.1)\n", + "\u001b[33mWARNING: Ignoring invalid distribution -andas (/usr/local/lib/python3.10/dist-packages)\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Defaulting to user installation because normal site-packages is not writeable\n", + "\u001b[33mWARNING: Ignoring invalid distribution -andas (/usr/local/lib/python3.10/dist-packages)\u001b[0m\u001b[33m\n", + "\u001b[0mRequirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (13.4.2)\n", + "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich) (3.0.0)\n", + "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich) (2.17.2)\n", + "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich) (0.1.1)\n", + "\u001b[33mWARNING: Ignoring invalid distribution -andas (/usr/local/lib/python3.10/dist-packages)\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "# installing libraries\n", + "!pip install openai\n", + "!pip install jiwer\n", + "!pip install rich" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "# Enter OpenAI API key in the space below.\n", + "# Access to OpenAI's API keys can be found here: https://beta.openai.com/signup\n", + "\n", + "import os\n", + "os.environ[\"OPENAI_API_KEY\"] = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
GPT-4's Interpretation of 'The Hayseed':\n",
+       "\n",
+       "**Step 1 - Contextualization:**\n",
+       "\n",
+       "The poem \"The Hayseed\" by Arthur L. Kellog, published on October 4, 1890, in the Farmers Alliance newspaper in \n",
+       "Nebraska, reflects the socio-political climate of late 19th-century America, a tumultuous period characterized by \n",
+       "rapid industrialization, the rise of monopolies, and the struggles of the agrarian community against economic \n",
+       "exploitation. The Farmers Alliance was part of a broader agrarian movement, which included the Populist Party, that\n",
+       "emerged in response to the growing power of railroads and large corporate interests that appeared to prioritize \n",
+       "industrial over agricultural development. This era saw the rise of what would be known as the \"Gilded Age,\" a \n",
+       "period marked by economic disparity, political corruption, and the tug-of-war between reformists and established \n",
+       "economic interests. Key events of this period include the McKinley Tariff, which exacerbated economic woes for \n",
+       "farmers by elevating domestic product prices, and the subsequent Panic of 1893, which underscored economic \n",
+       "vulnerabilities in the agricultural sector. The personalities influential in this period included William Jennings \n",
+       "Bryan, a champion of the agrarian cause, and political figures like Grover Cleveland, who often seemed aligned with\n",
+       "big business interests.\n",
+       "\n",
+       "**Step 2 - Purpose:**\n",
+       "\n",
+       "The purpose of Kellog's work was to articulate and amplify the frustrations of the common farmer, the so-called \n",
+       "\"hayseed,\" against the entrenched economic powers that exploited them. By employing the format of a song that could\n",
+       "be easily disseminated and understood by the largely rural and perhaps minimally literate farmer population, Kellog\n",
+       "aimed to inspire solidarity and mobilize these individuals to become politically active against corporate and \n",
+       "political exploitation. The specific references to \"railroads\" and \"old party bosses\" highlight the systemic nature\n",
+       "of the oppression faced by farmers and seek to draw attention to a perceived need for agrarian political reform. \n",
+       "The piece aligns with the broader goals of the Farmers Alliance and later the Populist Party, aiming to coalesce \n",
+       "farmer discontent into a political force capable of challenging the status quo.\n",
+       "\n",
+       "**Step 3 - Audience:**\n",
+       "\n",
+       "The intended audience for Kellog's poem was primarily the agrarian members of the Farmers Alliance and wider rural \n",
+       "communities in the Midwest, who were adversely affected by the industrial economy's growth. This audience was \n",
+       "largely composed of smallholder farmers who felt marginalized by the economic and political systems favoring urban \n",
+       "industrialists and railroads. Given their shared experiences and grievances, the audience would likely receive the \n",
+       "poem with empathy and conviction, supporting collective political action. This piece also served to bolster the \n",
+       "morale and sense of identity among farmers, reinforcing a common cause against perceived oppression.\n",
+       "\n",
+       "**Step 4 - Historiographical Interpretation:**\n",
+       "\n",
+       "- **Progressive Historiography:** Progressive historians might interpret this source as part of the broader \n",
+       "narrative of reform movements challenging entrenched interests during the Gilded Age. They would emphasize how the \n",
+       "poem exemplifies the struggle against monopolistic power structures and the call for political reform, focusing on \n",
+       "the contribution of grassroots movements like the Farmers Alliance to American reform traditions.\n",
+       "\n",
+       "- **Marxist Historiography:** Marxist historians would likely view this poem as an articulation of class struggle, \n",
+       "highlighting the conflict between the proletarian agrarian class and the bourgeois industrial monopolists, \n",
+       "including railroad magnates. They would analyze the poem as a reflection of the exploitation and socio-economic \n",
+       "alienation faced by farmers, and a call to action that hints at rising class consciousness among rural workers.\n",
+       "\n",
+       "- **Cultural Historiography:** Cultural historians might examine Kellog's poem as a cultural artifact illustrating \n",
+       "the identity and values of the agrarian community during the late 19th century. They would focus on the linguistic \n",
+       "choices, the common trope of the \"hayseed,\" and how these elements communicate a sense of unity and shared plight, \n",
+       "reflecting the cultural dynamics and collective consciousness emerging within American rural societies.\n",
+       "\n",
+       "In comparing these perspectives, progressive historians focus on the reformist and institutional outcomes of such \n",
+       "movements, while Marxists are concerned with underlying class tensions and economic structures. Cultural \n",
+       "historians, contrastingly, delve into the identity and shared experiences of communities, providing insights into \n",
+       "the social and cultural fabric of the time. Each approach offers a nuanced understanding of the poem's significance\n",
+       "within its historical context.\n",
+       "\n",
+       "End of GPT-4's Interpretation\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mGPT-\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1m's Interpretation of \u001b[0m\u001b[1;32m'The Hayseed'\u001b[0m\u001b[1m:\u001b[0m\n", + "\n", + "**Step \u001b[1;36m1\u001b[0m - Contextualization:**\n", + "\n", + "The poem \u001b[32m\"The Hayseed\"\u001b[0m by Arthur L. Kellog, published on October \u001b[1;36m4\u001b[0m, \u001b[1;36m1890\u001b[0m, in the Farmers Alliance newspaper in \n", + "Nebraska, reflects the socio-political climate of late 19th-century America, a tumultuous period characterized by \n", + "rapid industrialization, the rise of monopolies, and the struggles of the agrarian community against economic \n", + "exploitation. The Farmers Alliance was part of a broader agrarian movement, which included the Populist Party, that\n", + "emerged in response to the growing power of railroads and large corporate interests that appeared to prioritize \n", + "industrial over agricultural development. This era saw the rise of what would be known as the \u001b[32m\"Gilded Age,\"\u001b[0m a \n", + "period marked by economic disparity, political corruption, and the tug-of-war between reformists and established \n", + "economic interests. Key events of this period include the McKinley Tariff, which exacerbated economic woes for \n", + "farmers by elevating domestic product prices, and the subsequent Panic of \u001b[1;36m1893\u001b[0m, which underscored economic \n", + "vulnerabilities in the agricultural sector. The personalities influential in this period included William Jennings \n", + "Bryan, a champion of the agrarian cause, and political figures like Grover Cleveland, who often seemed aligned with\n", + "big business interests.\n", + "\n", + "**Step \u001b[1;36m2\u001b[0m - Purpose:**\n", + "\n", + "The purpose of Kellog's work was to articulate and amplify the frustrations of the common farmer, the so-called \n", + "\u001b[32m\"hayseed,\"\u001b[0m against the entrenched economic powers that exploited them. By employing the format of a song that could\n", + "be easily disseminated and understood by the largely rural and perhaps minimally literate farmer population, Kellog\n", + "aimed to inspire solidarity and mobilize these individuals to become politically active against corporate and \n", + "political exploitation. The specific references to \u001b[32m\"railroads\"\u001b[0m and \u001b[32m\"old party bosses\"\u001b[0m highlight the systemic nature\n", + "of the oppression faced by farmers and seek to draw attention to a perceived need for agrarian political reform. \n", + "The piece aligns with the broader goals of the Farmers Alliance and later the Populist Party, aiming to coalesce \n", + "farmer discontent into a political force capable of challenging the status quo.\n", + "\n", + "**Step \u001b[1;36m3\u001b[0m - Audience:**\n", + "\n", + "The intended audience for Kellog's poem was primarily the agrarian members of the Farmers Alliance and wider rural \n", + "communities in the Midwest, who were adversely affected by the industrial economy's growth. This audience was \n", + "largely composed of smallholder farmers who felt marginalized by the economic and political systems favoring urban \n", + "industrialists and railroads. Given their shared experiences and grievances, the audience would likely receive the \n", + "poem with empathy and conviction, supporting collective political action. This piece also served to bolster the \n", + "morale and sense of identity among farmers, reinforcing a common cause against perceived oppression.\n", + "\n", + "**Step \u001b[1;36m4\u001b[0m - Historiographical Interpretation:**\n", + "\n", + "- **Progressive Historiography:** Progressive historians might interpret this source as part of the broader \n", + "narrative of reform movements challenging entrenched interests during the Gilded Age. They would emphasize how the \n", + "poem exemplifies the struggle against monopolistic power structures and the call for political reform, focusing on \n", + "the contribution of grassroots movements like the Farmers Alliance to American reform traditions.\n", + "\n", + "- **Marxist Historiography:** Marxist historians would likely view this poem as an articulation of class struggle, \n", + "highlighting the conflict between the proletarian agrarian class and the bourgeois industrial monopolists, \n", + "including railroad magnates. They would analyze the poem as a reflection of the exploitation and socio-economic \n", + "alienation faced by farmers, and a call to action that hints at rising class consciousness among rural workers.\n", + "\n", + "- **Cultural Historiography:** Cultural historians might examine Kellog's poem as a cultural artifact illustrating \n", + "the identity and values of the agrarian community during the late 19th century. They would focus on the linguistic \n", + "choices, the common trope of the \u001b[32m\"hayseed,\"\u001b[0m and how these elements communicate a sense of unity and shared plight, \n", + "reflecting the cultural dynamics and collective consciousness emerging within American rural societies.\n", + "\n", + "In comparing these perspectives, progressive historians focus on the reformist and institutional outcomes of such \n", + "movements, while Marxists are concerned with underlying class tensions and economic structures. Cultural \n", + "historians, contrastingly, delve into the identity and shared experiences of communities, providing insights into \n", + "the social and cultural fabric of the time. Each approach offers a nuanced understanding of the poem's significance\n", + "within its historical context.\n", + "\n", + "\u001b[1mEnd of GPT-\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1m's Interpretation\u001b[0m\n" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Code for running primary source analysis of the \"Hayseed\" with OpenAI's GPT-4o model.\n", + "\n", + "from openai import OpenAI\n", + "from rich.console import Console\n", + "\n", + "# Initialize the console for rich output\n", + "console = Console()\n", + "\n", + "# Initialize the OpenAI client\n", + "client = OpenAI()\n", + "\n", + "# Create the query for the LLM\n", + "query = client.chat.completions.create(\n", + " model=\"gpt-4o\",\n", + " messages=[\n", + " {\"role\": \"user\", \"content\": primary_source_analysis_prompt}\n", + " ]\n", + ")\n", + "\n", + "# Extract the output from the response\n", + "output = query.choices[0].message.content\n", + "\n", + "# Create formatted text for display\n", + "output_text = (\n", + " \"[bold]GPT-4's Interpretation of 'The Hayseed':[/bold]\\n\\n\"\n", + " f\"{output}\\n\\n\"\n", + " \"[bold]End of GPT-4's Interpretation[/bold]\"\n", + ")\n", + "\n", + "# Display the formatted output\n", + "console.print(output_text, width=console.size.width)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "citation-manager": { + "citations": { + "rxaxc": [ + { + "id": "27937/G5ESJ8NI", + "source": "zotero" + } + ] + } + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "While one can debate aspects of GPT-4’s interpretations, it nonetheless accurately captures much of the context and intent of the source. With the right design, LLMs could be automated to annotate an entire corpus of archival sources in a similar manner, becoming a tool of the digital historian overwhelmed by an abundance of historical data, as envisioned by Roy Rozenweig twenty years ago. Yet LLM outputs and hallucinations are already contributing to this deluge of data. Both benign and malicious use of these technologies are impacting our understanding of the past and ability to comprehend the present. Historians should contribute to the broader dialogue about the implications and informed use of these technologies, especially as they become increasingly embedded in our digital lives. Further experimentation is also needed to more fully assess LLM’s capabilities for historical interpretation, as well as the creation of new benchmarks to test different approaches to historical analysis. But progress moves quickly in the field of generative AI, and there is intense competition to build new models that advance the existing capabilities of LLMs while shedding their shortcomings. Yet progress remains uneven. Of significant concern are LLM’s performance on benchmarks on ethics and morality, which continue to demonstrate troubling areas of weakness. (Hoffmann et al., “Training Compute-Optimal Large Language Models.”)\n", + "\n", + "While imperfect tools, their flaws do not mean that LLMs have no place in the historian’s toolkit. In fact, by acknowledging and confronting these shortcomings, historians can better contribute our disciplinary perspectives on the debates concerning this technology, particularly in leveraging the strengths of these models to empower and broaden accessibility to historical sources. The case studies below demonstrate how historians are using LLMs as a versatile tools for both researching and communicating the past." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "# Prompting LLMs for Digital History: Case Studies" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "citation-manager": { + "citations": { + "c1hh3": [ + { + "id": "27937/XEUKQDPE", + "source": "zotero" + } + ], + "zf8ru": [ + { + "id": "27937/VXGSAGTI", + "source": "zotero" + } + ] + } + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "A promising approach for LLMs and other foundational AI models lies in their capacity to assist data preparation and cleanup – a process that often constitutes an estimated 80% of the labor involved in preparing data for analysis. (Dasu and Johnson, Exploratory Data Mining and Data Cleaning.) Digitized historical materials frequently require transcriptions, error correction, and the creation of extensive metadata. These essential but time-consuming tasks can become research bottlenecks. However, through use of simple prompting techniques historians can leverage the power of LLMs to streamline and accelerate the creation of “tidy datasets” possessing standardized and ordered structures essential for analysis and replication. (Wickham, “Tidy Data.”)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## Case Study: Oral History Transcriptions" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "citation-manager": { + "citations": { + "d9rvh": [ + { + "id": "27937/7VHKCH3M", + "source": "zotero" + } + ], + "etf8p": [ + { + "id": "27937/YWJAQ4V8", + "source": "zotero" + } + ] + } + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "Oral history provides a particularly useful case study for demonstrating the potential utility of generative AI. Transcribing audio recordings is a central activity in this methodology, a task typically requiring significant time and labor. As one oral history guide notes, transcribing a single hour typically requires six to eight hours of manual processing and review. (Ritchie, Doing Oral History.) However, advances in specialized generative AI models permits significant streamlining of this task.\n", + "\n", + "Notable among these models is Whisper, an open-source audio transcription and translation model developed by OpenAI that belongs to the same Transformer family as the GPT series. (Radford et al., “Robust Speech Recognition via Large-Scale Weak Supervision.”) Trained on over 180,000 hours of audio recordings, this model demonstrates performance comparable (and in some cases exceeding) the accuracy rates of human transcriptions in over 57 languages. In this test we’ll examine Whisper’s performance on the first two minutes of a transcribed oral history of historian John Hope Franklin by the Southern Oral History Program. Recorded on audiotape in 1990, this segment features multiple voices, crosstalk, filler words, and background noise - typical features for many oral history recordings, but features that nonetheless complicate efforts to create accurate transcripts. In the code below, we will use Whisper to transcribe the audio segment and compare it against the official transcript. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "citation-manager": { + "citations": { + "kav0z": [ + { + "id": "27937/I2BKP7MN", + "source": "zotero" + } + ] + } + }, + "collapsed": false, + "editable": true, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hermeneutics" + ] + }, + "source": [ + "The Whisper series is offered as a series of open-source voice recognition and voice translation models across several tiers of computing power and freely available on sites like [HuggingFace](https://huggingface.co/docs/transformers/en/model_doc/whisper). However, for simplicity this demonstration code uses OpenAI’s API for the Whisper-2-large model. As of September 2024, OpenAI charged $0.36 per hour of recorded time for transcriptions using the API.\n", + "\n", + "For a detailed and informative tutorial on using and analyzing Whisper, see: (Schultz, “[Tutorial] Using Whisper to Transcribe Oral Interviews – CSS @ IPP.”)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Whisper Transcription time: 6.89 seconds\n",
+       "\n",
+       "Estimated Transcription Time for an hour recording at this rate: 0 hours, 2 minutes, 42 seconds\n",
+       "\n",
+       "Raw Whisper Transcript\n",
+       "What I'd like to do, I'd like to, I know, I know your historical personal background about your parents meeting at \n",
+       "Walden and you know we've talked about that at Roger Williams, we've talked about that before and about how you got\n",
+       "to Nashville from Oklahoma and all that, but I want to kind of pick up about the time when you were an \n",
+       "undergraduate at Fisk in the 30s and ask you first a couple of things. One, do you recall any meetings, interracial\n",
+       "meetings that took place on the Vanderbilt campus during those years? No. Never happened? No. Never happened so far\n",
+       "as I know. At Fisk, yes, but at Vanderbilt, no. That's right. The people from Vanderbilt would come over there but \n",
+       "not the other way. That's right and I don't know whether you remember the famous meeting, maybe then I would have \n",
+       "to back up and say I know of one, where a number of people, distinguished sociologists, probably Robert Park and \n",
+       "people like that, I'm not certain who they were, they had a meeting out at Vanderbilt and I invited E. Franklin \n",
+       "Frazier out there. It might even have been a luncheon and I think Chancellor Kirkland learned about it with a \n",
+       "little bit of staggering. This would have been in that period when you were an undergraduate? It would have been \n",
+       "because you see Frazier left at the end of my junior year when we went to Howard in 1934. The other instance that I\n",
+       "remember at Vanderbilt was when in my senior year, in the spring of my senior year, I was an applicant for \n",
+       "admission to Harvard to go to graduate school and this is before the GRE. So they wanted to take a scholastic \n",
+       "aptitude test and I, and of course it was scheduled, like the GRE, at a certain time. At a certain place. And it \n",
+       "was at Vanderbilt and it was in a certain room on Vanderbilt campus. And I went there.\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mWhisper Transcription time:\u001b[0m \u001b[1;36m6.89\u001b[0m seconds\n", + "\n", + "\u001b[1mEstimated Transcription Time for an hour recording at this rate:\u001b[0m \u001b[1;36m0\u001b[0m hours, \u001b[1;36m2\u001b[0m minutes, \u001b[1;36m42\u001b[0m seconds\n", + "\n", + "\u001b[1mRaw Whisper Transcript\u001b[0m\n", + "\u001b[2mWhat I'd like to do, I'd like to, I know, I know your historical personal background about your parents meeting at \u001b[0m\n", + "\u001b[2mWalden and you know we've talked about that at Roger Williams, we've talked about that before and about how you got\u001b[0m\n", + "\u001b[2mto Nashville from Oklahoma and all that, but I want to kind of pick up about the time when you were an \u001b[0m\n", + "\u001b[2mundergraduate at Fisk in the 30s and ask you first a couple of things. One, do you recall any meetings, interracial\u001b[0m\n", + "\u001b[2mmeetings that took place on the Vanderbilt campus during those years? No. Never happened? No. Never happened so far\u001b[0m\n", + "\u001b[2mas I know. At Fisk, yes, but at Vanderbilt, no. That's right. The people from Vanderbilt would come over there but \u001b[0m\n", + "\u001b[2mnot the other way. That's right and I don't know whether you remember the famous meeting, maybe then I would have \u001b[0m\n", + "\u001b[2mto back up and say I know of one, where a number of people, distinguished sociologists, probably Robert Park and \u001b[0m\n", + "\u001b[2mpeople like that, I'm not certain who they were, they had a meeting out at Vanderbilt and I invited E. Franklin \u001b[0m\n", + "\u001b[2mFrazier out there. It might even have been a luncheon and I think Chancellor Kirkland learned about it with a \u001b[0m\n", + "\u001b[2mlittle bit of staggering. This would have been in that period when you were an undergraduate? It would have been \u001b[0m\n", + "\u001b[2mbecause you see Frazier left at the end of my junior year when we went to Howard in \u001b[0m\u001b[1;2;36m1934\u001b[0m\u001b[2m. The other instance that I\u001b[0m\n", + "\u001b[2mremember at Vanderbilt was when in my senior year, in the spring of my senior year, I was an applicant for \u001b[0m\n", + "\u001b[2madmission to Harvard to go to graduate school and this is before the GRE. So they wanted to take a scholastic \u001b[0m\n", + "\u001b[2maptitude test and I, and of course it was scheduled, like the GRE, at a certain time. At a certain place. And it \u001b[0m\n", + "\u001b[2mwas at Vanderbilt and it was in a certain room on Vanderbilt campus. And I went there.\u001b[0m\n" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Code for transcribing oral history segment with Whisper API\n", + "\n", + "import requests\n", + "from openai import OpenAI\n", + "import time\n", + "from rich.console import Console\n", + "from rich.console import Console\n", + "from rich.text import Text\n", + "\n", + "# Initialize the OpenAI client\n", + "client = OpenAI()\n", + "\n", + "# URL for the audio file on GitHub\n", + "audio_url = \"https://github.com/Dr-Hutchinson/jdh_submission/raw/refs/heads/main/media/A-0339_edited.mp3\"\n", + "\n", + "# Save location for the downloaded audio file\n", + "file_path = \"./A-0339_edited.mp3\"\n", + "\n", + "# Download the audio file and save it locally\n", + "response = requests.get(audio_url)\n", + "with open(file_path, \"wb\") as audio_file:\n", + " audio_file.write(response.content)\n", + "\n", + "# Measure the transcription time for the audio file\n", + "start_time = time.time()\n", + "\n", + "# Transcribe the audio\n", + "with open(file_path, \"rb\") as audio_file:\n", + " transcription = client.audio.transcriptions.create(\n", + " model=\"whisper-1\", \n", + " file=audio_file\n", + " )\n", + "whisper_transcript = transcription.text\n", + "\n", + "end_time = time.time()\n", + "\n", + "# Calculate the actual transcription time\n", + "automation_time = end_time - start_time\n", + "\n", + "# Calculate the estimated transcription time for 1 hour based on the transcription time for audio segment\n", + "audio_length_seconds = 153 # 2 minutes and 33 seconds in seconds\n", + "estimated_time_for_one_hour = (automation_time / audio_length_seconds) * 3600 # Time for 1 hour (3600 seconds)\n", + "\n", + "# Convert estimated time for better readability\n", + "hours = int(estimated_time_for_one_hour // 3600)\n", + "minutes = int((estimated_time_for_one_hour % 3600) // 60)\n", + "seconds = int(estimated_time_for_one_hour % 60)\n", + "\n", + "console = Console()\n", + "\n", + "# Outputs with rich formatting\n", + "output_text = (\n", + " f\"[bold]Whisper Transcription time:[/bold] {automation_time:.2f} seconds\\n\\n\"\n", + " f\"[bold]Estimated Transcription Time for an hour recording at this rate:[/bold] \"\n", + " f\"{hours} hours, {minutes} minutes, {seconds} seconds\\n\\n\"\n", + " f\"[bold]Raw Whisper Transcript[/bold]\\n\"\n", + " f\"[dim]{whisper_transcript}[/dim]\"\n", + ")\n", + "\n", + "# Print outputs\n", + "console.print(output_text, width=console.size.width)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "The code below generates a audio player to listen to the audio segment. Listen and follow along to observe Whisper’s performance." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "editable": true, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "sound-franklin-*" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Citation:\n",
+       "“John Hope Franklin and John Egerton, Conducted by Oral History Interview with John Hope Franklin, July 27, 1990. \n",
+       "Interview A-0339. Southern Oral History Program Collection (#4007).”\n",
+       "https://docsouth.unc.edu/sohp/A-0339/menu.html\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mCitation:\u001b[0m\n", + "\u001b[3m“John Hope Franklin and John Egerton, Conducted by Oral History Interview with John Hope Franklin, July \u001b[0m\u001b[1;3;36m27\u001b[0m\u001b[3m, \u001b[0m\u001b[1;3;36m1990\u001b[0m\u001b[3m. \u001b[0m\n", + "\u001b[3mInterview A-\u001b[0m\u001b[1;3;36m0339\u001b[0m\u001b[3m. Southern Oral History Program Collection \u001b[0m\u001b[1;3m(\u001b[0m\u001b[3m#\u001b[0m\u001b[1;3;36m4007\u001b[0m\u001b[1;3m)\u001b[0m\u001b[3m.”\u001b[0m\n", + "\u001b[2;4;94mhttps://docsouth.unc.edu/sohp/A-0339/menu.html\u001b[0m\n" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Audio\n", + "from rich.console import Console\n", + "\n", + "# Initialize the console for rich output\n", + "console = Console()\n", + "\n", + "# URL for the audio file on GitHub\n", + "audio_url = \"https://github.com/Dr-Hutchinson/jdh_submission/raw/refs/heads/main/media/A-0339_edited.mp3\"\n", + "\n", + "# Save location for the downloaded audio file\n", + "file_path = \"./A-0339_edited.mp3\"\n", + "\n", + "# Displaying citation\n", + "citation_text = (\n", + " \"[bold]Citation:[/bold]\\n\"\n", + " \"[italic]“John Hope Franklin and John Egerton, Conducted by Oral History Interview with John Hope Franklin, \"\n", + " \"July 27, 1990. Interview A-0339. Southern Oral History Program Collection (#4007).”[/italic]\\n\"\n", + " \"[dim]https://docsouth.unc.edu/sohp/A-0339/menu.html[/dim]\"\n", + ")\n", + "\n", + "# Print the citation with console output\n", + "console.print(citation_text, width=console.size.width)\n", + "\n", + "# Load and play the saved audio file\n", + "Audio(file_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "Based on the professional standard, this excerpt would take approximately fifteen to twenty minutes to manually transcribe. Whisper achieved this in less than ten seconds.\n", + "\n", + "How accurate is the model compared to a human-produced transcript? Due to the stochastic nature of these models, each time you run this code slightly different variations might occur, particularly in the most challenging segments. The code block below visualizes a sample transcription produced by Whisper that was annotated and compared against the original. Notable omissions and discrepancies are highlighted. Whisper’s accuracy is then calculated via a standard benchmark for audio transcription, the word error rate (WER)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "editable": true, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "

Original Transcript: (discrepancies in green)

\n", + " JOHN EGERTON: I know your historical, personal background, about your parents meeting at Walden. You know, we talked. . . .

JOHN HOPE FRANKLIN: At Roger Williams.

JOHN EGERTON: At Roger Williams. We've talked about that before, and about how you got to Nashville from Oklahoma and all that. But I want to kind of pick up about the time when you were an undergraduate at Fisk in the '30s, and ask you first, well, a couple of things. One, do you recall any meeting, interracial meetings, that took place on the Vanderbilt campus during those years?

JOHN HOPE FRANKLIN: No.

JOHN EGERTON: Never happened?

JOHN HOPE FRANKLIN: No, never happened so far as I know.

JOHN EGERTON: At Fisk, yes, but at Vanderbilt, no?

JOHN HOPE FRANKLIN: That's right.

JOHN EGERTON: The people from Vanderbilt would come over there, but not the other way around?

JOHN HOPE FRANKLIN: That's right. And I don't know whether you remember the famous meeting — maybe I would have to back up and say I know of one — where a number of people, distinguished sociologists, probably Robert Park, people like that. I'm not certain who they were. They had a meeting out at Vanderbilt and invited E. Franklin Frazier. It might even have been a luncheon. And I think Chancellor Kirkland learned about and simply blew his stack.

JOHN EGERTON: This would have been in that period when you were an undergraduate.

JOHN HOPE FRANKLIN: Yes. It would have been because, you see, Frazier left at the end of my junior year. Went to Harvard in 1934. Other incidents that I remember in Nashville and at Vanderbilt was when, in my senior year, the spring of my senior year, I was an applicant for admission to Harvard to go to graduate school. This is before the GRE's, you see. So they wanted me to take a scholastic Aptitude Test, and, of course, it was scheduled, like the GRE's, at a certain time and place. And it was at Vanderbilt, and it was in a certain room on Vanderbilt campus. I went there.
\n", + "
\n", + "
\n", + "

Whisper Transcript: (discrepancies in red)

\n", + " JOHN EGERTON: What I'd like to do, I'd like to, I know, I know your historical personal background about your parents meeting at Walden and you know we've talked about that at Roger Williams, we've talked about that before and about how you got to Nashville from Oklahoma and all that, but I want to kind of pick up about the time when you were an undergraduate at Fisk in the 30s and ask you first a couple of things. One, do you recall any meetings, interracial meetings that took place on the Vanderbilt campus during those years?

JOHN HOPE FRANKLIN: No.

JOHN EGERTON: Never happened?

JOHN HOPE FRANKLIN: No, never happened so far as I know.

JOHN EGERTON: At Fisk, yes, but at Vanderbilt, no.

JOHN HOPE FRANKLIN: That's right.

JOHN EGERTON: The people from Vanderbilt would come over there, but not the other way around?

JOHN HOPE FRANKLIN: That's right, and I don't know whether you remember the famous meeting, maybe then I would have to back up and say I know of one, where a number of people, distinguished sociologists, probably Robert Park and people like that, I'm not certain who they were, they had a meeting out at Vanderbilt and invited E. Franklin Frazier out there. It might even have been a luncheon, and I think Chancellor Kirkland learned about it.

JOHN EGERTON: This would have been in that period when you were an undergraduate?

JOHN HOPE FRANKLIN: Yes. It would have been because, you see, Frazier left at the end of my junior year. Went to Howard in 1934. Other incidents that I remember in international and at Vanderbilt was when, in my senior year, the spring of my senior year, I was an applicant for admission to Harvard to go to graduate school. This is before the GRE's, you see. So they wanted me to take a scholastic Aptitude Test, and, of course, it was scheduled, like the GRE's, at a certain time and place. And it was at Vanderbilt, and it was in a certain room on Vanderbilt campus, and I went there.
\n", + "
\n", + "
\n", + "

\n", + "
\n", + "

Word Error Rate (WER) for Whisper: 19.14%

\n", + "
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import requests\n", + "import re\n", + "from jiwer import wer\n", + "from IPython.display import display, HTML\n", + "\n", + "# Function to clean HTML tags\n", + "def clean_html(text):\n", + " return re.sub(r'<.*?>', '', text)\n", + "\n", + "# URLs for the transcripts on GitHub\n", + "original_file_url = \"https://raw.githubusercontent.com/Dr-Hutchinson/jdh_submission/refs/heads/main/media/revised_original_transcript_formatted.txt\"\n", + "whisper_file_url = \"https://raw.githubusercontent.com/Dr-Hutchinson/jdh_submission/refs/heads/main/media/revised_whisper_transcript_formatted.txt\"\n", + "\n", + "# Download and read the contents of the original and whisper transcripts\n", + "original_transcript = requests.get(original_file_url).text\n", + "whisper_transcript = requests.get(whisper_file_url).text\n", + "\n", + "# Clean the transcripts for WER calculation\n", + "cleaned_original_transcript = clean_html(original_transcript)\n", + "cleaned_whisper_transcript = clean_html(whisper_transcript)\n", + "\n", + "# Calculate the Word Error Rate (WER)\n", + "error_rate = wer(cleaned_original_transcript, cleaned_whisper_transcript)\n", + "\n", + "# Add
tags to preserve line breaks in the text\n", + "original_transcript = original_transcript.replace('\\n', '
')\n", + "whisper_transcript = whisper_transcript.replace('\\n', '
')\n", + "\n", + "# Ensure that color highlighting also includes bolding\n", + "whisper_transcript = whisper_transcript.replace(\n", + " 'style=\"background-color: #fbb;\"',\n", + " 'style=\"background-color: #fbb; font-weight: bold;\"'\n", + ")\n", + "\n", + "original_transcript = original_transcript.replace(\n", + " 'style=\"background-color: #bfb;\"',\n", + " 'style=\"background-color: #bfb; font-weight: bold;\"'\n", + ")\n", + "\n", + "# Display the two transcripts side by side using HTML in Jupyter\n", + "html_content = f'''\n", + "
\n", + "
\n", + "

Original Transcript: (discrepancies in green)

\n", + " {original_transcript}\n", + "
\n", + "
\n", + "

Whisper Transcript: (discrepancies in red)

\n", + " {whisper_transcript}\n", + "
\n", + "
\n", + "

\n", + "
\n", + "

Word Error Rate (WER) for Whisper: {error_rate:.2%}

\n", + "
\n", + "'''\n", + "\n", + "# Render the HTML content in Jupyter\n", + "display(HTML(html_content))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "citation-manager": { + "citations": { + "4y95j": [ + { + "id": "27937/MYFQUX4C", + "source": "zotero" + } + ], + "7mtvf": [ + { + "id": "27937/KKDPZJYW", + "source": "zotero" + } + ], + "a22ms": [ + { + "id": "27937/4ITT4MQK", + "source": "zotero" + } + ], + "yq0ts": [ + { + "id": "27937/UHZYQM3W", + "source": "zotero" + } + ] + } + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "There are some suggestive observations we can take from these results. Closer inspection of the Whisper transcript shows some errors, a significant omission, differences in syntax, and literal transcriptions that an edited transcript would likely leave out. But given the media format and its audio quality, the oral historian has a solid first draft in just a few seconds. While the WER score indicates a need for final human review, that review will take considerably less effort and enable oral historians to shift their focus to interpreting, annotating, and validating their transcriptions. And even the best human transcriptions still contains errors. Take note of the final paragraph in the original transcript, which names Harvard as the destination of E. Franklin Frazier in 1934; but the noted sociologist actually joined the faculty of Howard University. Here Whisper accurately corrects a human error in the transcription. \n", + "\n", + "Applications like Whisper are already changing the field of oral history, as well as journalism, court reporting, and language translation. (Somers, “Whispers of A.I.’s Modular Future | The New Yorker.”) Scholars are using these techniques to complete multi-lingual transcriptions of aging and vulnerable media (Lehečka et al., “Transformer-Based Speech Recognition Models for Oral History Archives in English, German, and Czech.”) while also enabling new forms of community-based scholarship and teaching. (Rochester Institute of Technology, “Artificial Intelligence Aids Cultural Heritage Researchers Documenting and Teaching Oral Histories.”) Like other forms of generative AI, Whisper is prone to bias, error, and hallucination. (Koenecke et al., “Careless Whisper.”) Scholars should use this model to augment human review, and not replace it.\n", + "\n", + "Generative AI is being used to process and transform other media types as well. Just as Whisper transforms spoken language into text, LLMs can be leveraged to process visual information, such as printed or handwritten text, to aid in source digitization." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## Case Study: Error Correction of Optical Character Recognition Scans" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "citation-manager": { + "citations": { + "0jirx": [ + { + "id": "27937/ZJW9AI49", + "source": "zotero" + } + ], + "b6864": [ + { + "id": "27937/TIAJYHF6", + "source": "zotero" + } + ] + } + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "Another potential use case for AI models in digital history is error correction of optical character recognition (OCR) scans. Machine learning techniques, such as those pioneered by the research team at Transkribus, have greatly enhanced the quality, speed, and cost-effectiveness of OCR for a broad range of historical texts. (Muehlberger et al., “Transforming Scholarship in the Archives through Handwritten Text Recognition.”) However, even high-fidelity OCR scans often produce errors that impact the accessibility and searchability of text collections. (Milligan, “Illusionary Order.”) In the examples below, we can observe how a LLM can be prompted to correct these errors.\n", + "\n", + "The following image is from a newspaper published in a German prisoner-of-war camp in Mississippi during World War II and later microfilmed by the Library of Congress." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false, + "editable": true, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "figure-lotse-6-30-1945-*" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/html": [ + "
Source: “Nur ein Film?,” Die Lotse (Camp McCain, Mississippi), 30 June 1945.\n",
+       "In: Karl John Richard Arndt, editor. German P.O.W. Camp Papers. (Washington, D.C.: Library of Congress \n",
+       "Photoduplication Service, 1965). Reel 9.\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mSource:\u001b[0m \u001b[3m“Nur ein Film?,”\u001b[0m \u001b[3mDie Lotse\u001b[0m \u001b[1m(\u001b[0mCamp McCain, Mississippi\u001b[1m)\u001b[0m, \u001b[1;36m30\u001b[0m June \u001b[36m1945\u001b[0m.\n", + "In: Karl John Richard Arndt, editor. \u001b[3mGerman P.O.W. Camp Papers\u001b[0m. \u001b[1m(\u001b[0mWashington, D.C.: Library of Congress \n", + "Photoduplication Service, \u001b[36m1965\u001b[0m). Reel \u001b[36m9\u001b[0m.\n" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from PIL import Image\n", + "from IPython.display import display, Image as IPImage\n", + "from rich.console import Console\n", + "\n", + "# Initialize the console for rich output\n", + "console = Console()\n", + "\n", + "# Load and resize the image\n", + "ocr2_url = 'https://raw.githubusercontent.com/Dr-Hutchinson/jdh_submission/refs/heads/main/media/die_lotse_6-30-45_1.png'\n", + "image2 = Image.open('./die_lotse_6-30-45_1.png')\n", + "\n", + "# Set new dimensions for the resized image\n", + "new_width = 600\n", + "new_height = int(image2.height * (new_width / image2.width))\n", + "\n", + "# Resize the image for better visualization\n", + "resized_image = image2.resize((new_width, new_height), Image.LANCZOS)\n", + "\n", + "# Convert the PIL image to a format compatible with IPython display \n", + "resized_image.save(\"/tmp/resized_image.png\") \n", + "display(IPImage(filename=\"/tmp/resized_image.png\")) \n", + "\n", + "# Format the citation text using rich\n", + "citation_text = (\n", + " \"[bold]Source:[/bold] [italic]“Nur ein Film?,”[/italic] [italic]Die Lotse[/italic] (Camp McCain, Mississippi), 30 June [not bold]1945[/not bold].\\n\"\n", + " \"In: Karl John Richard Arndt, editor. [italic]German P.O.W. Camp Papers[/italic]. (Washington, D.C.: Library of Congress Photoduplication Service, [not bold]1965)[/not bold]. Reel [not bold]9[/not bold].\"\n", + ")\n", + "\n", + "# Display the formatted citation\n", + "console.print(citation_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "citation-manager": { + "citations": { + "5zf5d": [ + { + "id": "27937/KNEK45E4", + "source": "zotero" + } + ] + } + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "Let’s compare a human transcription of this image against outputs generated by Google’s Cloud Vision OCR service and GPT-4. In this code block GPT-4 is given the raw OCR output along with a [prompt](https://raw.githubusercontent.com/Dr-Hutchinson/jdh_submission/refs/heads/main/media/prompts/ocr_prompt.txt) for correcting OCR errors. This prompt includes examples of the task we wish the model to perform and is tailored to the type of data the model will encounter. This method, called few-shot prompting, is a common “prompt engineering” method for effectively guiding LLM generations. (Brown et al., “Language Models Are Few-Shot Learners.”) By again using the word error rate (WER), we can compare the relative accuracy of these different techniques against human performance. Discrepancies between the human-created transcription and the OCR outputs are highlighted." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "citation-manager": { + "citations": { + "s9wzk": [ + { + "id": "27937/5G5LJCLC", + "source": "zotero" + } + ], + "t1kfr": [ + { + "id": "27937/3H7M3AJ8", + "source": "zotero" + } + ] + } + }, + "collapsed": false, + "editable": true, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hermeneutics" + ] + }, + "source": [ + "Effectively utilizing LLMs for historical research extends beyond simply inputting text instructions; it demands an understanding of how to guide these models toward desired outcomes. This process, known as prompt engineering, has emerged as a critical skill for leveraging the power of LLMs. By carefully structuring instructions and providing relevant context, historians can shape LLM outputs to address a wide array of research needs.\n", + "\n", + "Prompt engineering is an iterative process. Experimentation is often required, using different prompt structures tailored to the dataset to achieve the desired outputs. One prominent prompt engineering technique is few-shot prompting. This method involves providing the LLM with a small number of examples demonstrating a specific task and desired output format. An emergent ability of LLMs called “in-context learning” allows LLMs to adapt their approach based on a few carefully chosen examples. Such examples can significantly improve the model’s ability to generalize to new, unseen data. This technique is especially valuable for tasks where explicit rules are difficult to define, allowing the LLM to learn from demonstration rather than strict programming. \n", + "\n", + "Few-shot prompting is among the most common of a rapidly growing number of prompt approaches. (Vatsal and Dubey, “A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks.”) Regardless of the technique, effective prompt prompting for historical research does not require extensive technical skill, but instead benefits from clear communication and the application of specific domain knowledge. As LLMs become increasingly integrated into digital historical practice, the ability to craft effective prompts may join skills like text analysis and data visualization as useful components of the digital historian’s toolkit.\n", + "\n", + "To learn more about the practices of prompt engineering, a good starting place is DAIR.AI’s Prompt Engineering Guide, which lays out accessible examples for various prompting approaches. (Saravia, Prompt Engineering Guide.)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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Human Corrected Transcript (Corrections in green):

\n", + "
NUR EIN FILM? Unverloeschbar tief haben sich uns die Bilder des Grauens eingepraegt, die jeder von uns dieser Tage in dem ersten amerikanischen Armeefilm aus Deutschland sah. Erschuetterung und Entsetzen haben jeden Fuehlenden verstummen lassen, aber die Unmenschlichkeit, von \"Deutschen\" auf deutschem Boden begangen, laesst den Gesitteten nicht schweigend darueberhingehen. Dante setzt in seinem Werk: \"Die goettliche Komoedie\" ueber den Eingang zur Hoelle die Worte: \"Lasst fahren alle Hoffnungen ihr, die ihr hier eintritt.\" Diese Worte koennen ueber jedem K.Z.-Lager Deutschlands gestanden haben; denn die Bilder des Schreckens und Grauens, wie sie Dante von der Hoelle entwirft, verblassen vor dieser schaurigen Wirklichkeit, die sich hier auf Erden unter lebenden Menschen im Herzen Europas abspielte. Was wir sahen, war dabei wohl nur ein kleiner Ausschnitt, wenn wir bedenken, dass diese Tragoedie seit 1933 unzaehlige Opfer forderte.
\n", + "
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\n", + "

OCR Transcript (Errors in red):

\n", + "
NUR EIN Unverloeschbar tief haben sich uns die Bilder des Grauens einge- praegt, die jeder von uns dieser Tage in dem ersten amerikanischen Armeefilm aus Deutschland sah. Er schuetterung und Ent setzen haben Jeden Fuehlenden verstummen las sen, aber die Unmenschlichkeit, von \"Deutschen\" auf deutschem Bo- den begangen, lassst den Gesitte- ten nicht schweigend darueberhin- gehen. Dante setzt in seinem Werk: 1 \"Die goettliche Komoedie\" ueber den Eingang zur Hoelle die Worte: \"Lasst fahren alle Hoffnungen 1hr, die ihr hier eintritt.\" Diese Worte koonnen ueber je- dem K.Z.-Lager Deutschlands ge standen haben; denn die Bilder des Schreckens und Grauens, wie sie Dante von der Hoelle entwirft, ver- blassen vor dieser schaurigen Wirklichkeit, die sich hier auf Er den unter lebenden Menschen im Herzen Europas abspielte. Was wir sahen, war dabei wohl nur ein kleiner Ausschnitt, wenn wir beden- ken, dass diese Tragoedie seit 1933 unzaehlige Opfer forderte.
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\n", + "
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Word Error Rate (WER) for OCR Transcript: 22.22%

" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/html": [ + "\n", + "
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Human Corrected Transcript (Corrections in green):

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NUR EIN FILM? Unverloeschbar tief haben sich uns die Bilder des Grauens eingepraegt, die jeder von uns dieser Tage in dem ersten amerikanischen Armeefilm aus Deutschland sah. Erschuetterung und Entsetzen haben jeden Fuehlenden verstummen lassen, aber die Unmenschlichkeit, von \"Deutschen\" auf deutschem Boden begangen, laesst den Gesitteten nicht schweigend darueberhingehen. Dante setzt in seinem Werk: \"Die goettliche Komoedie\" ueber den Eingang zur Hoelle die Worte: \"Lasst fahren alle Hoffnungen ihr, die ihr hier eintritt.\" Diese Worte koennen ueber jedem K.Z.-Lager Deutschlands gestanden haben; denn die Bilder des Schreckens und Grauens, wie sie Dante von der Hoelle entwirft, verblassen vor dieser schaurigen Wirklichkeit, die sich hier auf Erden unter lebenden Menschen im Herzen Europas abspielte. Was wir sahen, war dabei wohl nur ein kleiner Ausschnitt, wenn wir bedenken, dass diese Tragoedie seit 1933 unzaehlige Opfer forderte.
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GPT-4 Corrected Transcript (Errors in red):

\n", + "
NUR EIN Unverloeschbar tief haben sich uns die Bilder des Grauens einge- praegt, die jeder von uns dieser Tage in dem ersten amerikanischen Armeefilm aus Deutschland sah. Erschuetterung und Entsetzen haben jeden Fuehlenden verstummen lassen, aber die Unmenschlichkeit, von \"Deutschen\" auf deutschem Boden begangen, laesst den Gesitteten nicht schweigend darueber hingehen. Dante setzt in seinem Werk: \"Die goettliche Komoedie\" ueber den Eingang zur Hoelle die Worte: \"Lasst fahren alle Hoffnungen, ihr, die ihr hier eintretet.\" Diese Worte koennen ueber jedem K.Z.-Lager Deutschlands gestanden haben; denn die Bilder des Schreckens und Grauens, wie sie Dante von der Hoelle entwirft, verblassen vor dieser schaurigen Wirklichkeit, die sich hier auf Erden unter lebenden Menschen im Herzen Europas abspielte. Was wir sahen, war dabei wohl nur ein kleiner Ausschnitt, wenn wir bedenken, dass diese Tragoedie seit 1933 unzaehlige Opfer forderte.
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\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/html": [ + "

Word Error Rate (WER) for GPT-4 Corrected Transcript: 5.19%

" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import requests\n", + "import difflib\n", + "import re\n", + "from jiwer import wer\n", + "from IPython.display import display, HTML\n", + "from openai import OpenAI\n", + "\n", + "# Function to fetch text content from a URL\n", + "def fetch_text_from_url(url):\n", + " response = requests.get(url)\n", + " response.raise_for_status() # Ensure the request was successful\n", + " return response.text.strip()\n", + "\n", + "# Function to annotate differences between two texts\n", + "def annotate_differences(diff, target):\n", + " result = []\n", + " for word in diff:\n", + " if word.startswith('+') and target == 'ocr':\n", + " result.append(f'{word[2:]}')\n", + " elif word.startswith('-') and target == 'human':\n", + " result.append(f'{word[2:]}')\n", + " elif word.startswith(' '):\n", + " result.append(word[2:])\n", + " return ' '.join(result)\n", + "\n", + "# Function to calculate Word Error Rate (WER)\n", + "def calculate_wer(original_text, compared_text):\n", + " error_rate = wer(original_text, compared_text)\n", + " return f'{error_rate:.2%}'\n", + "\n", + "# Function to display annotated transcripts side by side\n", + "def display_side_by_side(ocr_output, corrected_output, title_ocr, title_corrected):\n", + " # Use difflib to identify differences\n", + " differ = difflib.Differ()\n", + " diff = list(differ.compare(ocr_output.split(), corrected_output.split()))\n", + " \n", + " # Create annotated versions of the transcripts\n", + " ocr_annotated = annotate_differences(diff, target='ocr')\n", + " corrected_annotated = annotate_differences(diff, target='human')\n", + " \n", + " # Construct HTML to display the two annotated versions side by side\n", + " html_content = f'''\n", + "
\n", + "
\n", + "

{title_ocr}:

\n", + "
{ocr_annotated}
\n", + "
\n", + "
\n", + "

{title_corrected}:

\n", + "
{corrected_annotated}
\n", + "
\n", + "
\n", + " '''\n", + " \n", + " # Display the HTML content in Jupyter\n", + " display(HTML(html_content))\n", + "\n", + "# Function to run the whole comparison workflow\n", + "def run_comparison(ocr_output, corrected_output, title_ocr, title_corrected):\n", + " # Display side-by-side comparison\n", + " display_side_by_side(ocr_output, corrected_output, title_ocr, title_corrected)\n", + " \n", + " # Create a clean title for WER by removing any text in parentheses\n", + " wer_title_corrected = title_corrected.split('(')[0].strip()\n", + " \n", + " # Calculate and display WER\n", + " error_rate = calculate_wer(corrected_output, ocr_output)\n", + " display(HTML(f'

Word Error Rate (WER) for {wer_title_corrected}: {error_rate}

'))\n", + "\n", + "# Function to query GPT-4 for OCR corrections\n", + "def query_gpt4(ocr_prompt, ocr_output):\n", + " try:\n", + " response = client.chat.completions.create(\n", + " model=\"gpt-4o\",\n", + " messages=[{\"role\": \"user\", \"content\": ocr_prompt + \"\\n\" + ocr_output}]\n", + " )\n", + " return response.choices[0].message.content\n", + " except Exception as e:\n", + " return str(e) \n", + "\n", + "# URLs for the OCR outputs, human corrections, and prompt for GPT-4\n", + "file_urls = {\n", + " \"ocr_1\": \"https://raw.githubusercontent.com/Dr-Hutchinson/jdh_submission/refs/heads/main/media/die_lotse_1_ocr_output.txt\",\n", + " \"corrected_1\": \"https://raw.githubusercontent.com/Dr-Hutchinson/jdh_submission/refs/heads/main/media/die_lotse_1_human_correction.txt\",\n", + " \"ocr_2\": \"https://raw.githubusercontent.com/Dr-Hutchinson/jdh_submission/refs/heads/main/media/die_lotse_2_ocr_output.txt\",\n", + " \"corrected_2\": \"https://raw.githubusercontent.com/Dr-Hutchinson/jdh_submission/refs/heads/main/media/die_lotse_2_human_correction.txt\",\n", + " \"ocr_prompt\": \"https://raw.githubusercontent.com/Dr-Hutchinson/jdh_submission/refs/heads/main/media/prompts/ocr_prompt.txt\"\n", + "}\n", + "\n", + "# Initialize the OpenAI client\n", + "client = OpenAI()\n", + "\n", + "# Load content from URLs\n", + "ocr_output_1 = fetch_text_from_url(file_urls[\"ocr_1\"])\n", + "human_corrected_output_1 = fetch_text_from_url(file_urls[\"corrected_1\"])\n", + "ocr_prompt = fetch_text_from_url(file_urls[\"ocr_prompt\"])\n", + "\n", + "# Make API Call for GPT-4 Correction of OCR Output\n", + "gpt4_corrected_output_1 = query_gpt4(ocr_prompt, ocr_output_1)\n", + "\n", + "# Block 1: Human-corrected transcript vs OCR output\n", + "run_comparison(\n", + " ocr_output_1,\n", + " human_corrected_output_1,\n", + " \"Human Corrected Transcript (Corrections in green)\",\n", + " \"OCR Transcript (Errors in red)\",\n", + ")\n", + "\n", + "# Block 2: Human-corrected transcript vs GPT-4 output\n", + "run_comparison(\n", + " gpt4_corrected_output_1,\n", + " human_corrected_output_1,\n", + " \"Human Corrected Transcript (Corrections in green)\",\n", + " \"GPT-4 Corrected Transcript (Errors in red)\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "While the image quality is satisfactory and the text is printed using modern typefaces, the OCR scan still generates errors requiring human correction. Correcting even minor errors necessitates review, representing significant labor when processing a sizable text corpus. The LLM accelerates that task in this case by correcting OCR errors ahead of human review, particularly when guided by detailed instructions and a few examples tailored to the dataset or OCR task.\n", + "\n", + "However, there are limits to this prompt engineering technique. Accuracy falls for both OCR models and LLMs alike when processing images containing considerable ‘noise’ and distortion, as in the image below. But recent LLMs like GPT-4 have been trained on multi-modal data, allowing them to process images as well as text. In the code below, GPT-4 is fed a specialized [prompt](https://raw.githubusercontent.com/Dr-Hutchinson/jdh_submission/refs/heads/main/media/prompts/gpt_vision_prompt.txt), a [few examples](https://raw.githubusercontent.com/Dr-Hutchinson/jdh_submission/refs/heads/main/media/prompts/vision_few_shot.txt), the raw OCR output, as well as the original image to help guide the model in correcting OCR errors." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false, + "editable": true, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "figure-lotse-3-15-1945-*" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/html": [ + "
Source: “Zum Geleit,” *Die Lotse* (Camp McCain, Mississippi), 15 March 1945.\n",
+       "In: Karl John Richard Arndt, editor. *German P.O.W. Camp Papers*. (Washington, D.C.: Library of Congress \n",
+       "Photoduplication Service, 1965). Reel 9.\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mSource:\u001b[0m \u001b[3m“Zum Geleit,”\u001b[0m \u001b[3m*Die Lotse*\u001b[0m \u001b[1m(\u001b[0mCamp McCain, Mississippi\u001b[1m)\u001b[0m, \u001b[1;36m15\u001b[0m March \u001b[36m1945\u001b[0m.\n", + "In: Karl John Richard Arndt, editor. \u001b[3m*German P.O.W. Camp Papers*\u001b[0m. \u001b[1m(\u001b[0mWashington, D.C.: Library of Congress \n", + "Photoduplication Service, \u001b[36m1965\u001b[0m). Reel \u001b[36m9\u001b[0m.\n" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from PIL import Image\n", + "from IPython.display import display, Image as IPImage\n", + "from rich.console import Console\n", + "\n", + "# Initialize the console for rich output\n", + "console = Console()\n", + "\n", + "# Load and resize the image\n", + "ocr2_url = 'https://raw.githubusercontent.com/Dr-Hutchinson/jdh_submission/refs/heads/main/media/die_lotse_3-15-45_1.png'\n", + "image2 = Image.open('./die_lotse_3-15-45_1.png')\n", + "\n", + "# Set new dimensions for the resized image\n", + "new_width = 600\n", + "new_height = int(image2.height * (new_width / image2.width))\n", + "\n", + "# Resize the image for better visualization\n", + "resized_image = image2.resize((new_width, new_height), Image.LANCZOS)\n", + "\n", + "# Prepare the image for IPython display\n", + "resized_image.save(\"/tmp/resized_image.png\") \n", + "display(IPImage(filename=\"/tmp/resized_image.png\")) \n", + "\n", + "# Create formatted citation text with rich\n", + "citation_text = (\n", + " \"[bold]Source:[/bold] [italic]“Zum Geleit,”[/italic] [italic]*Die Lotse*[/italic] (Camp McCain, Mississippi), 15 March [not bold]1945[/not bold].\\n\"\n", + " \"In: Karl John Richard Arndt, editor. [italic]*German P.O.W. Camp Papers*[/italic]. (Washington, D.C.: Library of Congress Photoduplication Service, [not bold]1965)[/not bold]. Reel [not bold]9[/not bold].\"\n", + ")\n", + "\n", + "# Display the formatted citation\n", + "console.print(citation_text)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "

Human Corrected Transcript (Corrections in green):

\n", + "
Zum Geleit: Die neue Lagerzeitung ist nun erschienen. Ja sie ist nunmehr eine unlengoare Tatsache geworden und was die Oshirne [?] der Prisoner in stillen Stunden und in froher Laune ersonnen, hier findet ihr es schwarz auf weiss. Ueber manches moechtet ihr nachdenken, ueber manches euch freuen, belaecheln koennt ihr aller, aber denkt daran wie man es besser machen koennte und seit mit Vorschlaegen nicht geizig und zurueckhaltend. Alles, was euch bewegt, Ernstes und Heiteres, soll seinen Platz finden in diesen Blaettern, nur Politik lasst ferne. Wenn euch diese Zeitung Ermunterung, Unterhaltung und Anregung geben, so ist das der schoenste Lohn fuer die Muehe aller, die um das Zustandekommen dieser Lagerzeitung bemueht war'n. Nochmals, jeder arbeite mit an diesem schoenen Werk, nach der Parole “Alles von Prisoner fuer Prisoner” wollen wir die Zeitung fuehren. Das Erscheinen ist monatlich zweimal vorgesehen. Einsendungen werden nach Hasnabe des verfuegbaren Platzes aufgenommen, wobei kein besonders kritischer Massstab bezueglich der künstlerischen Vollendung angelegt wird, immerhin denkt daran sie viele Kameraden eure Geistesprodukte lesen und wir doch eine Auswahl treffen muessen. Die Schriftleitung.
\n", + "
\n", + "
\n", + "

OCR Transcript (Errors highlighted in red):

\n", + "
Zum Deleit: Die neue Lagerzeitung ist nun erschienen. Ja eis ist nun ehr eine unengoare Totesche reworden und we ate anime der Prisoner in stilien Stunden und in froler Laune ersonnon, ier findet ihr es schwarz auf weiss. Ueber manches moschtet ihr nachdenken, ueber manches euch freuen, belaecheln koennt ihr aller, aber denkt iaren wie an es besser nachen koennte und seit mit Vorschlaegen nicht geizig und zurueckhaltend. Alles, or euch bewegt, arnstes und Heiteres, soll seinen Platz Tinden in dieren. Blaettern, nur Politik lasst ferne. Wenn euch diese Zeitung Errunterung Unterhaltung und Anregung Ceben, so ist das Cer rchoenste Loin fuer die Nuehe aller, die um das Zustandekommen dieser Laerzeitung benueht war'n.wollen Noolimals, Jeder arbeite mit an diesen schoenen Werk, nach der Parole Alles von Prisoner fuer Prisoner wir die Zeitung fuehren.\\nDas Erscheinen ist nonetlich zreimal vorgesehen. Einsendungen werden nach Hasnabe des verfuegberen Platzes aufgenommen, wobei kein besonders kritischer Kesesta oezue lich er kuenetlerischen Vollendun; an- Celest sird, inner in denkt daran sie viele Kameraden sure Geisteeprodukte lesen und wir doch eine Auerall treffen muessen. Die Sohriftleitung.
\n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/html": [ + "

Word Error Rate (WER) for OCR Transcript: 32.77%

" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "

Human Corrected Transcript (Corrections in green):

\n", + "
Zum Geleit: Die neue Lagerzeitung ist nun erschienen. Ja sie ist nunmehr eine unlengoare Tatsache geworden und was die Oshirne [?] der Prisoner in stillen Stunden und in froher Laune ersonnen, hier findet ihr es schwarz auf weiss. Ueber manches moechtet ihr nachdenken, ueber manches euch freuen, belaecheln koennt ihr aller, aber denkt daran wie man es besser machen koennte und seit mit Vorschlaegen nicht geizig und zurueckhaltend. Alles, was euch bewegt, Ernstes und Heiteres, soll seinen Platz finden in diesen Blaettern, nur Politik lasst ferne. Wenn euch diese Zeitung Ermunterung, Unterhaltung und Anregung geben, so ist das der schoenste Lohn fuer die Muehe aller, die um das Zustandekommen dieser Lagerzeitung bemueht war'n. Nochmals, jeder arbeite mit an diesem schoenen Werk, nach der Parole “Alles von Prisoner fuer Prisoner” wollen wir die Zeitung fuehren. Das Erscheinen ist monatlich zweimal vorgesehen. Einsendungen werden nach Hasnabe des verfuegbaren Platzes aufgenommen, wobei kein besonders kritischer Massstab bezueglich der künstlerischen Vollendung angelegt wird, immerhin denkt daran sie viele Kameraden eure Geistesprodukte lesen und wir doch eine Auswahl treffen muessen. Die Schriftleitung.
\n", + "
\n", + "
\n", + "

GPT-4 Corrected Transcript (Errors highlighted in red):

\n", + "
Zum Geleit: Die neue Lagerzeitung ist nun erschienen. Ja es ist nun eher eine unantastbare Tatsache geworden und wer etwas euer Inneren, der Prisoner in stillen Stunden und in froher Laune ersinnen, hier findet ihr es schwarz auf weiss. Ueber manches moechtet ihr nachdenken, ueber manches euch freuen, belaecheln koennt ihr alles, aber denkt daran wie man es besser machen koennte und seid mit Vorschlaegen nicht geizig und zurueckhaltend. Alles, was euch bewegt, Ernstes und Heiteres, soll seinen Platz finden in diesen Blaettern, nur Politik lasst fern. Wenn euch diese Zeitung Erinnerung, Unterhaltung und Anregung geben, so ist das der schoenste Lohn fuer die Muehe aller, die um das Zustandekommen dieser Lagerzeitung bemueht waren. Nocheinmal, Jeder arbeite mit an diesem schoenen Werk, nach der Parole Alles von Prisoner fuer Prisoner wird die Zeitung fuehren.\\nDas Erscheinen ist monatlich zweimal vorgesehen. Einsendungen werden nach Massgabe des verfuegbaren Platzes aufgenommen, wobei kein besonders kritischer Massstab hinsichtlich der kuenstlerischen Vollendung angest wird, sondern denkt daran wie viele Kameraden eure Geistesprodukte lesen und wir doch eine Auswahl treffen muessen. Die Schriftleitung.
\n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/html": [ + "

Word Error Rate (WER) for GPT-4 Corrected Transcript: 15.82%

" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "import base64\n", + "import requests\n", + "from PIL import Image\n", + "from IPython.display import display, HTML, Markdown\n", + "\n", + "# Function to fetch text content from a URL\n", + "def fetch_text_from_url(url):\n", + " response = requests.get(url)\n", + " response.raise_for_status() \n", + " return response.text.strip()\n", + "\n", + "# Function to encode the image to base64 for API call\n", + "def encode_image_from_url(image_url):\n", + " response = requests.get(image_url)\n", + " response.raise_for_status()\n", + " return base64.b64encode(response.content).decode('utf-8')\n", + "\n", + "# Function to prepare the payload for the API request, with OCR output and vision_few_shot included\n", + "def prepare_payload(encoded_image, prompt, ocr_output, image_filename):\n", + " # Combine gpt_vision_prompt, vision_few_shot, and OCR output into a single prompt\n", + " full_prompt = f\"{gpt_vision_prompt}\\n\\n{vision_few_shot}\\n\\nOCR Output:\\n{ocr_output}\\n\\nPlease correct the OCR errors for the image {image_filename}.\"\n", + " \n", + " return {\n", + " \"model\": \"gpt-4\",\n", + " \"messages\": [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": full_prompt\n", + " }\n", + " ],\n", + " \"max_tokens\": 1000\n", + " }\n", + "\n", + "# Function to make an API call to GPT-4 for OCR correction\n", + "def get_gpt4_ocr_output(payload):\n", + " response = requests.post(\"https://api.openai.com/v1/chat/completions\", headers=headers, json=payload)\n", + " response_json = response.json()\n", + " if 'choices' in response_json:\n", + " return response_json['choices'][0]['message']['content']\n", + " else:\n", + " print(\"Error in OCR:\", response_json)\n", + " return \"\"\n", + "\n", + "# URLs for the image, human-corrected transcription, OCR output, and prompts for GPT-4\n", + "file_urls = {\n", + " \"image_2\": \"https://raw.githubusercontent.com/Dr-Hutchinson/jdh_submission/refs/heads/main/media/die_lotse_3-15-45_1.png\",\n", + " \"corrected_2\": \"https://raw.githubusercontent.com/Dr-Hutchinson/jdh_submission/refs/heads/main/media/die_lotse_2_human_correction.txt\",\n", + " \"ocr_2\": \"https://raw.githubusercontent.com/Dr-Hutchinson/jdh_submission/refs/heads/main/media/die_lotse_2_ocr_output.txt\",\n", + " \"gpt_vision_prompt\": \"https://raw.githubusercontent.com/Dr-Hutchinson/jdh_submission/refs/heads/main/media/prompts/gpt_vision_prompt.txt\",\n", + " \"vision_few_shot\": \"https://raw.githubusercontent.com/Dr-Hutchinson/jdh_submission/refs/heads/main/media/prompts/vision_few_shot.txt\"\n", + "}\n", + "\n", + "# Load the human-corrected transcript and OCR output for image from URLs\n", + "human_corrected_output = fetch_text_from_url(file_urls[\"corrected_2\"])\n", + "ocr_output = fetch_text_from_url(file_urls[\"ocr_2\"])\n", + "\n", + "# Load gpt_vision_prompt and few shot examples from URLs\n", + "gpt_vision_prompt = fetch_text_from_url(file_urls[\"gpt_vision_prompt\"])\n", + "vision_few_shot = fetch_text_from_url(file_urls[\"vision_few_shot\"])\n", + "\n", + "# Encode image from the URL\n", + "encoded_image = encode_image_from_url(file_urls[\"image_2\"])\n", + "\n", + "# Load the API key from environment variable\n", + "api_key = os.getenv(\"OPENAI_API_KEY\")\n", + "\n", + "# Set up headers for the API request\n", + "headers = {\n", + " \"Content-Type\": \"application/json\",\n", + " \"Authorization\": f\"Bearer {api_key}\"\n", + "}\n", + "\n", + "# Prepare the payload and get GPT-4 OCR output for image\n", + "image_filename = os.path.basename(file_urls[\"image_2\"]) \n", + "payload = prepare_payload(encoded_image, gpt_vision_prompt, ocr_output, image_filename) \n", + "gpt4_ocr_output = get_gpt4_ocr_output(payload)\n", + "\n", + "# Run comparisons for image_2\n", + "\n", + "# Block 1: Human vs OCR\n", + "run_comparison(\n", + " ocr_output,\n", + " human_corrected_output,\n", + " \"Human Corrected Transcript (Corrections in green)\",\n", + " \"OCR Transcript (Errors highlighted in red)\"\n", + ")\n", + "\n", + "# Block 2: Human vs GPT-4 Vision\n", + "run_comparison(\n", + " gpt4_ocr_output,\n", + " human_corrected_output,\n", + " \"Human Corrected Transcript (Corrections in green)\",\n", + " \"GPT-4 Corrected Transcript (Errors highlighted in red)\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "citation-manager": { + "citations": { + "27nt7": [ + { + "id": "27937/ZXTQBIJU", + "source": "zotero" + } + ], + "6jdd5": [ + { + "id": "27937/JV9GGCQA", + "source": "zotero" + } + ], + "t1nqo": [ + { + "id": "27937/58X69RSW", + "source": "zotero" + } + ] + } + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "While GPT-4 provides a less accurate transcription for this difficult and degraded image, it did significantly correct and improve the initial OCR output. There are other means to achieve even better performance. Specialized LLMs trained specifically for OCR tasks, like Microsoft’s Phi model series, show great promise in advancing the fidelity of such corrections. (Abdin et al., “Phi-3 Technical Report.”) Similarly, Pleias, a French AI research group, has been applying LLMs at scale for post-OCR correction in larger text datasets such as the Common Corpus, which comprises 500 billion words of text scraped from the Internet. Their recent release of a multilingual, billion-word dataset of corrected OCR text includes English, French, German, and Italian texts from cultural heritage repositories such as Gallica and Chronicling America. (PleIAs, “PleIAs/Post-OCR-Correction · Datasets at Hugging Face.”) However, while the quality of these corrections has shown significant improvement, Pleias’ work also highlights potential limitations of LLM-based OCR correction. Early tests revealed issues such as language-switching (e.g., parts of English texts mistakenly corrected into French or German) and the risk of hallucinations. (Langlais, “Post-OCR-Correction.”) Despite these limitations, the potential for LLMs in post-OCR correction is significant. As LLMs continue to evolve, their capacity to perform tasks like OCR correction will likely improve, but human review will remain essential to ensure accuracy.\n", + "\n", + "These two case studies demonstrate LLMs' capacity to assist in various forms of data cleanup and preparation. While human review remains essential, LLMs can make that review less time-consuming and labor-intensive. Such approaches can improve the accuracy, lower the costs, and accelerate the pace of data preparation. The same is true for data extraction. In the following example, a LLM will demonstrate how such data can be captured by use of guided prompts." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## Case Study: Structured Data Extraction" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "citation-manager": { + "citations": { + "3y4ir": [ + { + "id": "27937/HIPL38QS", + "source": "zotero" + } + ], + "6161c": [ + { + "id": "27937/68YHDUH6", + "source": "zotero" + } + ] + } + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "Automated data extraction has increasingly become an important part of digital scholarship. Reseachers have utilized a variety of computational approaches for compiling ordered data from historical media. Named entity recognition (NER) is one such example, a method which uses machine learning models like [spaCy](https://spacy.io/usage/facts-figures) to extract locations, events, individuals, and even concepts from texts into ordered classifications. Such techniques have enabled the creation of rich metadata from archival collections (Chastang, Aguilar, and Tannier, “A Named Entity Recognition Model for Medieval Latin Charters.”) and museum catalogs. (Nikolova and Levy, “Using Named Entity Recognition to Enhance Access to a Museum Catalog – Document Blog.”) Similar techniques such as sentiment analysis, automated summarization, and machine translation have allowed for more detailed and granular examination of historical source collections, enabling researchers to more accurately pinpoint specific sources most pertinent to their interests. However, each of these extraction techniques usually requires specialized models tailored to each task. LLMs, in contrast, offers the possibility of a all-in-one tool for performing various forms of data extraction in a single inference.\n", + "\n", + "To demonstrate, in the following code block a LLM extracts metadata from the earlier oral history transcription and the OCR correction through use of a another [prompt](https://raw.githubusercontent.com/Dr-Hutchinson/jdh_submission/refs/heads/main/media/prompts/metadata_extraction_prompt.txt) containing examples for the LLM to emulate. For each source the LLM will offer a brief summary, perform sentiment analysis, and extract keywords for NER classifications in both English and German. This data is then structured into a machine-usable format as a JSON object." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "citation-manager": { + "citations": { + "7amub": [ + { + "id": "27937/LC63DETW", + "source": "zotero" + } + ], + "kwh7q": [ + { + "id": "27937/Z44J4BKC", + "source": "zotero" + } + ], + "l0c94": [ + { + "id": "27937/5ED45HQE", + "source": "zotero" + } + ], + "mfahj": [ + { + "id": "27937/LC63DETW", + "source": "zotero" + } + ], + "z20ld": [ + { + "id": "27937/5ED45HQE", + "source": "zotero" + } + ] + } + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Metadata Extraction for Oral History Transcript\n",
+       "\n",
+       "```json\n",
+       "{\n",
+       "  \"document\": {\n",
+       "    \"title\": \"Oral History Interview with John Hope Franklin\",\n",
+       "    \"language\": \"English\",\n",
+       "    \"content\": \"JOHN EGERTON: What I'd like to do, I'd like to, I know, I know your historical personal background \n",
+       "about your parents meeting at Walden and you know we've talked about that at Roger Williams, we've talked about \n",
+       "that before and about how you got to Nashville from Oklahoma and all that, but I want to kind of pick up about the \n",
+       "time when you were an undergraduate at Fisk in the 30s and ask you first a couple of things. One, do you recall any\n",
+       "meetings, interracial meetings that took place on the Vanderbilt campus during those years? JOHN HOPE FRANKLIN: No.\n",
+       "JOHN EGERTON: Never happened? JOHN HOPE FRANKLIN: No, never happened so far as I know. JOHN EGERTON: At Fisk, yes, \n",
+       "but at Vanderbilt, no. JOHN HOPE FRANKLIN: That's right. JOHN EGERTON: The people from Vanderbilt would come over \n",
+       "there, but not the other way around? JOHN HOPE FRANKLIN: That's right, and I don't know whether you remember the \n",
+       "famous meeting, maybe then I would have to back up and say I know of one, where a number of people, distinguished \n",
+       "sociologists, probably Robert Park and people like that, I'm not certain who they were, they had a meeting out at \n",
+       "Vanderbilt and invited E. Franklin Frazier out there. It might even have been a luncheon, and I think Chancellor \n",
+       "Kirkland learned about it. JOHN EGERTON: This would have been in that period when you were an undergraduate? JOHN \n",
+       "HOPE FRANKLIN: Yes. It would have been because, you see, Frazier left at the end of my junior year. Went to Howard \n",
+       "in 1934. Other incidents that I remember in international and at Vanderbilt was when, in my senior year, the spring\n",
+       "of my senior year, I was an applicant for admission to Harvard to go to graduate school. This is before the GRE's, \n",
+       "you see. So they wanted me to take a Scholastic Aptitude Test, and, of course, it was scheduled, like the GRE's, at\n",
+       "a certain time and place. And it was at Vanderbilt, and it was in a certain room on Vanderbilt campus, and I went \n",
+       "there.\",\n",
+       "    \"summary\": {\n",
+       "      \"en\": \"This excerpt features an interview with John Hope Franklin, who recalls his undergraduate years at \n",
+       "Fisk University in the 1930s. He discusses the lack of interracial meetings on the Vanderbilt campus, highlighting \n",
+       "that while Vanderbilt students would visit Fisk, the reverse did not occur. He recalls a notable exception—a \n",
+       "meeting with distinguished sociologists, including E. Franklin Frazier—during which Chancellor Kirkland of \n",
+       "Vanderbilt became aware of the event. Franklin also mentions his application to Harvard for graduate school, \n",
+       "highlighting the requirement to take the Scholastic Aptitude Test at Vanderbilt before the era of the GRE.\",\n",
+       "      \"de\": \"Dieser Auszug enthält ein Interview mit John Hope Franklin, der sich an seine Studienjahre an der Fisk\n",
+       "University in den 1930er Jahren erinnert. Er spricht über das Fehlen von interrassischen Treffen auf dem \n",
+       "Vanderbilt-Campus und betont, dass während Vanderbilt-Studenten Fisk besuchten, der umgekehrte Fall nicht \n",
+       "stattfand. Er erinnert sich an eine bemerkenswerte Ausnahme – ein Treffen mit angesehenen Soziologen, darunter E. \n",
+       "Franklin Frazier –, bei dem Kanzler Kirkland von Vanderbilt von dem Ereignis erfuhr. Franklin erwähnt auch seine \n",
+       "Bewerbung für die Graduiertenschule von Harvard und hebt hervor, dass er den Scholastic Aptitude Test in Vanderbilt\n",
+       "absolvieren musste, bevor die Zeit der GRE kam.\"\n",
+       "    },\n",
+       "    \"sentiment_analysis\": {\n",
+       "      \"overall_sentiment\": \"reflective\",\n",
+       "      \"justification\": \"The interview has a reflective tone as Franklin reminisces about his college years and \n",
+       "highlights racial boundaries and academic challenges of the time. The lack of significant emotional swings or \n",
+       "expressions indicates a neutral recollection with respect to the events mentioned.\"\n",
+       "    },\n",
+       "    \"named_entities\": {\n",
+       "      \"places\": {\n",
+       "        \"en\": [\"Fisk University\", \"Vanderbilt University\", \"Howard University\", \"Harvard University\", \"Walden\", \n",
+       "\"Roger Williams\", \"Nashville\", \"Oklahoma\"],\n",
+       "        \"de\": [\"Fisk Universität\", \"Vanderbilt Universität\", \"Howard Universität\", \"Harvard Universität\", \"Walden\",\n",
+       "\"Roger Williams\", \"Nashville\", \"Oklahoma\"]\n",
+       "      },\n",
+       "      \"people\": {\n",
+       "        \"en\": [\"John Hope Franklin\", \"John Egerton\", \"E. Franklin Frazier\", \"Robert Park\", \"Chancellor Kirkland\"],\n",
+       "        \"de\": [\"John Hope Franklin\", \"John Egerton\", \"E. Franklin Frazier\", \"Robert Park\", \"Kanzler Kirkland\"]\n",
+       "      },\n",
+       "      \"events\": {\n",
+       "        \"en\": [\"Sociology Meeting at Vanderbilt\", \"John Hope Franklin's application to Harvard\"],\n",
+       "        \"de\": [\"Soziologietreffen an der Vanderbilt\", \"Bewerbung von John Hope Franklin an Harvard\"]\n",
+       "      },\n",
+       "      \"organizations\": {\n",
+       "        \"en\": [\"Fisk University\", \"Vanderbilt University\", \"Howard University\", \"Harvard University\"],\n",
+       "        \"de\": [\"Fisk Universität\", \"Vanderbilt Universität\", \"Howard Universität\", \"Harvard Universität\"]\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "```\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mMetadata Extraction for Oral History Transcript\u001b[0m\n", + "\n", + "```json\n", + "\u001b[1m{\u001b[0m\n", + " \u001b[32m\"document\"\u001b[0m: \u001b[1m{\u001b[0m\n", + " \u001b[32m\"title\"\u001b[0m: \u001b[32m\"Oral History Interview with John Hope Franklin\"\u001b[0m,\n", + " \u001b[32m\"language\"\u001b[0m: \u001b[32m\"English\"\u001b[0m,\n", + " \u001b[32m\"content\"\u001b[0m: \u001b[32m\"JOHN EGERTON: What I'd like to do, I'd like to, I know, I know your historical personal background \u001b[0m\n", + "\u001b[32mabout your parents meeting at Walden and you know we've talked about that at Roger Williams, we've talked about \u001b[0m\n", + "\u001b[32mthat before and about how you got to Nashville from Oklahoma and all that, but I want to kind of pick up about the \u001b[0m\n", + "\u001b[32mtime when you were an undergraduate at Fisk in the 30s and ask you first a couple of things. One, do you recall any\u001b[0m\n", + "\u001b[32mmeetings, interracial meetings that took place on the Vanderbilt campus during those years? JOHN HOPE FRANKLIN: No.\u001b[0m\n", + "\u001b[32mJOHN EGERTON: Never happened? JOHN HOPE FRANKLIN: No, never happened so far as I know. JOHN EGERTON: At Fisk, yes, \u001b[0m\n", + "\u001b[32mbut at Vanderbilt, no. JOHN HOPE FRANKLIN: That's right. JOHN EGERTON: The people from Vanderbilt would come over \u001b[0m\n", + "\u001b[32mthere, but not the other way around? JOHN HOPE FRANKLIN: That's right, and I don't know whether you remember the \u001b[0m\n", + "\u001b[32mfamous meeting, maybe then I would have to back up and say I know of one, where a number of people, distinguished \u001b[0m\n", + "\u001b[32msociologists, probably Robert Park and people like that, I'm not certain who they were, they had a meeting out at \u001b[0m\n", + "\u001b[32mVanderbilt and invited E. Franklin Frazier out there. It might even have been a luncheon, and I think Chancellor \u001b[0m\n", + "\u001b[32mKirkland learned about it. JOHN EGERTON: This would have been in that period when you were an undergraduate? JOHN \u001b[0m\n", + "\u001b[32mHOPE FRANKLIN: Yes. It would have been because, you see, Frazier left at the end of my junior year. Went to Howard \u001b[0m\n", + "\u001b[32min 1934. Other incidents that I remember in international and at Vanderbilt was when, in my senior year, the spring\u001b[0m\n", + "\u001b[32mof my senior year, I was an applicant for admission to Harvard to go to graduate school. This is before the GRE's, \u001b[0m\n", + "\u001b[32myou see. So they wanted me to take a Scholastic Aptitude Test, and, of course, it was scheduled, like the GRE's, at\u001b[0m\n", + "\u001b[32ma certain time and place. And it was at Vanderbilt, and it was in a certain room on Vanderbilt campus, and I went \u001b[0m\n", + "\u001b[32mthere.\"\u001b[0m,\n", + " \u001b[32m\"summary\"\u001b[0m: \u001b[1m{\u001b[0m\n", + " \u001b[32m\"en\"\u001b[0m: \u001b[32m\"This excerpt features an interview with John Hope Franklin, who recalls his undergraduate years at \u001b[0m\n", + "\u001b[32mFisk University in the 1930s. He discusses the lack of interracial meetings on the Vanderbilt campus, highlighting \u001b[0m\n", + "\u001b[32mthat while Vanderbilt students would visit Fisk, the reverse did not occur. He recalls a notable exception—a \u001b[0m\n", + "\u001b[32mmeeting with distinguished sociologists, including E. Franklin Frazier—during which Chancellor Kirkland of \u001b[0m\n", + "\u001b[32mVanderbilt became aware of the event. Franklin also mentions his application to Harvard for graduate school, \u001b[0m\n", + "\u001b[32mhighlighting the requirement to take the Scholastic Aptitude Test at Vanderbilt before the era of the GRE.\"\u001b[0m,\n", + " \u001b[32m\"de\"\u001b[0m: \u001b[32m\"Dieser Auszug enthält ein Interview mit John Hope Franklin, der sich an seine Studienjahre an der Fisk\u001b[0m\n", + "\u001b[32mUniversity in den 1930er Jahren erinnert. Er spricht über das Fehlen von interrassischen Treffen auf dem \u001b[0m\n", + "\u001b[32mVanderbilt-Campus und betont, dass während Vanderbilt-Studenten Fisk besuchten, der umgekehrte Fall nicht \u001b[0m\n", + "\u001b[32mstattfand. Er erinnert sich an eine bemerkenswerte Ausnahme – ein Treffen mit angesehenen Soziologen, darunter E. \u001b[0m\n", + "\u001b[32mFranklin Frazier –, bei dem Kanzler Kirkland von Vanderbilt von dem Ereignis erfuhr. Franklin erwähnt auch seine \u001b[0m\n", + "\u001b[32mBewerbung für die Graduiertenschule von Harvard und hebt hervor, dass er den Scholastic Aptitude Test in Vanderbilt\u001b[0m\n", + "\u001b[32mabsolvieren musste, bevor die Zeit der GRE kam.\"\u001b[0m\n", + " \u001b[1m}\u001b[0m,\n", + " \u001b[32m\"sentiment_analysis\"\u001b[0m: \u001b[1m{\u001b[0m\n", + " \u001b[32m\"overall_sentiment\"\u001b[0m: \u001b[32m\"reflective\"\u001b[0m,\n", + " \u001b[32m\"justification\"\u001b[0m: \u001b[32m\"The interview has a reflective tone as Franklin reminisces about his college years and \u001b[0m\n", + "\u001b[32mhighlights racial boundaries and academic challenges of the time. The lack of significant emotional swings or \u001b[0m\n", + "\u001b[32mexpressions indicates a neutral recollection with respect to the events mentioned.\"\u001b[0m\n", + " \u001b[1m}\u001b[0m,\n", + " \u001b[32m\"named_entities\"\u001b[0m: \u001b[1m{\u001b[0m\n", + " \u001b[32m\"places\"\u001b[0m: \u001b[1m{\u001b[0m\n", + " \u001b[32m\"en\"\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m\"Fisk University\"\u001b[0m, \u001b[32m\"Vanderbilt University\"\u001b[0m, \u001b[32m\"Howard University\"\u001b[0m, \u001b[32m\"Harvard University\"\u001b[0m, \u001b[32m\"Walden\"\u001b[0m, \n", + "\u001b[32m\"Roger Williams\"\u001b[0m, \u001b[32m\"Nashville\"\u001b[0m, \u001b[32m\"Oklahoma\"\u001b[0m\u001b[1m]\u001b[0m,\n", + " \u001b[32m\"de\"\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m\"Fisk Universität\"\u001b[0m, \u001b[32m\"Vanderbilt Universität\"\u001b[0m, \u001b[32m\"Howard Universität\"\u001b[0m, \u001b[32m\"Harvard Universität\"\u001b[0m, \u001b[32m\"Walden\"\u001b[0m,\n", + "\u001b[32m\"Roger Williams\"\u001b[0m, \u001b[32m\"Nashville\"\u001b[0m, \u001b[32m\"Oklahoma\"\u001b[0m\u001b[1m]\u001b[0m\n", + " \u001b[1m}\u001b[0m,\n", + " \u001b[32m\"people\"\u001b[0m: \u001b[1m{\u001b[0m\n", + " \u001b[32m\"en\"\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m\"John Hope Franklin\"\u001b[0m, \u001b[32m\"John Egerton\"\u001b[0m, \u001b[32m\"E. Franklin Frazier\"\u001b[0m, \u001b[32m\"Robert Park\"\u001b[0m, \u001b[32m\"Chancellor Kirkland\"\u001b[0m\u001b[1m]\u001b[0m,\n", + " \u001b[32m\"de\"\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m\"John Hope Franklin\"\u001b[0m, \u001b[32m\"John Egerton\"\u001b[0m, \u001b[32m\"E. Franklin Frazier\"\u001b[0m, \u001b[32m\"Robert Park\"\u001b[0m, \u001b[32m\"Kanzler Kirkland\"\u001b[0m\u001b[1m]\u001b[0m\n", + " \u001b[1m}\u001b[0m,\n", + " \u001b[32m\"events\"\u001b[0m: \u001b[1m{\u001b[0m\n", + " \u001b[32m\"en\"\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m\"Sociology Meeting at Vanderbilt\"\u001b[0m, \u001b[32m\"John Hope Franklin's application to Harvard\"\u001b[0m\u001b[1m]\u001b[0m,\n", + " \u001b[32m\"de\"\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m\"Soziologietreffen an der Vanderbilt\"\u001b[0m, \u001b[32m\"Bewerbung von John Hope Franklin an Harvard\"\u001b[0m\u001b[1m]\u001b[0m\n", + " \u001b[1m}\u001b[0m,\n", + " \u001b[32m\"organizations\"\u001b[0m: \u001b[1m{\u001b[0m\n", + " \u001b[32m\"en\"\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m\"Fisk University\"\u001b[0m, \u001b[32m\"Vanderbilt University\"\u001b[0m, \u001b[32m\"Howard University\"\u001b[0m, \u001b[32m\"Harvard University\"\u001b[0m\u001b[1m]\u001b[0m,\n", + " \u001b[32m\"de\"\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m\"Fisk Universität\"\u001b[0m, \u001b[32m\"Vanderbilt Universität\"\u001b[0m, \u001b[32m\"Howard Universität\"\u001b[0m, \u001b[32m\"Harvard Universität\"\u001b[0m\u001b[1m]\u001b[0m\n", + " \u001b[1m}\u001b[0m\n", + " \u001b[1m}\u001b[0m\n", + " \u001b[1m}\u001b[0m\n", + "\u001b[1m}\u001b[0m\n", + "```\n" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/html": [ + "
Metadata Extraction for OCR Correction\n",
+       "\n",
+       "```json\n",
+       "{\n",
+       "  \"document\": {\n",
+       "    \"title\": \"Nur ein\",\n",
+       "    \"language\": \"German\",\n",
+       "    \"content\": \"Unverloeschbar tief haben sich uns die Bilder des Grauens einge- praegt, die jeder von uns dieser \n",
+       "Tage in dem ersten amerikanischen Armeefilm aus Deutschland sah...\",\n",
+       "    \"summary\": {\n",
+       "      \"en\": \"The text reflects on the deep impact of viewing a harrowing American army film depicting scenes from \n",
+       "Germany, specifically concentration camps during WWII. It conveys a sense of shock and horror at the inhumanity \n",
+       "perpetuated by the Nazis, comparing the dreadful reality witnessed to scenes from Dante's 'Divine Comedy' and \n",
+       "suggesting that the atrocities in the camps surpass even those depicted in literature. The film serves as a grim \n",
+       "reminder of the tragic and numerous losses since 1933, evoking a moral responsibility to not remain silent.\",\n",
+       "      \"de\": \"Der Text beschreibt den tiefen Eindruck, den das Ansehen eines erschütternden amerikanischen \n",
+       "Armeefilms ausgelöst hat, der Szenen aus Deutschland, insbesondere aus Konzentrationslagern während des Zweiten \n",
+       "Weltkriegs, zeigt. Er vermittelt ein Gefühl von Schock und Entsetzen über die von den Nazis begangene \n",
+       "Unmenschlichkeit und vergleicht die schreckliche Realität, die gezeigt wurde, mit Szenen aus Dantes 'Göttlicher \n",
+       "Komödie', wobei darauf hingewiesen wird, dass die Gräueltaten in den Lagern die in der Literatur dargestellten \n",
+       "Grauen sogar übertreffen. Der Film dient als düstere Erinnerung an die tragischen und vielfältigen Verluste seit \n",
+       "1933 und ruft eine moralische Verantwortung hervor, nicht zu schweigen.\"\n",
+       "    },\n",
+       "    \"sentiment_analysis\": {\n",
+       "      \"overall_sentiment\": \"deeply negative\",\n",
+       "      \"justification\": \"The text is permeated with sentiments of horror, shock, and a profound condemnation of the \n",
+       "atrocities committed by the Nazis. The comparison to Dante's depiction of hell underscores a severe emotional \n",
+       "response to the witnessed inhumanity.\"\n",
+       "    },\n",
+       "    \"named_entities\": {\n",
+       "      \"places\": {\n",
+       "        \"de\": [\"Deutschland\", \"Europa\"],\n",
+       "        \"en\": [\"Germany\", \"Europe\"]\n",
+       "      },\n",
+       "      \"people\": {\n",
+       "        \"de\": [\"Dante\"],\n",
+       "        \"en\": [\"Dante\"]\n",
+       "      },\n",
+       "      \"events\": {\n",
+       "        \"de\": [\"Konzentrationslager\", \"Zweiter Weltkrieg\"],\n",
+       "        \"en\": [\"Concentration Camps\", \"World War II\"]\n",
+       "      },\n",
+       "      \"organizations\": {\n",
+       "        \"de\": [\"amerikanische Armee\"],\n",
+       "        \"en\": [\"American Army\"]\n",
+       "      }\n",
+       "    },\n",
+       "    \"translation_context\": {\n",
+       "      \"specialized_terms\": {\n",
+       "        \"de\": {\n",
+       "          \"K.Z.-Lager\": \"Konzentrationslager\"\n",
+       "        },\n",
+       "        \"en\": {\n",
+       "          \"K.Z.-Lager\": \"Concentration Camp\"\n",
+       "        },\n",
+       "        \"historical_context\": \"The term 'K.Z.' is an abbreviation for 'Konzentrationslager,' commonly used during \n",
+       "WWII to refer to concentration camps run by Nazi Germany.\"\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "}\n",
+       "```\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mMetadata Extraction for OCR Correction\u001b[0m\n", + "\n", + "```json\n", + "\u001b[1m{\u001b[0m\n", + " \u001b[32m\"document\"\u001b[0m: \u001b[1m{\u001b[0m\n", + " \u001b[32m\"title\"\u001b[0m: \u001b[32m\"Nur ein\"\u001b[0m,\n", + " \u001b[32m\"language\"\u001b[0m: \u001b[32m\"German\"\u001b[0m,\n", + " \u001b[32m\"content\"\u001b[0m: \u001b[32m\"Unverloeschbar tief haben sich uns die Bilder des Grauens einge- praegt, die jeder von uns dieser \u001b[0m\n", + "\u001b[32mTage in dem ersten amerikanischen Armeefilm aus Deutschland sah...\"\u001b[0m,\n", + " \u001b[32m\"summary\"\u001b[0m: \u001b[1m{\u001b[0m\n", + " \u001b[32m\"en\"\u001b[0m: \u001b[32m\"The text reflects on the deep impact of viewing a harrowing American army film depicting scenes from \u001b[0m\n", + "\u001b[32mGermany, specifically concentration camps during WWII. It conveys a sense of shock and horror at the inhumanity \u001b[0m\n", + "\u001b[32mperpetuated by the Nazis, comparing the dreadful reality witnessed to scenes from Dante's 'Divine Comedy' and \u001b[0m\n", + "\u001b[32msuggesting that the atrocities in the camps surpass even those depicted in literature. The film serves as a grim \u001b[0m\n", + "\u001b[32mreminder of the tragic and numerous losses since 1933, evoking a moral responsibility to not remain silent.\"\u001b[0m,\n", + " \u001b[32m\"de\"\u001b[0m: \u001b[32m\"Der Text beschreibt den tiefen Eindruck, den das Ansehen eines erschütternden amerikanischen \u001b[0m\n", + "\u001b[32mArmeefilms ausgelöst hat, der Szenen aus Deutschland, insbesondere aus Konzentrationslagern während des Zweiten \u001b[0m\n", + "\u001b[32mWeltkriegs, zeigt. Er vermittelt ein Gefühl von Schock und Entsetzen über die von den Nazis begangene \u001b[0m\n", + "\u001b[32mUnmenschlichkeit und vergleicht die schreckliche Realität, die gezeigt wurde, mit Szenen aus Dantes 'Göttlicher \u001b[0m\n", + "\u001b[32mKomödie', wobei darauf hingewiesen wird, dass die Gräueltaten in den Lagern die in der Literatur dargestellten \u001b[0m\n", + "\u001b[32mGrauen sogar übertreffen. Der Film dient als düstere Erinnerung an die tragischen und vielfältigen Verluste seit \u001b[0m\n", + "\u001b[32m1933 und ruft eine moralische Verantwortung hervor, nicht zu schweigen.\"\u001b[0m\n", + " \u001b[1m}\u001b[0m,\n", + " \u001b[32m\"sentiment_analysis\"\u001b[0m: \u001b[1m{\u001b[0m\n", + " \u001b[32m\"overall_sentiment\"\u001b[0m: \u001b[32m\"deeply negative\"\u001b[0m,\n", + " \u001b[32m\"justification\"\u001b[0m: \u001b[32m\"The text is permeated with sentiments of horror, shock, and a profound condemnation of the \u001b[0m\n", + "\u001b[32matrocities committed by the Nazis. The comparison to Dante's depiction of hell underscores a severe emotional \u001b[0m\n", + "\u001b[32mresponse to the witnessed inhumanity.\"\u001b[0m\n", + " \u001b[1m}\u001b[0m,\n", + " \u001b[32m\"named_entities\"\u001b[0m: \u001b[1m{\u001b[0m\n", + " \u001b[32m\"places\"\u001b[0m: \u001b[1m{\u001b[0m\n", + " \u001b[32m\"de\"\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m\"Deutschland\"\u001b[0m, \u001b[32m\"Europa\"\u001b[0m\u001b[1m]\u001b[0m,\n", + " \u001b[32m\"en\"\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m\"Germany\"\u001b[0m, \u001b[32m\"Europe\"\u001b[0m\u001b[1m]\u001b[0m\n", + " \u001b[1m}\u001b[0m,\n", + " \u001b[32m\"people\"\u001b[0m: \u001b[1m{\u001b[0m\n", + " \u001b[32m\"de\"\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m\"Dante\"\u001b[0m\u001b[1m]\u001b[0m,\n", + " \u001b[32m\"en\"\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m\"Dante\"\u001b[0m\u001b[1m]\u001b[0m\n", + " \u001b[1m}\u001b[0m,\n", + " \u001b[32m\"events\"\u001b[0m: \u001b[1m{\u001b[0m\n", + " \u001b[32m\"de\"\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m\"Konzentrationslager\"\u001b[0m, \u001b[32m\"Zweiter Weltkrieg\"\u001b[0m\u001b[1m]\u001b[0m,\n", + " \u001b[32m\"en\"\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m\"Concentration Camps\"\u001b[0m, \u001b[32m\"World War II\"\u001b[0m\u001b[1m]\u001b[0m\n", + " \u001b[1m}\u001b[0m,\n", + " \u001b[32m\"organizations\"\u001b[0m: \u001b[1m{\u001b[0m\n", + " \u001b[32m\"de\"\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m\"amerikanische Armee\"\u001b[0m\u001b[1m]\u001b[0m,\n", + " \u001b[32m\"en\"\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m\"American Army\"\u001b[0m\u001b[1m]\u001b[0m\n", + " \u001b[1m}\u001b[0m\n", + " \u001b[1m}\u001b[0m,\n", + " \u001b[32m\"translation_context\"\u001b[0m: \u001b[1m{\u001b[0m\n", + " \u001b[32m\"specialized_terms\"\u001b[0m: \u001b[1m{\u001b[0m\n", + " \u001b[32m\"de\"\u001b[0m: \u001b[1m{\u001b[0m\n", + " \u001b[32m\"K.Z.-Lager\"\u001b[0m: \u001b[32m\"Konzentrationslager\"\u001b[0m\n", + " \u001b[1m}\u001b[0m,\n", + " \u001b[32m\"en\"\u001b[0m: \u001b[1m{\u001b[0m\n", + " \u001b[32m\"K.Z.-Lager\"\u001b[0m: \u001b[32m\"Concentration Camp\"\u001b[0m\n", + " \u001b[1m}\u001b[0m,\n", + " \u001b[32m\"historical_context\"\u001b[0m: \u001b[32m\"The term 'K.Z.' is an abbreviation for 'Konzentrationslager,' commonly used during \u001b[0m\n", + "\u001b[32mWWII to refer to concentration camps run by Nazi Germany.\"\u001b[0m\n", + " \u001b[1m}\u001b[0m\n", + " \u001b[1m}\u001b[0m\n", + " \u001b[1m}\u001b[0m\n", + "\u001b[1m}\u001b[0m\n", + "```\n" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import requests\n", + "from openai import OpenAI\n", + "from rich.console import Console\n", + "\n", + "# Initialize the console for rich output\n", + "console = Console()\n", + "\n", + "# Function to fetch text content from a URL\n", + "def fetch_text_from_url(url):\n", + " response = requests.get(url)\n", + " response.raise_for_status() # Ensure the request was successful\n", + " return response.text.strip()\n", + "\n", + "# URL for the metadata extraction prompt\n", + "file_urls = {\n", + " \"metadata_extraction_prompt\": \"https://raw.githubusercontent.com/Dr-Hutchinson/jdh_submission/refs/heads/main/media/prompts/metadata_extraction_prompt.txt\"\n", + "}\n", + "\n", + "# Load the metadata extraction prompt from the URL\n", + "metadata_extraction_prompt = fetch_text_from_url(file_urls[\"metadata_extraction_prompt\"])\n", + "\n", + "# Initialize the OpenAI client\n", + "client = OpenAI()\n", + "\n", + "# Function to query the LLM for metadata extraction with rich-formatted output\n", + "def extract_metadata(source_text, source_name):\n", + " query = client.chat.completions.create(\n", + " model=\"gpt-4o\",\n", + " messages=[\n", + " {\"role\": \"user\", \"content\": metadata_extraction_prompt + \"\\n\" + source_text}\n", + " ]\n", + " )\n", + " output = query.choices[0].message.content\n", + " \n", + " # Format the metadata output for display\n", + " output_text = (\n", + " f\"[bold]Metadata Extraction for {source_name}[/bold]\\n\\n\"\n", + " f\"{output}\"\n", + " )\n", + " console.print(output_text)\n", + "\n", + "# Whisper transcript and OCR corrected output from earlier code\n", + "sources = [\n", + " {\"text\": whisper_transcript, \"name\": \"Oral History Transcript\"},\n", + " {\"text\": gpt4_corrected_output_1, \"name\": \"OCR Correction\"}\n", + "]\n", + "\n", + "# Run the extraction for each source\n", + "for source in sources:\n", + " extract_metadata(source[\"text\"], source[\"name\"])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "citation-manager": { + "citations": { + "9k5i6": [ + { + "id": "27937/CDKJEW4Z", + "source": "zotero" + } + ], + "nkv58": [ + { + "id": "27937/Z44J4BKC", + "source": "zotero" + } + ], + "v3avs": [ + { + "id": "27937/5ED45HQE", + "source": "zotero" + } + ], + "wv7ar": [ + { + "id": "27937/LC63DETW", + "source": "zotero" + } + ] + } + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "While neither the Whisper transcript nor the OCR correction were error-free, these errors did not prevent the LLM from successfully extracting relevant data in a structured JSON format. Further refinement of the prompt and incorporating more task-specific examples could improve the consistency and fidelity of such outputs. Additionally, fine-tuning LLM models for particular NER tasks and specific datasets can reduce misclassifications and the extraction of irrelevant entities. Researchers in various fields are already applying these techniques to extract data from multilingual historic text collections (González-Gallardo et al., “Leveraging Open Large Language Models for Historical Named Entity Recognition.”), as well in other fields for sources as varied as scientific papers (Dagdelen et al., “Structured Information Extraction from Scientific Text with Large Language Models.”), and electronic health records. (Hu et al., “Improving Large Language Models for Clinical Named Entity Recognition via Prompt Engineering.”)\n", + "\n", + "The implications of using LLMs for data extraction are significant. As Lauren Tilton argues: “As artificial intelligence generates data about data, guided by schemas, ontologies, and ways of seeing built into algorithms that will guide our search and aggregation, attention to the metadata and rethinking how artificial intelligence generates it will likely become a key part of historical research.\" (Tilton, “Relating to Historical Sources.”) By streamlining tasks such as transcription, text correction, and metadata generation, LLMs can significantly reduce the workload of creating “tidy datasets”, enabling historians to focus on interpreting data instead of cleaning it. To be sure, this approach risks encoding hallucinations into the foundations of a data collection, and as emphasized in the previous case studies, human review remains essential. But with proper refinement, LLM-driven metadata extraction can prove a useful method for engaging with archival collections in new ways. Indeed, scholars are increasingly leveraging such data within larger computational systems to power new forms of scholarship using generative AI." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## Case Study: Retrieval Augmented Generation (RAG) and Exploring Historic Text Collections" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "citation-manager": { + "citations": { + "cwwxs": [ + { + "id": "27937/P2KVKTMZ", + "source": "zotero" + } + ], + "j022d": [ + { + "id": "27937/7D6BEHLB", + "source": "zotero" + } + ], + "k7j4i": [ + { + "id": "27937/ECQ4J8E9", + "source": "zotero" + } + ], + "usvx1": [ + { + "id": "27937/HGR9QB96", + "source": "zotero" + } + ] + } + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "While LLMs offer a broad range of capabilities for data cleanup and extraction, their tendency to hallucinate remains a significant challenge for its use in historical research and analysis. However, recent advances in pairing generative AI models with other computational tools have helped ground LLM responses in greater factual accuracy. These techniques also enable LLMs to consult large text collections, search the Internet, and utilize external tools to solve problems in unfamiliar knowledge domains.\n", + "\n", + "Retrieval augmented generation (RAG) has emerged as a leading approach in this space. (Lewis et al., “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.”) In this method a data collection is indexed and prepared for a retriever, which functions as a specialized search engine. When a user query submits a query, the retriever searches the data for the most relevant matches. While RAG retrievers typically feature keyword matching techniques found in search engines, they also use machine learning tools like text embeddings to provide results that are both semantically and contextually similar to the query. \n", + "(Blankenship, Connell, and Dombrowski, “Understanding and Creating Word Embeddings.”) The highest scoring results are then passed to the LLM, which then generates a response grounded in the search results instead of hallucinated speculation. \n", + "\n", + "This approach is particularly useful for knowledge areas outside a LLM’s training data, like archival sources. An example of a RAG system in action is *Nicolay: Exploring the Speeches of Abraham Lincoln with AI* (Hutchinson, “Nicolay: Exploring the Speeches of Abraham Lincoln with AI.”), a digital history project that uses RAG to analyze a collection of Lincoln speeches from the University of Virginia’s Miller Center. (“Presidential Speeches | Miller Center.”) While LLMs are well-versed in Lincoln’s life and writings, even the best generative AI models fall short on more narrow queries. For example, here is GPT’4’s response to the query: “What did Abraham Lincoln think of Japan?”" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
GPT-4's Response:\n",
+       "\n",
+       "There is no specific historical record that details Abraham Lincoln's personal views on Japan. During Lincoln's \n",
+       "presidency from 1861 to 1865, the United States was not heavily involved with Japan, as the country was just \n",
+       "beginning to open up to the West following Commodore Matthew Perry's expeditions in the 1850s. The broader \n",
+       "U.S.-Japan relationship was still in its early stages during this period and was not a major focus of Lincoln's \n",
+       "presidency, which was primarily concerned with the Civil War and domestic issues. Thus, any thoughts Lincoln might \n",
+       "have had about Japan were not prominently documented or discussed.\n",
+       "\n",
+       "End of GPT-4's Response\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1;36mGPT-\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;36m's Response:\u001b[0m\n", + "\n", + "There is no specific historical record that details Abraham Lincoln's personal views on Japan. During Lincoln's \n", + "presidency from \u001b[1;36m1861\u001b[0m to \u001b[1;36m1865\u001b[0m, the United States was not heavily involved with Japan, as the country was just \n", + "beginning to open up to the West following Commodore Matthew Perry's expeditions in the 1850s. The broader \n", + "U.S.-Japan relationship was still in its early stages during this period and was not a major focus of Lincoln's \n", + "presidency, which was primarily concerned with the Civil War and domestic issues. Thus, any thoughts Lincoln might \n", + "have had about Japan were not prominently documented or discussed.\n", + "\n", + "\u001b[1;36mEnd of GPT-\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;36m's Response\u001b[0m\n" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from openai import OpenAI\n", + "from rich.console import Console\n", + "\n", + "# Initialize the console for rich output\n", + "console = Console()\n", + "\n", + "# Initialize the OpenAI client\n", + "client = OpenAI()\n", + "\n", + "# Create the query for the LLM\n", + "query = client.chat.completions.create(\n", + " model=\"gpt-4o\",\n", + " messages=[\n", + " {\"role\": \"user\", \"content\": \"What did Abraham Lincoln think of Japan?\"}\n", + " ]\n", + ")\n", + "\n", + "# Extract the output from the response\n", + "output = query.choices[0].message.content\n", + "\n", + "# Format the output for display with rich\n", + "output_text = (\n", + " \"[bold cyan]GPT-4's Response:[/bold cyan]\\n\\n\"\n", + " f\"{output}\\n\\n\"\n", + " \"[bold cyan]End of GPT-4's Response[/bold cyan]\"\n", + ")\n", + "\n", + "# Display the formatted output\n", + "console.print(output_text, width=console.size.width)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "Here GPT-4 responds with general historical context to the question, which is valuable, but offers no specific insight into Lincoln’s own views, largely due to the lack of direct evidence in its training data. A LLM informed by RAG offers a very different response. Below is an output from *Nicolay*, which you can replicate via [this site.](https://nicolay-honestabes-info.streamlit.app/)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "**Begin Nicolay’s Response**\n", + "\n", + ">Abraham Lincoln regarded Japan as an important partner in international relations, as evidenced by his references in annual messages to Congress. In his Second Annual Message, he noted, ‘Very favorable relations also continue to be maintained with Turkey, Morocco, China and Japan.’ (Second Annual Message, December 1, 1862) This statement reflects a period of stable and positive relations between the U.S. and Japan. However, Lincoln was also aware of the challenges in these relations. In his Third Annual Message, he expressed concern about the internal political struggles in Japan, stating, ‘In common with other western powers, our relations with Japan have been brought into serious jeopardy, through the perverse opposition of the hereditary aristocracy of the empire, to the enlightened and liberal policy of the Tycoon designed to bring the country into the society of nations.’ (Third Annual Message, December 8, 1863) This indicates his understanding of the impact of Japan’s internal politics on bilateral relations. Furthermore, in his Fourth Annual Message, Lincoln acknowledged the complexities of Japan’s political structure and the progress in treaty compliance, noting, ‘Owing to the peculiar situation of Japan, and the anomalous form of its government, the action of that empire in performing treaty stipulations is inconstant and capricious. Nevertheless, good progress has been effected by the western powers, moving with enlightened concert.’ (Fourth Annual Message, December 6, 1864) These statements collectively demonstrate Lincoln’s recognition of Japan’s significance in U.S. foreign policy and his efforts to navigate the complexities of this relationship during his presidency.\n", + ">\n", + ">References:\n", + ">\n", + ">Third Annual Message, December 8, 1863\n", + ">\n", + ">Fourth Annual Message, December 6, 1864\n", + ">\n", + ">Second Annual Message, December 1, 1862\n", + "\n", + "**End Nicolay’s Response**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "citation-manager": { + "citations": { + "7ukai": [ + { + "id": "27937/38C5LZCI", + "source": "zotero" + } + ], + "fm8ii": [ + { + "id": "27937/GP3PUHUJ", + "source": "zotero" + } + ], + "hq9so": [ + { + "id": "27937/MYBFNHF8", + "source": "zotero" + } + ], + "q3vrr": [ + { + "id": "27937/RJTNQXZP", + "source": "zotero" + } + ] + } + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "In less than a minute Nicolay searched through a corpus of over three hundred pages of text, identified the most pertinent sections, and returned an informed response containing direct quotes and accurate citations. This highlights how RAG systems can ground AI responses in factual detail while offering a chain of evidence and LLM ‘reasoning’ that historians can verify. RAG thus offers scholars a new way to directly query historical source collections, allowing for dialogue with historical data via a LLM intermediary.\n", + "\n", + "Despite these advancements, RAG mitigates but does not eliminate the problem of hallucinations and errors. While increasingly sophisticated approaches are enhancing AI accuracy, LLMs can still misinterpret results in unexpected ways. Early RAG frameworks powering Google’s “AI Overviews” tool, for instance, famously returned nonsensical and sometimes dangerous responses to user queries, such as affirming the nutritional value of rocks and advising the use of glue to adhere cheese to pizzas. (McMahon and Kleinman, “Google AI Search Tells Users to Glue Pizza and Eat Rocks.”) RAG systems can serve as useful research tools, but the responses they generate should be treated as starting points for deeper analysis, not as final conclusions.\n", + "\n", + "Scholars are already experimenting with the potential of these systems. Developers from the Library Innovation Lab at Harvard Law School Library have released a framework for enabling RAG search over web archive collections, enabling chatbots to return relevant data based on user queries. (Cargnelutti, Mukk, and Stanton, “WARC-GPT.”) Among the most impressive RAG applications developed to date is [STORM](https://storm.genie.stanford.edu/) from Stanford University’s Virtual Open Assistant Lab. This advanced RAG application utilizes a complex framework of LLM “experts” to search the web and construct Wikipedia-style articles on demand. (Shao et al., “Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models.”) While Nicolay produced a paragraph-length response to the Lincoln query above based on a limited corpus, STORM produced a [multi-page overview](https://github.com/Dr-Hutchinson/jdh_submission/blob/main/media/STORM%20Essay%20-%20Abraham%20Lincoln's%20views%20on%20Japan..pdf) supported by citations from sources across the Internet (although, notably, not from Lincoln’s speeches).\n", + "\n", + "While such forms of programmatic essay writing are still in its infancy, the increasing capacities of LLMs to aid automated research has significant implications. While these RAG systems have exciting potential, they also raise important questions about the evolving place of these technologies for digital history. As Benjamin Schmidt notes, such AI-powered systems “don’t only give us new haystacks to search in; they also change the types of needles people will find.” (Schmidt, “Representation Learning.”) While the potential of generative AI as a historical research tool appears significant, their existing limits makes clear that human expertise will continue to be central in guiding and contextualizing these new technologies. Such expertise will be increasingly important, especially as generative AI leads to novel forms of historical exploration and representation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "# The Past as Latent Space: Exploring New Frontiers in Digital History" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "citation-manager": { + "citations": { + "39enb": [ + { + "id": "27937/I363EKXY", + "source": "zotero" + } + ], + "63boa": [ + { + "id": "27937/XBWIZZJJ", + "source": "zotero" + } + ], + "9bzc9": [ + { + "id": "27937/P3ZKA48D", + "source": "zotero" + } + ], + "cczf8": [ + { + "id": "27937/XQYUJV5F", + "source": "zotero" + } + ], + "dqw8n": [ + { + "id": "27937/CXURVMLQ", + "source": "zotero" + } + ], + "g2475": [ + { + "id": "27937/82YIALT5", + "source": "zotero" + } + ], + "gdfmp": [ + { + "id": "27937/VHJBTADE", + "source": "zotero" + } + ], + "l3aq9": [ + { + "id": "27937/JUTZSXVB", + "source": "zotero" + } + ], + "pdy8m": [ + { + "id": "27937/9CA225ZV", + "source": "zotero" + } + ], + "rnhet": [ + { + "id": "27937/KPPP2BAQ", + "source": "zotero" + } + ], + "sbrz8": [ + { + "id": "27937/USYR9HC8", + "source": "zotero" + } + ], + "uqhgl": [ + { + "id": "27937/BN44JR8V", + "source": "zotero" + } + ] + } + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "These case studies demonstrate how LLMs can be prompted to assist digital historians in familiar tasks - transcribing and correcting text, extracting data, even finding connections within archival collections. Yet, it’s often in unexpected domains where LLMs reveal their most intriguing and ethically complex implications. As Ted Underwood argues, these technologies enable us to explore the “latent spaces” of culture - the underlying patterns, assumptions, and potential meanings embedded within the vast datasets that shape our understanding of the past. (Underwood, “Mapping the Latent Spaces of Culture.”) As historians increasingly integrate LLMs into their work, they are discovering that generative AI is not simply streamlining old practices; it is also empowering entirely new forms of historical representation, for both good and ill.\n", + "\n", + "One area where this technology has been felt is in the classroom. For many instructors, their first encounter with generative AI involves suspicion: are student submissions genuine or AI-produced? This concern, compounded by the frustrating unreliability of current LLM-detection tools, has led many to “AI-proof” their assignments and even some to declare that “the essay is dead.” (Marche, “Will ChatGPT Kill the Student Essay? - The Atlantic.”) Collective responses to the impact of this technology are emerging from organizations like the MLA-CCCC’s Working Group on Writing and AI. (MLA-CCCC Joint Task Force on Writing and AI, “Using the Student Guide to AI Literacy – MLA-CCCC Joint Task Force on Writing and AI.”) Historians are joining these efforts in crafting disciplinary responses to generative AI. (Meadows and Sternfeld, “Artificial Intelligence and the Practice of History.”)\n", + "\n", + "Emerging from such discussions are innovative pedagogies that apply and critique generative AI. (Vee, Laquintano, and Schnitzler, “TextGenEd Exhibit.”) One approach shared by Benjamin Breen merits particular attention. Breen has designed prompts to transform LLMs into narrative game engines for interactive historical simulations, akin to “choose-your-own-adventure” games. Students navigate these simulated worlds and converse with AI-controlled characters. Students then annotate their ‘dialogues’ against assigned historical sources, helping contrast the model’s capacities for accuracy against its tendencies towards hallucination and bias. As Breen noted in his first interaction of this assignment, “Student engagement in the spring quarter, when I began these trials, was unlike anything I’ve seen….[the] assignments worked wonders in terms of sparking enthusiasm among previously disengaged students.\" (Breen, “Simulating History with ChatGPT.”) Breen’s creative approach reflects real opportunities for teaching with generative AI in inventive and effective ways.\n", + "\n", + "While creative pedagogies offer exciting possibilities for using and critiquing LLMs in the classroom, the ethical dilemmas posed by these technologies extend far beyond educational contexts, particularly when it comes to representing historical figures. One notable niche among generative AI services are programmable chatbot ‘personalities,’ such as prominent personalities from the past. Following the release of ChatGPT in November 2022, dialogues with AI-impersonations of iconic and controversial figures were widely shared over social media. App developers quickly tapped into what they perceived as an engaging approach to representing the past. However, these apps did little to address the problems of LLM “hallucinations,” nor did they take into account the impact of training biases in LLM “knowledge.” Users soon reported disturbing conversations with both humanity’s greatest luminaries and its greatest villains. (“Chatbot That Lets You Talk to Jesus and Hitler Is Latest AI Controversy.”) The ability of these apps to “bring history to life” soon gave way to an appreciation that perhaps some parts of the past are better off dead.\n", + "\n", + "Such concerns are particularly important as such resurrection can now occur at scale. Researchers are exploring how LLMs can be prompted to emulate human behaviors and serve as proxies for human research subjects in fields as varied as psychology (Cui, Li, and Zhou, “Can AI Replace Human Subjects?”), behavioral economics (Xie et al., “Can Large Language Model Agents Simulate Human Trust Behaviors?”), and public health. (Stade et al., “Large Language Models Could Change the Future of Behavioral Healthcare.”) A compelling study by Stanford researchers in 2023 demonstrates how such emulation could be scaled to simulate entire communities. In their paper “Generative Agents: Interactive Simulacra of Human Behavior,” researchers created a framework to generate a small town populated with residents possessing programmed behaviors and beliefs. LLMs then guided each individual resident as they completed daily tasks and encountered the world around them. As time progressed the inhabitants interacted with each other, reflected on their encounters, and updated their “memories” based on their experiences. Researchers observing the LLM-powered “agents” noted the emergence of spontaneous actions and autonomous planning, leading to unexpected but plausible collective behaviors. (Park et al., “Generative Agents.”) Such paradigms offer potential methods to model historical behaviors at scale and perhaps enable simulation as a new empirical approach. However, historians of earlier forms of computational modeling of human behavior would be quick to remind us of the abuses and errors from previous generations of “artificial intelligence” and warn us of the risks of such approaches. (Lepore, If Then.)\n", + "\n", + "Indeed, as LLMs increasingly become part of our digital lives, historians should move beyond simply using these technologies to using their scholarly expertise to shape the debates concerning their development and implementation. We have a distinctive perspective to share. LLMs are not just tools; they are also historical sources. And like every source they are flawed, anchored in their time and place, and influenced by a particular and often distorted view of the world. But within these limitations lies real potential to apply the vast historical data on which they are trained. Historians are already using LLMs to explore the “latent space” of the past, and offering pointed critiques on how to contextualize the significance of this technology. By approaching LLMs critically, ethically, and collaboratively, digital historians are contributing to Roy Rosenzweig’s vision of navigating the “unheard-of historical abundance” of the digital age in a manner that ensures that these technologies deepen our understanding of the past, and not distort it." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "editable": true, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "I am grateful to Abraham Gibson for extending an invitation to present the preliminary research findings of this article with the Digital History Working Group in May 2022, organized by the Consortium For History of Science, Technology, and Medicine. I would also like to express my appreciation to my colleagues William Mattingly, Patrick Wadden, and Ian Crowe for their insightful commentary on the article, and to the editorial staff and reviewers for the Journal of Digital History. This article was facilitated by a sabbatical semester generously granted by the Office of Academic Affairs at Belmont Abbey College. My thanks to Provost Travis Feezell and Vice Provost David Williams for their support of this project." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "editable": true, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hidden" + ] + }, + "source": [ + "# Bibliography" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "editable": true, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hidden" + ] + }, + "source": [ + "\n", + "
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Prompt engineering plays a key role in adding more to the already existing abilities of LLMs to achieve significant performance gains on various NLP tasks. Prompt engineering requires composing natural language instructions called prompts to elicit knowledge from LLMs in a structured way. 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In total, we read and present a survey of 44 research papers which talk about 39 different prompting methods on 29 different NLP tasks of which most of them have been published in the last two years.", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 28 + ] + ] + }, + "author": [ + { + "family": "Vatsal", + "given": "Shubham" + }, + { + "family": "Dubey", + "given": "Harsh" + } + ], + "id": "27937/3H7M3AJ8", + "issued": { + "date-parts": [ + [ + 2024, + 7, + 24 + ] + ] + }, + "note": "arXiv:2407.12994", + "number": "arXiv:2407.12994", + "publisher": "arXiv", + "system_id": "zotero|27937/3H7M3AJ8", + "title": "A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks", + "type": "article" + }, + "27937/4ITT4MQK": { + "DOI": "10.48550/arXiv.2402.08021", + "URL": "http://arxiv.org/abs/2402.08021", + "abstract": "Speech-to-text services aim to transcribe input audio as accurately as possible. They increasingly play a role in everyday life, for example in personal voice assistants or in customer-company interactions. We evaluate Open AI's Whisper, a state-of-the-art automated speech recognition service outperforming industry competitors, as of 2023. While many of Whisper's transcriptions were highly accurate, we find that roughly 1\\% of audio transcriptions contained entire hallucinated phrases or sentences which did not exist in any form in the underlying audio. We thematically analyze the Whisper-hallucinated content, finding that 38\\% of hallucinations include explicit harms such as perpetuating violence, making up inaccurate associations, or implying false authority. We then study why hallucinations occur by observing the disparities in hallucination rates between speakers with aphasia (who have a lowered ability to express themselves using speech and voice) and a control group. 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We call on industry practitioners to ameliorate these language-model-based hallucinations in Whisper, and to raise awareness of potential biases amplified by hallucinations in downstream applications of speech-to-text models.", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 28 + ] + ] + }, + "author": [ + { + "family": "Koenecke", + "given": "Allison" + }, + { + "family": "Choi", + "given": "Anna Seo Gyeong" + }, + { + "family": "Mei", + "given": "Katelyn X." + }, + { + "family": "Schellmann", + "given": "Hilke" + }, + { + "family": "Sloane", + "given": "Mona" + } + ], + "id": "27937/4ITT4MQK", + "issued": { + "date-parts": [ + [ + 2024, + 5, + 3 + ] + ] + }, + "note": "arXiv:2402.08021", + "number": "arXiv:2402.08021", + "publisher": "arXiv", + "shortTitle": "Careless Whisper", + "system_id": "zotero|27937/4ITT4MQK", + "title": "Careless Whisper: Speech-to-Text Hallucination Harms", + "type": "article" + }, + "27937/56EE9N63": { + "URL": "http://arxiv.org/abs/2206.07682", + "abstract": "Scaling up language models has been shown to predictably improve performance and sample efficiency on a wide range of downstream tasks. This paper instead discusses an unpredictable phenomenon that we refer to as emergent abilities of large language models. We consider an ability to be emergent if it is not present in smaller models but is present in larger models. Thus, emergent abilities cannot be predicted simply by extrapolating the performance of smaller models. The existence of such emergence implies that additional scaling could further expand the range of capabilities of language models.", + "accessed": { + "date-parts": [ + [ + 2023, + 3, + 27 + ] + ] + }, + "author": [ + { + "family": "Wei", + "given": "Jason" + }, + { + "family": "Tay", + "given": "Yi" + }, + { + "family": "Bommasani", + "given": "Rishi" + }, + { + "family": "Raffel", + "given": "Colin" + }, + { + "family": "Zoph", + "given": "Barret" + }, + { + "family": "Borgeaud", + "given": "Sebastian" + }, + { + "family": "Yogatama", + "given": "Dani" + }, + { + "family": "Bosma", + "given": "Maarten" + }, + { + "family": "Zhou", + "given": "Denny" + }, + { + "family": "Metzler", + "given": "Donald" + }, + { + "family": "Chi", + "given": "Ed H." + }, + { + "family": "Hashimoto", + "given": "Tatsunori" + }, + { + "family": "Vinyals", + "given": "Oriol" + }, + { + "family": "Liang", + "given": "Percy" + }, + { + "family": "Dean", + "given": "Jeff" + }, + { + "family": "Fedus", + "given": "William" + } + ], + "id": "27937/56EE9N63", + "issued": { + "date-parts": [ + [ + 2022, + 10, + 26 + ] + ] + }, + "note": "arXiv:2206.07682 [cs]", + "number": "arXiv:2206.07682", + "publisher": "arXiv", + "system_id": "zotero|27937/56EE9N63", + "title": "Emergent Abilities of Large Language Models", + "type": "article" + }, + "27937/58X69RSW": { + "URL": "http://arxiv.org/abs/2404.14219", + "abstract": "We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi-3-mini achieves 69% on MMLU and 8.38 on MT-bench), despite being small enough to be deployed on a phone. Our training dataset is a scaled-up version of the one used for phi-2, composed of heavily filtered publicly available web data and synthetic data. The model is also further aligned for robustness, safety, and chat format. We also provide parameter-scaling results with a 7B, 14B models trained for 4.8T tokens, called phi-3-small, phi-3-medium, both significantly more capable than phi-3-mini (e.g., respectively 75%, 78% on MMLU, and 8.7, 8.9 on MT-bench). To enhance multilingual, multimodal, and long-context capabilities, we introduce three models in the phi-3.5 series: phi-3.5-mini, phi-3.5-MoE, and phi-3.5-Vision. The phi-3.5-MoE, a 16 x 3.8B MoE model with 6.6 billion active parameters, achieves superior performance in language reasoning, math, and code tasks compared to other open-source models of similar scale, such as Llama 3.1 and the Mixtral series, and on par with Gemini-1.5-Flash and GPT-4o-mini. Meanwhile, phi-3.5-Vision, a 4.2 billion parameter model derived from phi-3.5-mini, excels in reasoning tasks and is adept at handling both single-image and text prompts, as well as multi-image and text prompts.", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 14 + ] + ] + }, + "author": [ + { + "family": "Abdin", + "given": "Marah" + }, + { + "family": "Aneja", + "given": "Jyoti" + }, + { + "family": "Awadalla", + "given": "Hany" + }, + { + "family": "Awadallah", + "given": "Ahmed" + }, + { + "family": "Awan", + "given": "Ammar Ahmad" + }, + { + "family": "Bach", + "given": "Nguyen" + }, + { + "family": "Bahree", + "given": "Amit" + }, + { + "family": "Bakhtiari", + "given": "Arash" + }, + { + "family": "Bao", + "given": "Jianmin" + }, + { + "family": "Behl", + "given": "Harkirat" + }, + { + "family": "Benhaim", + "given": "Alon" + }, + { + "family": "Bilenko", + "given": "Misha" + }, + { + "family": "Bjorck", + "given": "Johan" + }, + { + "family": "Bubeck", + "given": "Sébastien" + }, + { + "family": "Cai", + "given": "Martin" + }, + { + "family": "Cai", + "given": "Qin" + }, + { + "family": "Chaudhary", + "given": "Vishrav" + }, + { + "family": "Chen", + "given": "Dong" + }, + { + "family": "Chen", + "given": "Dongdong" + }, + { + "family": "Chen", + "given": "Weizhu" + }, + { + "family": "Chen", + "given": "Yen-Chun" + }, + { + "family": "Chen", + "given": "Yi-Ling" + }, + { + "family": "Cheng", + "given": "Hao" + }, + { + "family": "Chopra", + "given": "Parul" + }, + { + "family": "Dai", + "given": "Xiyang" + }, + { + "family": "Dixon", + "given": "Matthew" + }, + { + "family": "Eldan", + "given": "Ronen" + }, + { + "family": "Fragoso", + "given": "Victor" + }, + { + "family": "Gao", + "given": "Jianfeng" + }, + { + "family": "Gao", + "given": "Mei" + }, + { + "family": "Gao", + "given": "Min" + }, + { + "family": "Garg", + "given": "Amit" + }, + { + "family": "Giorno", + "given": "Allie Del" + }, + { + "family": "Goswami", + "given": "Abhishek" + }, + { + "family": "Gunasekar", + "given": "Suriya" + }, + { + "family": "Haider", + "given": "Emman" + }, + { + "family": "Hao", + "given": "Junheng" + }, + { + "family": "Hewett", + "given": "Russell J." + }, + { + "family": "Hu", + "given": "Wenxiang" + }, + { + "family": "Huynh", + "given": "Jamie" + }, + { + "family": "Iter", + "given": "Dan" + }, + { + "family": "Jacobs", + "given": "Sam Ade" + }, + { + "family": "Javaheripi", + "given": "Mojan" + }, + { + "family": "Jin", + "given": "Xin" + }, + { + "family": "Karampatziakis", + "given": "Nikos" + }, + { + "family": "Kauffmann", + "given": "Piero" + }, + { + "family": "Khademi", + "given": "Mahoud" + }, + { + "family": "Kim", + "given": "Dongwoo" + }, + { + "family": "Kim", + "given": "Young Jin" + }, + { + "family": "Kurilenko", + "given": "Lev" + }, + { + "family": "Lee", + "given": "James R." + }, + { + "family": "Lee", + "given": "Yin Tat" + }, + { + "family": "Li", + "given": "Yuanzhi" + }, + { + "family": "Li", + "given": "Yunsheng" + }, + { + "family": "Liang", + "given": "Chen" + }, + { + "family": "Liden", + "given": "Lars" + }, + { + "family": "Lin", + "given": "Xihui" + }, + { + "family": "Lin", + "given": "Zeqi" + }, + { + "family": "Liu", + "given": "Ce" + }, + { + "family": "Liu", + "given": "Liyuan" + }, + { + "family": "Liu", + "given": "Mengchen" + }, + { + "family": "Liu", + "given": "Weishung" + }, + { + "family": "Liu", + "given": "Xiaodong" + }, + { + "family": "Luo", + "given": "Chong" + }, + { + "family": "Madan", + "given": "Piyush" + }, + { + "family": "Mahmoudzadeh", + "given": "Ali" + }, + { + "family": "Majercak", + "given": "David" + }, + { + "family": "Mazzola", + "given": "Matt" + }, + { + "family": "Mendes", + "given": "Caio César Teodoro" + }, + { + "family": "Mitra", + "given": "Arindam" + }, + { + "family": "Modi", + "given": "Hardik" + }, + { + "family": "Nguyen", + "given": "Anh" + }, + { + "family": "Norick", + "given": "Brandon" + }, + { + "family": "Patra", + "given": "Barun" + }, + { + "family": "Perez-Becker", + "given": "Daniel" + }, + { + "family": "Portet", + "given": "Thomas" + }, + { + "family": "Pryzant", + "given": "Reid" + }, + { + "family": "Qin", + "given": "Heyang" + }, + { + "family": "Radmilac", + "given": "Marko" + }, + { + "family": "Ren", + "given": "Liliang" + }, + { + "family": "Rosa", + "given": "Gustavo de" + }, + { + "family": "Rosset", + "given": "Corby" + }, + { + "family": "Roy", + "given": "Sambudha" + }, + { + "family": "Ruwase", + "given": "Olatunji" + }, + { + "family": "Saarikivi", + "given": "Olli" + }, + { + "family": "Saied", + "given": "Amin" + }, + { + "family": "Salim", + "given": "Adil" + }, + { + "family": "Santacroce", + "given": "Michael" + }, + { + "family": "Shah", + "given": "Shital" + }, + { + "family": "Shang", + "given": "Ning" + }, + { + "family": "Sharma", + "given": "Hiteshi" + }, + { + "family": "Shen", + "given": "Yelong" + }, + { + "family": "Shukla", + "given": "Swadheen" + }, + { + "family": "Song", + "given": "Xia" + }, + { + "family": "Tanaka", + "given": "Masahiro" + }, + { + "family": "Tupini", + "given": "Andrea" + }, + { + "family": "Vaddamanu", + "given": "Praneetha" + }, + { + "family": "Wang", + "given": "Chunyu" + }, + { + "family": "Wang", + "given": "Guanhua" + }, + { + "family": "Wang", + "given": "Lijuan" + }, + { + "family": "Wang", + "given": "Shuohang" + }, + { + "family": "Wang", + "given": "Xin" + }, + { + "family": "Wang", + "given": "Yu" + }, + { + "family": "Ward", + "given": "Rachel" + }, + { + "family": "Wen", + "given": "Wen" + }, + { + "family": "Witte", + "given": "Philipp" + }, + { + "family": "Wu", + "given": "Haiping" + }, + { + "family": "Wu", + "given": "Xiaoxia" + }, + { + "family": "Wyatt", + "given": "Michael" + }, + { + "family": "Xiao", + "given": "Bin" + }, + { + "family": "Xu", + "given": "Can" + }, + { + "family": "Xu", + "given": "Jiahang" + }, + { + "family": "Xu", + "given": "Weijian" + }, + { + "family": "Xue", + "given": "Jilong" + }, + { + "family": "Yadav", + "given": "Sonali" + }, + { + "family": "Yang", + "given": "Fan" + }, + { + "family": "Yang", + "given": "Jianwei" + }, + { + "family": "Yang", + "given": "Yifan" + }, + { + "family": "Yang", + "given": "Ziyi" + }, + { + "family": "Yu", + "given": "Donghan" + }, + { + "family": "Yuan", + "given": "Lu" + }, + { + "family": "Zhang", + "given": "Chenruidong" + }, + { + "family": "Zhang", + "given": "Cyril" + }, + { + "family": "Zhang", + "given": "Jianwen" + }, + { + "family": "Zhang", + "given": "Li Lyna" + }, + { + "family": "Zhang", + "given": "Yi" + }, + { + "family": "Zhang", + "given": "Yue" + }, + { + "family": "Zhang", + "given": "Yunan" + }, + { + "family": "Zhou", + "given": "Xiren" + } + ], + "id": "27937/58X69RSW", + "issued": { + "date-parts": [ + [ + 2024, + 8, + 30 + ] + ] + }, + "note": "arXiv:2404.14219", + "number": "arXiv:2404.14219", + "publisher": "arXiv", + "shortTitle": "Phi-3 Technical Report", + "system_id": "zotero|27937/58X69RSW", + "title": "Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone", + "type": "article" + }, + "27937/5AL5LZ2K": { + "URL": "https://dr-hutchinson-what-do-ais-know-about-history-app-i3l5jo.streamlit.app/", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 1 + ] + ] + }, + "author": [ + { + "family": "Hutchinson", + "given": "Daniel" + } + ], + "container-title": "What Do AIs Know About History? A Digital History Experiment", + "id": "27937/5AL5LZ2K", + "issued": { + "date-parts": [ + [ + 2022 + ] + ] + }, + "system_id": "zotero|27937/5AL5LZ2K", + "type": "webpage" + }, + "27937/5ED45HQE": { + "URL": "https://univ-rochelle.hal.science/hal-04662000", + "abstract": "The efficacy of large-scale language models (LLMs) as few-shot learners has dominated the field of natural language processing, achieving state-of-the-art performance in most tasks, including named entity recognition (NER) for contemporary texts. However, exploration of NER in historical collections (e.g., historical newspapers and classical commentaries) remains limited. This presents a greater challenge as historical texts are often noisy due to storage conditions, OCR extraction, and spelling variation. In this paper, we conduct an empirical evaluation comparing different Instruct variants of open-access and open-sourced LLMs using prompt engineering through deductive (with guidelines) and inductive (without guidelines) approaches against the fully supervised benchmarks. In addition, we study how the interaction between the Instruct model and the user impacts the entity prediction. We conduct reproducible experiments using an easy-to-implement mechanism on publicly available historical collections covering three languages (i.e., English, French, and German) with code-switching on Ancient Greek and four open Instruct models. The results show that Instruct models encounter multiple difficulties handling the noisy input documents, scoring lower than fine-tuned dedicated NER systems, yet the resulting predictions provide entities that can be used in further tagging processes by human annotators.", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 16 + ] + ] + }, + "author": [ + { + "family": "González-Gallardo", + "given": "Carlos-Emiliano" + }, + { + "family": "Hanh", + "given": "Tran Thi Hong" + }, + { + "family": "Hamdi", + "given": "Ahmed" + }, + { + "family": "Doucet", + "given": "Antoine" + } + ], + "event": "The 28th International Conference on Theory and Practice of Digital Libraries", + "id": "27937/5ED45HQE", + "issued": { + "date-parts": [ + [ + 2024, + 9, + 24 + ] + ] + }, + "language": "en", + "system_id": "zotero|27937/5ED45HQE", + "title": "Leveraging Open Large Language Models for Historical Named Entity Recognition", + "type": "paper-conference" + }, + "27937/5G5LJCLC": { + "URL": "https://github.com/dair-ai/Prompt-Engineering-Guide", + "abstract": "🐙 Guides, papers, lecture, notebooks and resources for prompt engineering", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 25 + ] + ] + }, + "author": [ + { + "family": "Saravia", + "given": "Elvis" + } + ], + "id": "27937/5G5LJCLC", + "issued": { + "date-parts": [ + [ + 2022, + 12 + ] + ] + }, + "note": "Publication Title: https://github.com/dair-ai/Prompt-Engineering-Guide\noriginal-date: 2022-12-16T16:04:50Z", + "system_id": "zotero|27937/5G5LJCLC", + "title": "Prompt Engineering Guide", + "type": "book" + }, + "27937/5GTQD5W9": { + "URL": "https://excavating.ai", + "abstract": "An investigation into the politics of training sets, and the fundamental problems with classifying humans.", + "accessed": { + "date-parts": [ + [ + 2023, + 3, + 28 + ] + ] + }, + "author": [ + { + "family": "Crawford", + "given": "Kate" + }, + { + "family": "Paglen", + "given": "Trevor" + } + ], + "container-title": "Excavating AI", + "id": "27937/5GTQD5W9", + "language": "en-US", + "system_id": "zotero|27937/5GTQD5W9", + "title": "Excavating AI", + "type": "webpage" + }, + "27937/5YDNQS4V": { + "URL": "https://www.brookings.edu/research/algorithmic-bias-detection-and-mitigation-best-practices-and-policies-to-reduce-consumer-harms/", + "abstract": "Algorithms must be responsibly created to avoid discrimination and unethical applications.", + "accessed": { + "date-parts": [ + [ + 2023, + 3, + 28 + ] + ] + }, + "author": [ + { + "family": "Barton", + "given": "Nicol Turner Lee, Paul Resnick, and Genie" + } + ], + "container-title": "Brookings", + "id": "27937/5YDNQS4V", + "issued": { + "date-parts": [ + [ + 2019, + 5, + 22 + ] + ] + }, + "language": "en-US", + "shortTitle": "Algorithmic bias detection and mitigation", + "system_id": "zotero|27937/5YDNQS4V", + "title": "Algorithmic bias detection and mitigation: Best practices and policies to reduce consumer harms", + "type": "post-weblog" + }, + "27937/68YHDUH6": { + "URL": "https://blog.ehri-project.eu/2018/08/27/named-entity-recognition/", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 15 + ] + ] + }, + "author": [ + { + "family": "Nikolova", + "given": "Ivelina" + }, + { + "family": "Levy", + "given": "Michael" + } + ], + "container-title": "The European Holocaust Research Infrastructure Document Blog", + "id": "27937/68YHDUH6", + "issued": { + "date-parts": [ + [ + 2018, + 8, + 27 + ] + ] + }, + "language": "en-GB", + "system_id": "zotero|27937/68YHDUH6", + "title": "Using Named Entity Recognition to Enhance Access to a Museum Catalog – Document Blog", + "type": "post-weblog" + }, + "27937/78DL3V96": { + "URL": "https://contingentmagazine.org/2020/10/20/apush/", + "abstract": "Born out of the Cold War, the course has a great contradiction at its heart: why do we teach history?", + "accessed": { + "date-parts": [ + [ + 2024, + 11, + 19 + ] + ] + }, + "author": [ + { + "family": "Marshall", + "given": "Lindsay" + } + ], + "container-title": "CONTINGENT", + "id": "27937/78DL3V96", + "issued": { + "date-parts": [ + [ + 2020, + 10, + 20 + ] + ] + }, + "language": "en-US", + "system_id": "zotero|27937/78DL3V96", + "title": "The Strange World of AP U.S. History", + "type": "webpage" + }, + "27937/7D6BEHLB": { + "URL": "https://programminghistorian.org/en/lessons/understanding-creating-word-embeddings", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 16 + ] + ] + }, + "author": [ + { + "family": "Blankenship", + "given": "Avery" + }, + { + "family": "Connell", + "given": "Sarah" + }, + { + "family": "Dombrowski", + "given": "Quinn" + } + ], + "container-title": "Programming Historian", + "id": "27937/7D6BEHLB", + "issued": { + "date-parts": [ + [ + 2024, + 1, + 31 + ] + ] + }, + "language": "en", + "system_id": "zotero|27937/7D6BEHLB", + "title": "Understanding and Creating Word Embeddings", + "type": "article-journal" + }, + "27937/7VHKCH3M": { + "URL": "http://arxiv.org/abs/2212.04356", + "abstract": "We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual and multitask supervision, the resulting models generalize well to standard benchmarks and are often competitive with prior fully supervised results but in a zero-shot transfer setting without the need for any fine-tuning. When compared to humans, the models approach their accuracy and robustness. We are releasing models and inference code to serve as a foundation for further work on robust speech processing.", + "accessed": { + "date-parts": [ + [ + 2023, + 3, + 29 + ] + ] + }, + "author": [ + { + "family": "Radford", + "given": "Alec" + }, + { + "family": "Kim", + "given": "Jong Wook" + }, + { + "family": "Xu", + "given": "Tao" + }, + { + "family": "Brockman", + "given": "Greg" + }, + { + "family": "McLeavey", + "given": "Christine" + }, + { + "family": "Sutskever", + "given": "Ilya" + } + ], + "id": "27937/7VHKCH3M", + "issued": { + "date-parts": [ + [ + 2022, + 12, + 6 + ] + ] + }, + "note": "arXiv:2212.04356 [cs, eess]", + "number": "arXiv:2212.04356", + "publisher": "arXiv", + "system_id": "zotero|27937/7VHKCH3M", + "title": "Robust Speech Recognition via Large-Scale Weak Supervision", + "type": "article" + }, + "27937/82YIALT5": { + "DOI": "10.48550/arXiv.2409.00128", + "URL": "http://arxiv.org/abs/2409.00128", + "abstract": "Artificial Intelligence (AI) is increasingly being integrated into scientific research, particularly in the social sciences, where understanding human behavior is critical. Large Language Models (LLMs) like GPT-4 have shown promise in replicating human-like responses in various psychological experiments. However, the extent to which LLMs can effectively replace human subjects across diverse experimental contexts remains unclear. Here, we conduct a large-scale study replicating 154 psychological experiments from top social science journals with 618 main effects and 138 interaction effects using GPT-4 as a simulated participant. We find that GPT-4 successfully replicates 76.0 percent of main effects and 47.0 percent of interaction effects observed in the original studies, closely mirroring human responses in both direction and significance. However, only 19.44 percent of GPT-4's replicated confidence intervals contain the original effect sizes, with the majority of replicated effect sizes exceeding the 95 percent confidence interval of the original studies. Additionally, there is a 71.6 percent rate of unexpected significant results where the original studies reported null findings, suggesting potential overestimation or false positives. Our results demonstrate the potential of LLMs as powerful tools in psychological research but also emphasize the need for caution in interpreting AI-driven findings. While LLMs can complement human studies, they cannot yet fully replace the nuanced insights provided by human subjects.", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 23 + ] + ] + }, + "author": [ + { + "family": "Cui", + "given": "Ziyan" + }, + { + "family": "Li", + "given": "Ning" + }, + { + "family": "Zhou", + "given": "Huaikang" + } + ], + "id": "27937/82YIALT5", + "issued": { + "date-parts": [ + [ + 2024, + 9, + 4 + ] + ] + }, + "note": "arXiv:2409.00128", + "number": "arXiv:2409.00128", + "publisher": "arXiv", + "shortTitle": "Can AI Replace Human Subjects?", + "system_id": "zotero|27937/82YIALT5", + "title": "Can AI Replace Human Subjects? A Large-Scale Replication of Psychological Experiments with LLMs", + "type": "article" + }, + "27937/9CA225ZV": { + "DOI": "10.1038/s44184-024-00056-z", + "URL": "https://www.nature.com/articles/s44184-024-00056-z", + "abstract": "Large language models (LLMs) such as Open AI’s GPT-4 (which power ChatGPT) and Google’s Gemini, built on artificial intelligence, hold immense potential to support, augment, or even eventually automate psychotherapy. Enthusiasm about such applications is mounting in the field as well as industry. These developments promise to address insufficient mental healthcare system capacity and scale individual access to personalized treatments. However, clinical psychology is an uncommonly high stakes application domain for AI systems, as responsible and evidence-based therapy requires nuanced expertise. This paper provides a roadmap for the ambitious yet responsible application of clinical LLMs in psychotherapy. First, a technical overview of clinical LLMs is presented. Second, the stages of integration of LLMs into psychotherapy are discussed while highlighting parallels to the development of autonomous vehicle technology. Third, potential applications of LLMs in clinical care, training, and research are discussed, highlighting areas of risk given the complex nature of psychotherapy. Fourth, recommendations for the responsible development and evaluation of clinical LLMs are provided, which include centering clinical science, involving robust interdisciplinary collaboration, and attending to issues like assessment, risk detection, transparency, and bias. Lastly, a vision is outlined for how LLMs might enable a new generation of studies of evidence-based interventions at scale, and how these studies may challenge assumptions about psychotherapy.", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 23 + ] + ] + }, + "author": [ + { + "family": "Stade", + "given": "Elizabeth C." + }, + { + "family": "Stirman", + "given": "Shannon Wiltsey" + }, + { + "family": "Ungar", + "given": "Lyle H." + }, + { + "family": "Boland", + "given": "Cody L." + }, + { + "family": "Schwartz", + "given": "H. Andrew" + }, + { + "family": "Yaden", + "given": "David B." + }, + { + "family": "Sedoc", + "given": "João" + }, + { + "family": "DeRubeis", + "given": "Robert J." + }, + { + "family": "Willer", + "given": "Robb" + }, + { + "family": "Eichstaedt", + "given": "Johannes C." + } + ], + "container-title": "npj Mental Health Research", + "id": "27937/9CA225ZV", + "issue": "1", + "issued": { + "date-parts": [ + [ + 2024, + 4, + 2 + ] + ] + }, + "journalAbbreviation": "npj Mental Health Res", + "language": "en", + "note": "Publisher: Nature Publishing Group", + "page": "1-12", + "shortTitle": "Large language models could change the future of behavioral healthcare", + "system_id": "zotero|27937/9CA225ZV", + "title": "Large language models could change the future of behavioral healthcare: a proposal for responsible development and evaluation", + "type": "article-journal", + "volume": "3" + }, + "27937/9GQG6VFM": { + "DOI": "10.1145/3571730", + "URL": "http://arxiv.org/abs/2202.03629", + "abstract": "Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation. However, it is also apparent that deep learning based generation is prone to hallucinate unintended text, which degrades the system performance and fails to meet user expectations in many real-world scenarios. To address this issue, many studies have been presented in measuring and mitigating hallucinated texts, but these have never been reviewed in a comprehensive manner before. In this survey, we thus provide a broad overview of the research progress and challenges in the hallucination problem in NLG. The survey is organized into two parts: (1) a general overview of metrics, mitigation methods, and future directions; and (2) an overview of task-specific research progress on hallucinations in the following downstream tasks, namely abstractive summarization, dialogue generation, generative question answering, data-to-text generation, machine translation, and visual-language generation. This survey serves to facilitate collaborative efforts among researchers in tackling the challenge of hallucinated texts in NLG.", + "accessed": { + "date-parts": [ + [ + 2023, + 4, + 3 + ] + ] + }, + "author": [ + { + "family": "Ji", + "given": "Ziwei" + }, + { + "family": "Lee", + "given": "Nayeon" + }, + { + "family": "Frieske", + "given": "Rita" + }, + { + "family": "Yu", + "given": "Tiezheng" + }, + { + "family": "Su", + "given": "Dan" + }, + { + "family": "Xu", + "given": "Yan" + }, + { + "family": "Ishii", + "given": "Etsuko" + }, + { + "family": "Bang", + "given": "Yejin" + }, + { + "family": "Dai", + "given": "Wenliang" + }, + { + "family": "Madotto", + "given": "Andrea" + }, + { + "family": "Fung", + "given": "Pascale" + } + ], + "container-title": "ACM Computing Surveys", + "id": "27937/9GQG6VFM", + "issue": "12", + "issued": { + "date-parts": [ + [ + 2023, + 12, + 31 + ] + ] + }, + "journalAbbreviation": "ACM Comput. Surv.", + "note": "arXiv:2202.03629 [cs]", + "page": "1-38", + "system_id": "zotero|27937/9GQG6VFM", + "title": "Survey of Hallucination in Natural Language Generation", + "type": "article-journal", + "volume": "55" + }, + "27937/9T2I7QLM": { + "URL": "http://arxiv.org/abs/1706.03762", + "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 25 + ] + ] + }, + "author": [ + { + "family": "Vaswani", + "given": "Ashish" + }, + { + "family": "Shazeer", + "given": "Noam" + }, + { + "family": "Parmar", + "given": "Niki" + }, + { + "family": "Uszkoreit", + "given": "Jakob" + }, + { + "family": "Jones", + "given": "Llion" + }, + { + "family": "Gomez", + "given": "Aidan N." + }, + { + "family": "Kaiser", + "given": "Lukasz" + }, + { + "family": "Polosukhin", + "given": "Illia" + } + ], + "id": "27937/9T2I7QLM", + "issued": { + "date-parts": [ + [ + 2023, + 8, + 2 + ] + ] + }, + "note": "arXiv:1706.03762", + "number": "arXiv:1706.03762", + "publisher": "arXiv", + "system_id": "zotero|27937/9T2I7QLM", + "title": "Attention Is All You Need", + "type": "article" + }, + "27937/A834FRJL": { + "URL": "https://github.com/hendrycks/test", + "abstract": "Measuring Massive Multitask Language Understanding | ICLR 2021", + "accessed": { + "date-parts": [ + [ + 2023, + 4, + 2 + ] + ] + }, + "author": [ + { + "family": "Hendrycks", + "given": "Dan" + } + ], + "id": "27937/A834FRJL", + "issued": { + "date-parts": [ + [ + 2023, + 4, + 2 + ] + ] + }, + "note": "original-date: 2020-09-07T23:02:57Z", + "system_id": "zotero|27937/A834FRJL", + "title": "Measuring Massive Multitask Language Understanding", + "type": "book" + }, + "27937/BD8996H7": { + "URL": "https://reports.collegeboard.org/media/pdf/program-summary-report-2022.pdf", + "container-title": "AP Exam Administration Data Archive", + "id": "27937/BD8996H7", + "issued": { + "date-parts": [ + [ + 2022 + ] + ] + }, + "system_id": "zotero|27937/BD8996H7", + "title": "Program Summary Report", + "type": "webpage" + }, + "27937/BN44JR8V": { + "URL": "https://hcommons.org/deposits/item/hc:41973/", + "abstract": "As neural language models begin to change aspects of everyday life, they understandably attract criticism. This position paper was commissioned for a roundtable at Princeton University, dedicated to one of the most influential critiques: \"On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?\" by Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Margaret Mitchell. \n\nMy paper agrees that neural language models pose a variety of dangers, starting with and not limited to the list in \"Stochastic Parrots.\" But to understand those dangers, I think we need to look beyond the premise that these models mimic \"language understanding\" on an individual level. That may have been what linguists and computer scientists intended them to do. But the models' actual potential (for both good and ill) is more interesting, and will be easier to grasp if we approach them as models of culture. Science-fictional scenarios about robots that become autonomous (or remain mere \"parrots\") are less useful here than humanistic cultural theory.", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 23 + ] + ] + }, + "author": [ + { + "family": "Underwood", + "given": "Ted" + } + ], + "id": "27937/BN44JR8V", + "issued": { + "date-parts": [ + [ + "2021", + 10, + 20 + ] + ] + }, + "language": "en-US", + "system_id": "zotero|27937/BN44JR8V", + "title": "Mapping the Latent Spaces of Culture", + "type": "article-journal" + }, + "27937/BVBZMR66": { + "DOI": "10.48550/arXiv.2212.14402", + "URL": "http://arxiv.org/abs/2212.14402", + "abstract": "Nearly all jurisdictions in the United States require a professional license exam, commonly referred to as \"the Bar Exam,\" as a precondition for law practice. To even sit for the exam, most jurisdictions require that an applicant completes at least seven years of post-secondary education, including three years at an accredited law school. In addition, most test-takers also undergo weeks to months of further, exam-specific preparation. Despite this significant investment of time and capital, approximately one in five test-takers still score under the rate required to pass the exam on their first try. In the face of a complex task that requires such depth of knowledge, what, then, should we expect of the state of the art in \"AI?\" In this research, we document our experimental evaluation of the performance of OpenAI's `text-davinci-003` model, often-referred to as GPT-3.5, on the multistate multiple choice (MBE) section of the exam. While we find no benefit in fine-tuning over GPT-3.5's zero-shot performance at the scale of our training data, we do find that hyperparameter optimization and prompt engineering positively impacted GPT-3.5's zero-shot performance. For best prompt and parameters, GPT-3.5 achieves a headline correct rate of 50.3% on a complete NCBE MBE practice exam, significantly in excess of the 25% baseline guessing rate, and performs at a passing rate for both Evidence and Torts. GPT-3.5's ranking of responses is also highly-correlated with correctness; its top two and top three choices are correct 71% and 88% of the time, respectively, indicating very strong non-entailment performance. While our ability to interpret these results is limited by nascent scientific understanding of LLMs and the proprietary nature of GPT, we believe that these results strongly suggest that an LLM will pass the MBE component of the Bar Exam in the near future.", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 25 + ] + ] + }, + "author": [ + { + "family": "Katz", + "given": "Daniel Martin" + } + ], + "id": "27937/BVBZMR66", + "issued": { + "date-parts": [ + [ + 2022, + 12, + 29 + ] + ] + }, + "note": "arXiv:2212.14402", + "number": "arXiv:2212.14402", + "publisher": "arXiv", + "system_id": "zotero|27937/BVBZMR66", + "title": "GPT Takes the Bar Exam", + "type": "article" + }, + "27937/CDKJEW4Z": { + "DOI": "10.1093/ahr/rhad365", + "URL": "https://academic.oup.com/ahr/article/128/3/1354/7282256", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 28 + ] + ] + }, + "author": [ + { + "family": "Tilton", + "given": "Lauren" + } + ], + "container-title": "The American Historical Review", + "id": "27937/CDKJEW4Z", + "issue": "3", + "issued": { + "date-parts": [ + [ + 2023, + 9, + 26 + ] + ] + }, + "language": "en", + "page": "1354-1359", + "system_id": "zotero|27937/CDKJEW4Z", + "title": "Relating to Historical Sources", + "type": "article-journal", + "volume": "128" + }, + "27937/CJYNFHVI": { + "DOI": "10.1086/ahr/108.3.735", + "URL": "https://doi.org/10.1086/ahr/108.3.735", + "accessed": { + "date-parts": [ + [ + 2023, + 3, + 27 + ] + ] + }, + "author": [ + { + "family": "Rosenzweig", + "given": "Roy" + } + ], + "container-title": "The American Historical Review", + "id": "27937/CJYNFHVI", + "issue": "3", + "issued": { + "date-parts": [ + [ + 2003, + 6, + 1 + ] + ] + }, + "journalAbbreviation": "The American Historical Review", + "page": "735-762", + "shortTitle": "Scarcity or Abundance?", + "system_id": "zotero|27937/CJYNFHVI", + "title": "Scarcity or Abundance? Preserving the Past in a Digital Era", + "type": "article-journal", + "volume": "108" + }, + "27937/CXURVMLQ": { + "DOI": "10.37514/TWR-J.2023.1.1.02", + "URL": "https://wac.colostate.edu/repository/collections/textgened/", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 25 + ] + ] + }, + "author": [ + { + "family": "Vee", + "given": "Annette" + }, + { + "family": "Laquintano", + "given": "Tim" + }, + { + "family": "Schnitzler", + "given": "Carly" + } + ], + "container-title": "The WAC Repository", + "id": "27937/CXURVMLQ", + "issue": "1", + "issued": { + "date-parts": [ + [ + 2023 + ] + ] + }, + "language": "en", + "page": "1-100", + "system_id": "zotero|27937/CXURVMLQ", + "title": "TextGenEd Exhibit", + "type": "article-journal", + "volume": "1" + }, + "27937/ECQ4J8E9": { + "DOI": "10.48550/arXiv.2005.11401", + "URL": "http://arxiv.org/abs/2005.11401", + "abstract": "Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, their ability to access and precisely manipulate knowledge is still limited, and hence on knowledge-intensive tasks, their performance lags behind task-specific architectures. Additionally, providing provenance for their decisions and updating their world knowledge remain open research problems. Pre-trained models with a differentiable access mechanism to explicit non-parametric memory can overcome this issue, but have so far been only investigated for extractive downstream tasks. We explore a general-purpose fine-tuning recipe for retrieval-augmented generation (RAG) -- models which combine pre-trained parametric and non-parametric memory for language generation. We introduce RAG models where the parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever. We compare two RAG formulations, one which conditions on the same retrieved passages across the whole generated sequence, the other can use different passages per token. We fine-tune and evaluate our models on a wide range of knowledge-intensive NLP tasks and set the state-of-the-art on three open domain QA tasks, outperforming parametric seq2seq models and task-specific retrieve-and-extract architectures. For language generation tasks, we find that RAG models generate more specific, diverse and factual language than a state-of-the-art parametric-only seq2seq baseline.", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 16 + ] + ] + }, + "author": [ + { + "family": "Lewis", + "given": "Patrick" + }, + { + "family": "Perez", + "given": "Ethan" + }, + { + "family": "Piktus", + "given": "Aleksandra" + }, + { + "family": "Petroni", + "given": "Fabio" + }, + { + "family": "Karpukhin", + "given": "Vladimir" + }, + { + "family": "Goyal", + "given": "Naman" + }, + { + "family": "Küttler", + "given": "Heinrich" + }, + { + "family": "Lewis", + "given": "Mike" + }, + { + "family": "Yih", + "given": "Wen-tau" + }, + { + "family": "Rocktäschel", + "given": "Tim" + }, + { + "family": "Riedel", + "given": "Sebastian" + }, + { + "family": "Kiela", + "given": "Douwe" + } + ], + "id": "27937/ECQ4J8E9", + "issued": { + "date-parts": [ + [ + 2021, + 4, + 12 + ] + ] + }, + "note": "arXiv:2005.11401", + "number": "arXiv:2005.11401", + "publisher": "arXiv", + "system_id": "zotero|27937/ECQ4J8E9", + "title": "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks", + "type": "article" + }, + "27937/EZNK3CE3": { + "URL": "http://site.ebrary.com/id/10158052", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 25 + ] + ] + }, + "author": [ + { + "family": "McCorduck", + "given": "Pamela" + } + ], + "edition": "25th anniversary update", + "event-place": "Natick, Mass.", + "id": "27937/EZNK3CE3", + "issued": { + "date-parts": [ + [ + 2004 + ] + ] + }, + "language": "eng", + "note": "OCLC: 748860627", + "number-of-pages": "1", + "publisher": "A.K. Peters", + "publisher-place": "Natick, Mass.", + "system_id": "zotero|27937/EZNK3CE3", + "title": "Machines who think a personal inquiry into the history and prospects of artificial intelligence", + "type": "book" + }, + "27937/F3XT4XAQ": { + "URL": "http://arxiv.org/abs/2108.07258", + "abstract": "AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.", + "accessed": { + "date-parts": [ + [ + 2023, + 3, + 27 + ] + ] + }, + "author": [ + { + "family": "Bommasani", + "given": "Rishi" + }, + { + "family": "Hudson", + "given": "Drew A." + }, + { + "family": "Adeli", + "given": "Ehsan" + }, + { + "family": "Altman", + "given": "Russ" + }, + { + "family": "Arora", + "given": "Simran" + }, + { + "family": "von Arx", + "given": "Sydney" + }, + { + "family": "Bernstein", + "given": "Michael S." + }, + { + "family": "Bohg", + "given": "Jeannette" + }, + { + "family": "Bosselut", + "given": "Antoine" + }, + { + "family": "Brunskill", + "given": "Emma" + }, + { + "family": "Brynjolfsson", + "given": "Erik" + }, + { + "family": "Buch", + "given": "Shyamal" + }, + { + "family": "Card", + "given": "Dallas" + }, + { + "family": "Castellon", + "given": "Rodrigo" + }, + { + "family": "Chatterji", + "given": "Niladri" + }, + { + "family": "Chen", + "given": "Annie" + }, + { + "family": "Creel", + "given": "Kathleen" + }, + { + "family": "Davis", + "given": "Jared Quincy" + }, + { + "family": "Demszky", + "given": "Dora" + }, + { + "family": "Donahue", + "given": "Chris" + }, + { + "family": "Doumbouya", + "given": "Moussa" + }, + { + "family": "Durmus", + "given": "Esin" + }, + { + "family": "Ermon", + "given": "Stefano" + }, + { + "family": "Etchemendy", + "given": "John" + }, + { + "family": "Ethayarajh", + "given": "Kawin" + }, + { + "family": "Fei-Fei", + "given": "Li" + }, + { + "family": "Finn", + "given": "Chelsea" + }, + { + "family": "Gale", + "given": "Trevor" + }, + { + "family": "Gillespie", + "given": "Lauren" + }, + { + "family": "Goel", + "given": "Karan" + }, + { + "family": "Goodman", + "given": "Noah" + }, + { + "family": "Grossman", + "given": "Shelby" + }, + { + "family": "Guha", + "given": "Neel" + }, + { + "family": "Hashimoto", + "given": "Tatsunori" + }, + { + "family": "Henderson", + "given": "Peter" + }, + { + "family": "Hewitt", + "given": "John" + }, + { + "family": "Ho", + "given": "Daniel E." + }, + { + "family": "Hong", + "given": "Jenny" + }, + { + "family": "Hsu", + "given": "Kyle" + }, + { + "family": "Huang", + "given": "Jing" + }, + { + "family": "Icard", + "given": "Thomas" + }, + { + "family": "Jain", + "given": "Saahil" + }, + { + "family": "Jurafsky", + "given": "Dan" + }, + { + "family": "Kalluri", + "given": "Pratyusha" + }, + { + "family": "Karamcheti", + "given": "Siddharth" + }, + { + "family": "Keeling", + "given": "Geoff" + }, + { + "family": "Khani", + "given": "Fereshte" + }, + { + "family": "Khattab", + "given": "Omar" + }, + { + "family": "Koh", + "given": "Pang Wei" + }, + { + "family": "Krass", + "given": "Mark" + }, + { + "family": "Krishna", + "given": "Ranjay" + }, + { + "family": "Kuditipudi", + "given": "Rohith" + }, + { + "family": "Kumar", + "given": "Ananya" + }, + { + "family": "Ladhak", + "given": "Faisal" + }, + { + "family": "Lee", + "given": "Mina" + }, + { + "family": "Lee", + "given": "Tony" + }, + { + "family": "Leskovec", + "given": "Jure" + }, + { + "family": "Levent", + "given": "Isabelle" + }, + { + "family": "Li", + "given": "Xiang Lisa" + }, + { + "family": "Li", + "given": "Xuechen" + }, + { + "family": "Ma", + "given": "Tengyu" + }, + { + "family": "Malik", + "given": "Ali" + }, + { + "family": "Manning", + "given": "Christopher D." + }, + { + "family": "Mirchandani", + "given": "Suvir" + }, + { + "family": "Mitchell", + "given": "Eric" + }, + { + "family": "Munyikwa", + "given": "Zanele" + }, + { + "family": "Nair", + "given": "Suraj" + }, + { + "family": "Narayan", + "given": "Avanika" + }, + { + "family": "Narayanan", + "given": "Deepak" + }, + { + "family": "Newman", + "given": "Ben" + }, + { + "family": "Nie", + "given": "Allen" + }, + { + "family": "Niebles", + "given": "Juan Carlos" + }, + { + "family": "Nilforoshan", + "given": "Hamed" + }, + { + "family": "Nyarko", + "given": "Julian" + }, + { + "family": "Ogut", + "given": "Giray" + }, + { + "family": "Orr", + "given": "Laurel" + }, + { + "family": "Papadimitriou", + "given": "Isabel" + }, + { + "family": "Park", + "given": "Joon Sung" + }, + { + "family": "Piech", + "given": "Chris" + }, + { + "family": "Portelance", + "given": "Eva" + }, + { + "family": "Potts", + "given": "Christopher" + }, + { + "family": "Raghunathan", + "given": "Aditi" + }, + { + "family": "Reich", + "given": "Rob" + }, + { + "family": "Ren", + "given": "Hongyu" + }, + { + "family": "Rong", + "given": "Frieda" + }, + { + "family": "Roohani", + "given": "Yusuf" + }, + { + "family": "Ruiz", + "given": "Camilo" + }, + { + "family": "Ryan", + "given": "Jack" + }, + { + "family": "Ré", + "given": "Christopher" + }, + { + "family": "Sadigh", + "given": "Dorsa" + }, + { + "family": "Sagawa", + "given": "Shiori" + }, + { + "family": "Santhanam", + "given": "Keshav" + }, + { + "family": "Shih", + "given": "Andy" + }, + { + "family": "Srinivasan", + "given": "Krishnan" + }, + { + "family": "Tamkin", + "given": "Alex" + }, + { + "family": "Taori", + "given": "Rohan" + }, + { + "family": "Thomas", + "given": "Armin W." + }, + { + "family": "Tramèr", + "given": "Florian" + }, + { + "family": "Wang", + "given": "Rose E." + }, + { + "family": "Wang", + "given": "William" + }, + { + "family": "Wu", + "given": "Bohan" + }, + { + "family": "Wu", + "given": "Jiajun" + }, + { + "family": "Wu", + "given": "Yuhuai" + }, + { + "family": "Xie", + "given": "Sang Michael" + }, + { + "family": "Yasunaga", + "given": "Michihiro" + }, + { + "family": "You", + "given": "Jiaxuan" + }, + { + "family": "Zaharia", + "given": "Matei" + }, + { + "family": "Zhang", + "given": "Michael" + }, + { + "family": "Zhang", + "given": "Tianyi" + }, + { + "family": "Zhang", + "given": "Xikun" + }, + { + "family": "Zhang", + "given": "Yuhui" + }, + { + "family": "Zheng", + "given": "Lucia" + }, + { + "family": "Zhou", + "given": "Kaitlyn" + }, + { + "family": "Liang", + "given": "Percy" + } + ], + "id": "27937/F3XT4XAQ", + "issued": { + "date-parts": [ + [ + 2022, + 7, + 12 + ] + ] + }, + "note": "arXiv:2108.07258 [cs]", + "number": "arXiv:2108.07258", + "publisher": "arXiv", + "system_id": "zotero|27937/F3XT4XAQ", + "title": "On the Opportunities and Risks of Foundation Models", + "type": "article" + }, + "27937/FMW5DCWM": { + "URL": "http://arxiv.org/abs/2204.02311", + "abstract": "Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed to adapt the model to a particular application. To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly efficient training across multiple TPU Pods. We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model. PaLM also has strong capabilities in multilingual tasks and source code generation, which we demonstrate on a wide array of benchmarks. We additionally provide a comprehensive analysis on bias and toxicity, and study the extent of training data memorization with respect to model scale. Finally, we discuss the ethical considerations related to large language models and discuss potential mitigation strategies.", + "accessed": { + "date-parts": [ + [ + 2023, + 3, + 28 + ] + ] + }, + "author": [ + { + "family": "Chowdhery", + "given": "Aakanksha" + }, + { + "family": "Narang", + "given": "Sharan" + }, + { + "family": "Devlin", + "given": "Jacob" + }, + { + "family": "Bosma", + "given": "Maarten" + }, + { + "family": "Mishra", + "given": "Gaurav" + }, + { + "family": "Roberts", + "given": "Adam" + }, + { + "family": "Barham", + "given": "Paul" + }, + { + "family": "Chung", + "given": "Hyung Won" + }, + { + "family": "Sutton", + "given": "Charles" + }, + { + "family": "Gehrmann", + "given": "Sebastian" + }, + { + "family": "Schuh", + "given": "Parker" + }, + { + "family": "Shi", + "given": "Kensen" + }, + { + "family": "Tsvyashchenko", + "given": "Sasha" + }, + { + "family": "Maynez", + "given": "Joshua" + }, + { + "family": "Rao", + "given": "Abhishek" + }, + { + "family": "Barnes", + "given": "Parker" + }, + { + "family": "Tay", + "given": "Yi" + }, + { + "family": "Shazeer", + "given": "Noam" + }, + { + "family": "Prabhakaran", + "given": "Vinodkumar" + }, + { + "family": "Reif", + "given": "Emily" + }, + { + "family": "Du", + "given": "Nan" + }, + { + "family": "Hutchinson", + "given": "Ben" + }, + { + "family": "Pope", + "given": "Reiner" + }, + { + "family": "Bradbury", + "given": "James" + }, + { + "family": "Austin", + "given": "Jacob" + }, + { + "family": "Isard", + "given": "Michael" + }, + { + "family": "Gur-Ari", + "given": "Guy" + }, + { + "family": "Yin", + "given": "Pengcheng" + }, + { + "family": "Duke", + "given": "Toju" + }, + { + "family": "Levskaya", + "given": "Anselm" + }, + { + "family": "Ghemawat", + "given": "Sanjay" + }, + { + "family": "Dev", + "given": "Sunipa" + }, + { + "family": "Michalewski", + "given": "Henryk" + }, + { + "family": "Garcia", + "given": "Xavier" + }, + { + "family": "Misra", + "given": "Vedant" + }, + { + "family": "Robinson", + "given": "Kevin" + }, + { + "family": "Fedus", + "given": "Liam" + }, + { + "family": "Zhou", + "given": "Denny" + }, + { + "family": "Ippolito", + "given": "Daphne" + }, + { + "family": "Luan", + "given": "David" + }, + { + "family": "Lim", + "given": "Hyeontaek" + }, + { + "family": "Zoph", + "given": "Barret" + }, + { + "family": "Spiridonov", + "given": "Alexander" + }, + { + "family": "Sepassi", + "given": "Ryan" + }, + { + "family": "Dohan", + "given": "David" + }, + { + "family": "Agrawal", + "given": "Shivani" + }, + { + "family": "Omernick", + "given": "Mark" + }, + { + "family": "Dai", + "given": "Andrew M." + }, + { + "family": "Pillai", + "given": "Thanumalayan Sankaranarayana" + }, + { + "family": "Pellat", + "given": "Marie" + }, + { + "family": "Lewkowycz", + "given": "Aitor" + }, + { + "family": "Moreira", + "given": "Erica" + }, + { + "family": "Child", + "given": "Rewon" + }, + { + "family": "Polozov", + "given": "Oleksandr" + }, + { + "family": "Lee", + "given": "Katherine" + }, + { + "family": "Zhou", + "given": "Zongwei" + }, + { + "family": "Wang", + "given": "Xuezhi" + }, + { + "family": "Saeta", + "given": "Brennan" + }, + { + "family": "Diaz", + "given": "Mark" + }, + { + "family": "Firat", + "given": "Orhan" + }, + { + "family": "Catasta", + "given": "Michele" + }, + { + "family": "Wei", + "given": "Jason" + }, + { + "family": "Meier-Hellstern", + "given": "Kathy" + }, + { + "family": "Eck", + "given": "Douglas" + }, + { + "family": "Dean", + "given": "Jeff" + }, + { + "family": "Petrov", + "given": "Slav" + }, + { + "family": "Fiedel", + "given": "Noah" + } + ], + "id": "27937/FMW5DCWM", + "issued": { + "date-parts": [ + [ + 2022, + 10, + 5 + ] + ] + }, + "note": "arXiv:2204.02311 [cs]", + "number": "arXiv:2204.02311", + "publisher": "arXiv", + "shortTitle": "PaLM", + "system_id": "zotero|27937/FMW5DCWM", + "title": "PaLM: Scaling Language Modeling with Pathways", + "type": "article" + }, + "27937/G5ESJ8NI": { + "URL": "http://arxiv.org/abs/2203.15556", + "abstract": "We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly undertrained, a consequence of the recent focus on scaling language models whilst keeping the amount of training data constant. By training over 400 language models ranging from 70 million to over 16 billion parameters on 5 to 500 billion tokens, we find that for compute-optimal training, the model size and the number of training tokens should be scaled equally: for every doubling of model size the number of training tokens should also be doubled. We test this hypothesis by training a predicted compute-optimal model, Chinchilla, that uses the same compute budget as Gopher but with 70B parameters and 4$\\times$ more more data. Chinchilla uniformly and significantly outperforms Gopher (280B), GPT-3 (175B), Jurassic-1 (178B), and Megatron-Turing NLG (530B) on a large range of downstream evaluation tasks. This also means that Chinchilla uses substantially less compute for fine-tuning and inference, greatly facilitating downstream usage. As a highlight, Chinchilla reaches a state-of-the-art average accuracy of 67.5% on the MMLU benchmark, greater than a 7% improvement over Gopher.", + "accessed": { + "date-parts": [ + [ + 2023, + 4, + 2 + ] + ] + }, + "author": [ + { + "family": "Hoffmann", + "given": "Jordan" + }, + { + "family": "Borgeaud", + "given": "Sebastian" + }, + { + "family": "Mensch", + "given": "Arthur" + }, + { + "family": "Buchatskaya", + "given": "Elena" + }, + { + "family": "Cai", + "given": "Trevor" + }, + { + "family": "Rutherford", + "given": "Eliza" + }, + { + "family": "Casas", + "given": "Diego de Las" + }, + { + "family": "Hendricks", + "given": "Lisa Anne" + }, + { + "family": "Welbl", + "given": "Johannes" + }, + { + "family": "Clark", + "given": "Aidan" + }, + { + "family": "Hennigan", + "given": "Tom" + }, + { + "family": "Noland", + "given": "Eric" + }, + { + "family": "Millican", + "given": "Katie" + }, + { + "family": "Driessche", + "given": "George van den" + }, + { + "family": "Damoc", + "given": "Bogdan" + }, + { + "family": "Guy", + "given": "Aurelia" + }, + { + "family": "Osindero", + "given": "Simon" + }, + { + "family": "Simonyan", + "given": "Karen" + }, + { + "family": "Elsen", + "given": "Erich" + }, + { + "family": "Rae", + "given": "Jack W." + }, + { + "family": "Vinyals", + "given": "Oriol" + }, + { + "family": "Sifre", + "given": "Laurent" + } + ], + "id": "27937/G5ESJ8NI", + "issued": { + "date-parts": [ + [ + 2022, + 3, + 29 + ] + ] + }, + "note": "arXiv:2203.15556 [cs]", + "number": "arXiv:2203.15556", + "publisher": "arXiv", + "system_id": "zotero|27937/G5ESJ8NI", + "title": "Training Compute-Optimal Large Language Models", + "type": "article" + }, + "27937/GHGWH4HI": { + "URL": "https://www.theverge.com/24068716/ai-historians-academia-llm-chatgpt", + "abstract": "It turns out that large language models make surprisingly good research assistants for historians. Can the future of AI help reconstruct the past?", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 29 + ] + ] + }, + "author": [ + { + "family": "Dzieza", + "given": "Josh" + } + ], + "container-title": "The Verge", + "id": "27937/GHGWH4HI", + "issued": { + "date-parts": [ + [ + 2024, + 2, + 15 + ] + ] + }, + "language": "en", + "system_id": "zotero|27937/GHGWH4HI", + "title": "What AI can do for historians", + "type": "webpage" + }, + "27937/GP3PUHUJ": { + "DOI": "10.1093/ahr/rhad363", + "URL": "https://academic.oup.com/ahr/article/128/3/1350/7282258", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 28 + ] + ] + }, + "author": [ + { + "family": "Schmidt", + "given": "Benjamin" + } + ], + "container-title": "The American Historical Review", + "id": "27937/GP3PUHUJ", + "issue": "3", + "issued": { + "date-parts": [ + [ + 2023, + 9, + 26 + ] + ] + }, + "language": "en", + "page": "1350-1353", + "system_id": "zotero|27937/GP3PUHUJ", + "title": "Representation Learning", + "type": "article-journal", + "volume": "128" + }, + "27937/GSIXPJ7P": { + "URL": "https://crfm.stanford.edu/2024/05/01/helm-mmlu.html", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 1 + ] + ] + }, + "author": [ + { + "family": "Mai", + "given": "Yifan" + }, + { + "family": "Liang", + "given": "Percy" + } + ], + "container-title": "Center for Research on Foundation Models, Stanford University", + "genre": "Blog", + "id": "27937/GSIXPJ7P", + "issued": { + "date-parts": [ + [ + 2024, + 5, + 1 + ] + ] + }, + "system_id": "zotero|27937/GSIXPJ7P", + "title": "Massive Multitask Language Understanding (MMLU) on HELM", + "type": "webpage" + }, + "27937/H9BUWE28": { + "DOI": "10.48550/arXiv.2001.08361", + "URL": "http://arxiv.org/abs/2001.08361", + "abstract": "We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern the dependence of overfitting on model/dataset size and the dependence of training speed on model size. These relationships allow us to determine the optimal allocation of a fixed compute budget. Larger models are significantly more sample-efficient, such that optimally compute-efficient training involves training very large models on a relatively modest amount of data and stopping significantly before convergence.", + "accessed": { + "date-parts": [ + [ + 2024, + 9, + 30 + ] + ] + }, + "author": [ + { + "family": "Kaplan", + "given": "Jared" + }, + { + "family": "McCandlish", + "given": "Sam" + }, + { + "family": "Henighan", + "given": "Tom" + }, + { + "family": "Brown", + "given": "Tom B." + }, + { + "family": "Chess", + "given": "Benjamin" + }, + { + "family": "Child", + "given": "Rewon" + }, + { + "family": "Gray", + "given": "Scott" + }, + { + "family": "Radford", + "given": "Alec" + }, + { + "family": "Wu", + "given": "Jeffrey" + }, + { + "family": "Amodei", + "given": "Dario" + } + ], + "id": "27937/H9BUWE28", + "issued": { + "date-parts": [ + [ + 2020, + 1, + 22 + ] + ] + }, + "note": "arXiv:2001.08361 [cs, stat]", + "number": "arXiv:2001.08361", + "publisher": "arXiv", + "system_id": "zotero|27937/H9BUWE28", + "title": "Scaling Laws for Neural Language Models", + "type": "article" + }, + "27937/HGR9QB96": { + "URL": "https://millercenter.org/the-presidency/presidential-speeches", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 16 + ] + ] + }, + "id": "27937/HGR9QB96", + "issued": { + "date-parts": [ + [ + 2016, + 11, + 21 + ] + ] + }, + "language": "en", + "system_id": "zotero|27937/HGR9QB96", + "title": "Presidential Speeches | Miller Center", + "type": "webpage" + }, + "27937/HIPL38QS": { + "URL": "https://www.digitalhumanities.org/dhq/vol/15/4/000574/000574.html", + "abstract": "Named entity recognition is an advantageous technique with an increasing presence in digital humanities. In theory, automatic detection and recovery of named entities can provide new ways of looking up unedited information in edited sources and can allow the parsing of a massive amount of data in a short time for supporting historical hypotheses. In this paper, we detail the implementation of a model for automatic named entity recognition in medieval Latin sources and we test its robustness on different datasets. Different models were trained on a vast dataset of Burgundian diplomatic charters from the 9th to 14th centuries and validated by using general and century ad hoc models tested on short sets of Parisian, English, Italian and Spanish charters. We present the results of cross-validation in each case and we discuss the implications of these results for the history of medieval place-names and personal names.", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 15 + ] + ] + }, + "author": [ + { + "family": "Chastang", + "given": "Pierre" + }, + { + "family": "Aguilar", + "given": "Sergio Torres" + }, + { + "family": "Tannier", + "given": "Xavier" + } + ], + "container-title": "Digital Humanities Quarterly", + "id": "27937/HIPL38QS", + "issue": "4", + "issued": { + "date-parts": [ + [ + 2021 + ] + ] + }, + "system_id": "zotero|27937/HIPL38QS", + "title": "A Named Entity Recognition Model for Medieval Latin Charters", + "type": "article-journal", + "volume": "15" + }, + "27937/I2BKP7MN": { + "URL": "https://www.css.cnrs.fr/using-whisper-to-transcribe-oral-interviews/", + "abstract": "Site web de l'axe sciences sociales computationnelles du CREST-CNRS. Cours et tutoriels pour l'analyse des données numériques en sciences sociales.", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 25 + ] + ] + }, + "author": [ + { + "family": "Schultz", + "given": "Emilien" + } + ], + "id": "27937/I2BKP7MN", + "issued": { + "date-parts": [ + [ + 2024, + 2, + 12 + ] + ] + }, + "language": "en-US", + "system_id": "zotero|27937/I2BKP7MN", + "title": "[Tutorial] Using Whisper to Transcribe Oral Interviews – CSS @ IPP", + "type": "post-weblog" + }, + "27937/I363EKXY": { + "DOI": "10.48550/arXiv.2402.04559", + "URL": "http://arxiv.org/abs/2402.04559", + "abstract": "Large Language Model (LLM) agents have been increasingly adopted as simulation tools to model humans in applications such as social science. However, one fundamental question remains: can LLM agents really simulate human behaviors? In this paper, we focus on one of the most critical behaviors in human interactions, trust, and aim to investigate whether or not LLM agents can simulate human trust behaviors. We first find that LLM agents generally exhibit trust behaviors, referred to as agent trust, under the framework of Trust Games, which are widely recognized in behavioral economics. Then, we discover that LLM agents can have high behavioral alignment with humans regarding trust behaviors, particularly for GPT-4, indicating the feasibility to simulate human trust behaviors with LLM agents. In addition, we probe into the biases in agent trust and the differences in agent trust towards agents and humans. We also explore the intrinsic properties of agent trust under conditions including advanced reasoning strategies and external manipulations. We further offer important implications of our discoveries for various scenarios where trust is paramount. Our study provides new insights into the behaviors of LLM agents and the fundamental analogy between LLMs and humans.", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 23 + ] + ] + }, + "author": [ + { + "family": "Xie", + "given": "Chengxing" + }, + { + "family": "Chen", + "given": "Canyu" + }, + { + "family": "Jia", + "given": "Feiran" + }, + { + "family": "Ye", + "given": "Ziyu" + }, + { + "family": "Shu", + "given": "Kai" + }, + { + "family": "Bibi", + "given": "Adel" + }, + { + "family": "Hu", + "given": "Ziniu" + }, + { + "family": "Torr", + "given": "Philip" + }, + { + "family": "Ghanem", + "given": "Bernard" + }, + { + "family": "Li", + "given": "Guohao" + } + ], + "id": "27937/I363EKXY", + "issued": { + "date-parts": [ + [ + 2024, + 3, + 10 + ] + ] + }, + "note": "arXiv:2402.04559", + "number": "arXiv:2402.04559", + "publisher": "arXiv", + "system_id": "zotero|27937/I363EKXY", + "title": "Can Large Language Model Agents Simulate Human Trust Behaviors?", + "type": "article" + }, + "27937/IEQ8GAVU": { + "ISBN": "9781479837243", + "abstract": "A revealing look at how negative biases against women of color are embedded in search engine results and algorithms Run a Google search for “black girls”―what will you find? “Big Booty” and other sexually explicit terms are likely to come up as top search terms. But, if you type in “white girls,” the results are radically different. The suggested porn sites and un-moderated discussions about “why black women are so sassy” or “why black women are so angry” presents a disturbing portrait of black womanhood in modern society.In Algorithms of Oppression, Safiya Umoja Noble challenges the idea that search engines like Google offer an equal playing field for all forms of ideas, identities, and activities. Data discrimination is a real social problem; Noble argues that the combination of private interests in promoting certain sites, along with the monopoly status of a relatively small number of Internet search engines, leads to a biased set of search algorithms that privilege whiteness and discriminate against people of color, specifically women of color.Through an analysis of textual and media searches as well as extensive research on paid online advertising, Noble exposes a culture of racism and sexism in the way discoverability is created online. As search engines and their related companies grow in importance―operating as a source for email, a major vehicle for primary and secondary school learning, and beyond―understanding and reversing these disquieting trends and discriminatory practices is of utmost importance.An original, surprising and, at times, disturbing account of bias on the internet, Algorithms of Oppression contributes to our understanding of how racism is created, maintained, and disseminated in the 21st century.", + "author": [ + { + "family": "Noble", + "given": "Safiya Umoja" + } + ], + "edition": "Illustrated edition", + "event-place": "New York", + "id": "27937/IEQ8GAVU", + "issued": { + "date-parts": [ + [ + 2018, + 2, + 20 + ] + ] + }, + "language": "English", + "number-of-pages": "248", + "publisher": "NYU Press", + "publisher-place": "New York", + "shortTitle": "Algorithms of Oppression", + "system_id": "zotero|27937/IEQ8GAVU", + "title": "Algorithms of Oppression: How Search Engines Reinforce Racism", + "type": "book" + }, + "27937/JUTZSXVB": { + "URL": "https://resobscura.substack.com/p/simulating-history-with-chatgpt", + "abstract": "The Case for LLMs as Hallucination Engines", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 23 + ] + ] + }, + "author": [ + { + "family": "Breen", + "given": "Benjamin" + } + ], + "container-title": "Res Obscura", + "genre": "Substack newsletter", + "id": "27937/JUTZSXVB", + "issued": { + "date-parts": [ + [ + 2023, + 9, + 12 + ] + ] + }, + "system_id": "zotero|27937/JUTZSXVB", + "title": "Simulating History with ChatGPT", + "type": "post-weblog" + }, + "27937/JV9GGCQA": { + "URL": "https://huggingface.co/blog/Pclanglais/post-ocr-correction", + "abstract": "A Blog post by Pierre-Carl Langlais on Hugging Face", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 14 + ] + ] + }, + "author": [ + { + "family": "Langlais", + "given": "Pierre-Carl" + } + ], + "id": "27937/JV9GGCQA", + "shortTitle": "Post-OCR-Correction", + "system_id": "zotero|27937/JV9GGCQA", + "title": "Post-OCR-Correction: 1 billion words dataset of automated OCR correction by LLM", + "type": "webpage" + }, + "27937/KKDPZJYW": { + "DOI": "10.21437/Interspeech.2023-872", + "URL": "https://www.isca-archive.org/interspeech_2023/lehecka23_interspeech.html", + "abstract": "This paper is a step forward in our effort to make vast oral history archives more accessible to the public and researchers by breaking down the decoding barriers between the knowledge encoded in the spoken testimonies and users who want to search for the information of their interest. We present new Transformer-based monolingual models suitable for speech recognition of oral history archives in English, German, and Czech. Our experiments show that although the all-purpose speech recognition systems have recently made tremendous progress, the transcription of oral history archives is still a challenging task for them; our tailored models significantly outperformed larger public multilingual models and scored new stateof-the-art results on all tested datasets. Due to the 2-phase finetuning process, our models are robust and can be used for oral history archives of various domains. We publicly release our models within a public speech recognition service.", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 10 + ] + ] + }, + "author": [ + { + "family": "Lehečka", + "given": "Jan" + }, + { + "family": "Švec", + "given": "Jan" + }, + { + "family": "Psutka", + "given": "Josef V." + }, + { + "family": "Ircing", + "given": "Pavel" + } + ], + "container-title": "INTERSPEECH 2023", + "event": "INTERSPEECH 2023", + "id": "27937/KKDPZJYW", + "issued": { + "date-parts": [ + [ + 2023, + 8, + 20 + ] + ] + }, + "language": "en", + "page": "201-205", + "publisher": "ISCA", + "system_id": "zotero|27937/KKDPZJYW", + "title": "Transformer-based Speech Recognition Models for Oral History Archives in English, German, and Czech", + "type": "paper-conference" + }, + "27937/KNEK45E4": { + "URL": "http://arxiv.org/abs/2005.14165", + "abstract": "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general.", + "accessed": { + "date-parts": [ + [ + 2023, + 3, + 27 + ] + ] + }, + "author": [ + { + "family": "Brown", + "given": "Tom B." + }, + { + "family": "Mann", + "given": "Benjamin" + }, + { + "family": "Ryder", + "given": "Nick" + }, + { + "family": "Subbiah", + "given": "Melanie" + }, + { + "family": "Kaplan", + "given": "Jared" + }, + { + "family": "Dhariwal", + "given": "Prafulla" + }, + { + "family": "Neelakantan", + "given": "Arvind" + }, + { + "family": "Shyam", + "given": "Pranav" + }, + { + "family": "Sastry", + "given": "Girish" + }, + { + "family": "Askell", + "given": "Amanda" + }, + { + "family": "Agarwal", + "given": "Sandhini" + }, + { + "family": "Herbert-Voss", + "given": "Ariel" + }, + { + "family": "Krueger", + "given": "Gretchen" + }, + { + "family": "Henighan", + "given": "Tom" + }, + { + "family": "Child", + "given": "Rewon" + }, + { + "family": "Ramesh", + "given": "Aditya" + }, + { + "family": "Ziegler", + "given": "Daniel M." + }, + { + "family": "Wu", + "given": "Jeffrey" + }, + { + "family": "Winter", + "given": "Clemens" + }, + { + "family": "Hesse", + "given": "Christopher" + }, + { + "family": "Chen", + "given": "Mark" + }, + { + "family": "Sigler", + "given": "Eric" + }, + { + "family": "Litwin", + "given": "Mateusz" + }, + { + "family": "Gray", + "given": "Scott" + }, + { + "family": "Chess", + "given": "Benjamin" + }, + { + "family": "Clark", + "given": "Jack" + }, + { + "family": "Berner", + "given": "Christopher" + }, + { + "family": "McCandlish", + "given": "Sam" + }, + { + "family": "Radford", + "given": "Alec" + }, + { + "family": "Sutskever", + "given": "Ilya" + }, + { + "family": "Amodei", + "given": "Dario" + } + ], + "id": "27937/KNEK45E4", + "issued": { + "date-parts": [ + [ + 2020, + 7, + 22 + ] + ] + }, + "note": "arXiv:2005.14165 [cs]", + "number": "arXiv:2005.14165", + "publisher": "arXiv", + "system_id": "zotero|27937/KNEK45E4", + "title": "Language Models are Few-Shot Learners", + "type": "article" + }, + "27937/KPPP2BAQ": { + "URL": "https://www.nbcnews.com/tech/tech-news/chatgpt-gpt-chat-bot-ai-hitler-historical-figures-open-rcna66531", + "abstract": "The Historical Figures app is available in Apple's App Store and lets you chat with notable people from history re-animated by artificial intelligence.", + "accessed": { + "date-parts": [ + [ + 2023, + 4, + 2 + ] + ] + }, + "container-title": "NBC News", + "id": "27937/KPPP2BAQ", + "issued": { + "date-parts": [ + [ + 2023, + 1, + 20 + ] + ] + }, + "language": "en", + "system_id": "zotero|27937/KPPP2BAQ", + "title": "Chatbot that lets you talk to Jesus and Hitler is latest AI controversy", + "type": "webpage" + }, + "27937/L2ILKERU": { + "ISBN": "9781783266371", + "abstract": "The Digital Humanities have arrived at a moment when digital Big Data is becoming more readily available, opening exciting new avenues of inquiry but also new challenges. This pioneering book describes and demonstrates the ways these data can be explored to construct cultural heritage knowledge, for research and in teaching and learning. It helps humanities scholars to grasp Big Data in order to do their work, whether that means understanding the underlying algorithms at work in search engines, or designing and using their own tools to process large amounts of information.Demonstrating what digital tools have to offer and also what 'digital' does to how we understand the past, the authors introduce the many different tools and developing approaches in Big Data for historical and humanistic scholarship, show how to use them, what to be wary of, and discuss the kinds of questions and new perspectives this new macroscopic perspective opens up. Authored 'live' online with ongoing feedback from the wider digital history community, Exploring Big Historical Data breaks new ground and sets the direction for the conversation into the future. It represents the current state-of-the-art thinking in the field and exemplifies the way that digital work can enhance public engagement in the humanities.Exploring Big Historical Data should be the go-to resource for undergraduate and graduate students confronted by a vast corpus of data, and researchers encountering these methods for the first time. It will also offer a helping hand to the interested individual seeking to make sense of genealogical data or digitized newspapers, and even the local historical society who are trying to see the value in digitizing their holdings.", + "author": [ + { + "family": "Graham", + "given": "Shawn" + }, + { + "family": "Milligan", + "given": "Ian" + }, + { + "family": "Weingart", + "given": "Scott" + } + ], + "edition": "Reprint edition", + "event-place": "London", + "id": "27937/L2ILKERU", + "issued": { + "date-parts": [ + [ + 2015, + 11, + 16 + ] + ] + }, + "language": "English", + "number-of-pages": "306", + "publisher": "Icp", + "publisher-place": "London", + "shortTitle": "Exploring Big Historical Data", + "system_id": "zotero|27937/L2ILKERU", + "title": "Exploring Big Historical Data: The Historian's Macroscope", + "type": "book" + }, + "27937/LC63DETW": { + "DOI": "10.1038/s41467-024-45563-x", + "URL": "https://www.nature.com/articles/s41467-024-45563-x", + "abstract": "Extracting structured knowledge from scientific text remains a challenging task for machine learning models. Here, we present a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large language models (GPT-3, Llama-2) can be fine-tuned to extract useful records of complex scientific knowledge. We test three representative tasks in materials chemistry: linking dopants and host materials, cataloging metal-organic frameworks, and general composition/phase/morphology/application information extraction. Records are extracted from single sentences or entire paragraphs, and the output can be returned as simple English sentences or a more structured format such as a list of JSON objects. This approach represents a simple, accessible, and highly flexible route to obtaining large databases of structured specialized scientific knowledge extracted from research papers.", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 15 + ] + ] + }, + "author": [ + { + "family": "Dagdelen", + "given": "John" + }, + { + "family": "Dunn", + "given": "Alexander" + }, + { + "family": "Lee", + "given": "Sanghoon" + }, + { + "family": "Walker", + "given": "Nicholas" + }, + { + "family": "Rosen", + "given": "Andrew S." + }, + { + "family": "Ceder", + "given": "Gerbrand" + }, + { + "family": "Persson", + "given": "Kristin A." + }, + { + "family": "Jain", + "given": "Anubhav" + } + ], + "container-title": "Nature Communications", + "id": "27937/LC63DETW", + "issue": "1", + "issued": { + "date-parts": [ + [ + 2024, + 2, + 15 + ] + ] + }, + "journalAbbreviation": "Nat Commun", + "language": "en", + "note": "Publisher: Nature Publishing Group", + "page": "1418", + "system_id": "zotero|27937/LC63DETW", + "title": "Structured information extraction from scientific text with large language models", + "type": "article-journal", + "volume": "15" + }, + "27937/MHRIEHH8": { + "URL": "http://arxiv.org/abs/2009.11462", + "abstract": "Pretrained neural language models (LMs) are prone to generating racist, sexist, or otherwise toxic language which hinders their safe deployment. We investigate the extent to which pretrained LMs can be prompted to generate toxic language, and the effectiveness of controllable text generation algorithms at preventing such toxic degeneration. We create and release RealToxicityPrompts, a dataset of 100K naturally occurring, sentence-level prompts derived from a large corpus of English web text, paired with toxicity scores from a widely-used toxicity classifier. Using RealToxicityPrompts, we find that pretrained LMs can degenerate into toxic text even from seemingly innocuous prompts. We empirically assess several controllable generation methods, and find that while data- or compute-intensive methods (e.g., adaptive pretraining on non-toxic data) are more effective at steering away from toxicity than simpler solutions (e.g., banning \"bad\" words), no current method is failsafe against neural toxic degeneration. To pinpoint the potential cause of such persistent toxic degeneration, we analyze two web text corpora used to pretrain several LMs (including GPT-2; Radford et. al, 2019), and find a significant amount of offensive, factually unreliable, and otherwise toxic content. Our work provides a test bed for evaluating toxic generations by LMs and stresses the need for better data selection processes for pretraining.", + "accessed": { + "date-parts": [ + [ + 2023, + 3, + 28 + ] + ] + }, + "author": [ + { + "family": "Gehman", + "given": "Samuel" + }, + { + "family": "Gururangan", + "given": "Suchin" + }, + { + "family": "Sap", + "given": "Maarten" + }, + { + "family": "Choi", + "given": "Yejin" + }, + { + "family": "Smith", + "given": "Noah A." + } + ], + "id": "27937/MHRIEHH8", + "issued": { + "date-parts": [ + [ + 2020, + 9, + 25 + ] + ] + }, + "note": "arXiv:2009.11462 [cs]", + "number": "arXiv:2009.11462", + "publisher": "arXiv", + "shortTitle": "RealToxicityPrompts", + "system_id": "zotero|27937/MHRIEHH8", + "title": "RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models", + "type": "article" + }, + "27937/MVDFMR8K": { + "URL": "https://dl.acm.org/doi/10.1145/3442188.3445922", + "accessed": { + "date-parts": [ + [ + 2023, + 3, + 27 + ] + ] + }, + "author": [ + { + "family": "Bender", + "given": "Emily" + }, + { + "family": "Gebru", + "given": "Timnit" + }, + { + "family": "McMillan-Major", + "given": "Angelina" + }, + { + "family": "Mitchell", + "given": "Margaret" + } + ], + "id": "27937/MVDFMR8K", + "system_id": "zotero|27937/MVDFMR8K", + "title": "On the Dangers of Stochastic Parrots | Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency", + "type": "webpage" + }, + "27937/MYBFNHF8": { + "URL": "https://lil.law.harvard.edu/blog/2024/02/12/warc-gpt-an-open-source-tool-for-exploring-web-archives-with-ai/", + "abstract": "Today we’re releasing WARC-GPT: an open-source, highly-customizable Retrieval Augmented Generation tool the web archiving community can use to explore the in...", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 16 + ] + ] + }, + "author": [ + { + "family": "Cargnelutti", + "given": "Matteo" + }, + { + "family": "Mukk", + "given": "Kristi" + }, + { + "family": "Stanton", + "given": "Clare" + } + ], + "container-title": "Library Innovation Lab Blog, Harvard Law Library", + "id": "27937/MYBFNHF8", + "issued": { + "date-parts": [ + [ + 2024, + 2, + 12 + ] + ] + }, + "language": "en", + "shortTitle": "WARC-GPT", + "system_id": "zotero|27937/MYBFNHF8", + "title": "WARC-GPT: An Open-Source Tool for Exploring Web Archives Using AI | Library Innovation Lab", + "type": "webpage" + }, + "27937/MYFQUX4C": { + "URL": "https://www.rit.edu/news/artificial-intelligence-aids-cultural-heritage-researchers-documenting-and-teaching-oral", + "abstract": "The application of artificial intelligence (AI) continues to expand as more people experiment with the technology. Scholars in RIT’s College of Liberal Arts, the RIT Archives, and the Research Computing services are exploring how AI can aid scholars working with oral histories.", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 10 + ] + ] + }, + "author": [ + { + "family": "Rochester Institute of Technology", + "given": "" + } + ], + "container-title": "Artificial intelligence aids cultural heritage researchers documenting and teaching oral histories", + "id": "27937/MYFQUX4C", + "language": "en", + "system_id": "zotero|27937/MYFQUX4C", + "title": "Artificial intelligence aids cultural heritage researchers documenting and teaching oral histories", + "type": "webpage" + }, + "27937/NYNDVYMM": { + "URL": "https://spectrum.ieee.org/open-ais-powerful-text-generating-tool-is-ready-for-business", + "abstract": "OpenAI's language model, GPT-3, is being used in commercial products and services, but experts worry about embedded bias and toxic language generation", + "accessed": { + "date-parts": [ + [ + 2023, + 3, + 28 + ] + ] + }, + "author": [ + { + "family": "Strickland", + "given": "Eliza" + } + ], + "container-title": "OpenAI's GPT-3 Speaks! (Kindly Disregard Toxic Language) - IEEE Spectrum", + "id": "27937/NYNDVYMM", + "issued": { + "date-parts": [ + [ + 2021, + 2, + 1 + ] + ] + }, + "language": "en", + "system_id": "zotero|27937/NYNDVYMM", + "title": "OpenAI's GPT-3 Speaks! (Kindly Disregard Toxic Language) - IEEE Spectrum", + "type": "webpage" + }, + "27937/P2KVKTMZ": { + "URL": "https://nicolay-honestabes-info.streamlit.app/", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 16 + ] + ] + }, + "author": [ + { + "family": "Hutchinson", + "given": "Daniel" + } + ], + "container-title": "Honest Abe’s Information Emporium.", + "id": "27937/P2KVKTMZ", + "issued": { + "date-parts": [ + [ + 2023 + ] + ] + }, + "system_id": "zotero|27937/P2KVKTMZ", + "title": "Nicolay: Exploring the Speeches of Abraham Lincoln with AI", + "type": "webpage" + }, + "27937/P3ZKA48D": { + "URL": "https://www.theatlantic.com/technology/archive/2022/12/chatgpt-ai-writing-college-student-essays/672371/", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 25 + ] + ] + }, + "author": [ + { + "family": "Marche", + "given": "Stephen" + } + ], + "container-title": "The Atlantic", + "id": "27937/P3ZKA48D", + "issued": { + "date-parts": [ + [ + "2022", + 12, + 6 + ] + ] + }, + "system_id": "zotero|27937/P3ZKA48D", + "title": "Will ChatGPT Kill the Student Essay? - The Atlantic", + "type": "webpage" + }, + "27937/QD3X7XMD": { + "URL": "http://arxiv.org/abs/2303.10130", + "abstract": "We investigate the potential implications of large language models (LLMs), such as Generative Pre-trained Transformers (GPTs), on the U.S. labor market, focusing on the increased capabilities arising from LLM-powered software compared to LLMs on their own. Using a new rubric, we assess occupations based on their alignment with LLM capabilities, integrating both human expertise and GPT-4 classifications. Our findings reveal that around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while approximately 19% of workers may see at least 50% of their tasks impacted. We do not make predictions about the development or adoption timeline of such LLMs. The projected effects span all wage levels, with higher-income jobs potentially facing greater exposure to LLM capabilities and LLM-powered software. Significantly, these impacts are not restricted to industries with higher recent productivity growth. Our analysis suggests that, with access to an LLM, about 15% of all worker tasks in the US could be completed significantly faster at the same level of quality. When incorporating software and tooling built on top of LLMs, this share increases to between 47 and 56% of all tasks. This finding implies that LLM-powered software will have a substantial effect on scaling the economic impacts of the underlying models. We conclude that LLMs such as GPTs exhibit traits of general-purpose technologies, indicating that they could have considerable economic, social, and policy implications.", + "accessed": { + "date-parts": [ + [ + 2023, + 3, + 27 + ] + ] + }, + "author": [ + { + "family": "Eloundou", + "given": "Tyna" + }, + { + "family": "Manning", + "given": "Sam" + }, + { + "family": "Mishkin", + "given": "Pamela" + }, + { + "family": "Rock", + "given": "Daniel" + } + ], + "id": "27937/QD3X7XMD", + "issued": { + "date-parts": [ + [ + 2023, + 3, + 23 + ] + ] + }, + "note": "arXiv:2303.10130 [cs, econ, q-fin]", + "number": "arXiv:2303.10130", + "publisher": "arXiv", + "shortTitle": "GPTs are GPTs", + "system_id": "zotero|27937/QD3X7XMD", + "title": "GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models", + "type": "article" + }, + "27937/RJTNQXZP": { + "DOI": "10.48550/arXiv.2402.14207", + "URL": "http://arxiv.org/abs/2402.14207", + "abstract": "We study how to apply large language models to write grounded and organized long-form articles from scratch, with comparable breadth and depth to Wikipedia pages. This underexplored problem poses new challenges at the pre-writing stage, including how to research the topic and prepare an outline prior to writing. We propose STORM, a writing system for the Synthesis of Topic Outlines through Retrieval and Multi-perspective Question Asking. STORM models the pre-writing stage by (1) discovering diverse perspectives in researching the given topic, (2) simulating conversations where writers carrying different perspectives pose questions to a topic expert grounded on trusted Internet sources, (3) curating the collected information to create an outline. For evaluation, we curate FreshWiki, a dataset of recent high-quality Wikipedia articles, and formulate outline assessments to evaluate the pre-writing stage. We further gather feedback from experienced Wikipedia editors. Compared to articles generated by an outline-driven retrieval-augmented baseline, more of STORM's articles are deemed to be organized (by a 25% absolute increase) and broad in coverage (by 10%). The expert feedback also helps identify new challenges for generating grounded long articles, such as source bias transfer and over-association of unrelated facts.", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 16 + ] + ] + }, + "author": [ + { + "family": "Shao", + "given": "Yijia" + }, + { + "family": "Jiang", + "given": "Yucheng" + }, + { + "family": "Kanell", + "given": "Theodore A." + }, + { + "family": "Xu", + "given": "Peter" + }, + { + "family": "Khattab", + "given": "Omar" + }, + { + "family": "Lam", + "given": "Monica S." + } + ], + "id": "27937/RJTNQXZP", + "issued": { + "date-parts": [ + [ + 2024, + 4, + 8 + ] + ] + }, + "note": "arXiv:2402.14207", + "number": "arXiv:2402.14207", + "publisher": "arXiv", + "system_id": "zotero|27937/RJTNQXZP", + "title": "Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models", + "type": "article" + }, + "27937/S3ADX5DD": { + "ISBN": "9780190067397", + "URL": "https://doi.org/10.1093/oxfordhb/9780190067397.013.16", + "abstract": "This chapter discusses the role of race and gender in artificial intelligence (AI). The rapid permeation of AI into society has not been accompanied by a thorough investigation of the sociopolitical issues that cause certain groups of people to be harmed rather than advantaged by it. For instance, recent studies have shown that commercial automated facial analysis systems have much higher error rates for dark-skinned women, while having minimal errors on light-skinned men. Moreover, a 2016 ProPublica investigation uncovered that machine learning–based tools that assess crime recidivism rates in the United States are biased against African Americans. Other studies show that natural language–processing tools trained on news articles exhibit societal biases. While many technical solutions have been proposed to alleviate bias in machine learning systems, a holistic and multifaceted approach must be taken. This includes standardization bodies determining what types of systems can be used in which scenarios, making sure that automated decision tools are created by people from diverse backgrounds, and understanding the historical and political factors that disadvantage certain groups who are subjected to these tools.", + "accessed": { + "date-parts": [ + [ + 2023, + 3, + 28 + ] + ] + }, + "author": [ + { + "family": "Gebru", + "given": "Timnit" + } + ], + "container-title": "The Oxford Handbook of Ethics of AI", + "editor": [ + { + "family": "Dubber", + "given": "Markus D." + }, + { + "family": "Pasquale", + "given": "Frank" + }, + { + "family": "Das", + "given": "Sunit" + } + ], + "id": "27937/S3ADX5DD", + "issued": { + "date-parts": [ + [ + 2020, + 7, + 9 + ] + ] + }, + "note": "DOI: 10.1093/oxfordhb/9780190067397.013.16", + "page": "0", + "publisher": "Oxford University Press", + "system_id": "zotero|27937/S3ADX5DD", + "title": "Race and Gender", + "type": "chapter" + }, + "27937/TGPDB8WX": { + "DOI": "10.48550/arXiv.1706.03741", + "URL": "http://arxiv.org/abs/1706.03741", + "abstract": "For sophisticated reinforcement learning (RL) systems to interact usefully with real-world environments, we need to communicate complex goals to these systems. In this work, we explore goals defined in terms of (non-expert) human preferences between pairs of trajectory segments. We show that this approach can effectively solve complex RL tasks without access to the reward function, including Atari games and simulated robot locomotion, while providing feedback on less than one percent of our agent's interactions with the environment. This reduces the cost of human oversight far enough that it can be practically applied to state-of-the-art RL systems. To demonstrate the flexibility of our approach, we show that we can successfully train complex novel behaviors with about an hour of human time. These behaviors and environments are considerably more complex than any that have been previously learned from human feedback.", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 1 + ] + ] + }, + "author": [ + { + "family": "Christiano", + "given": "Paul" + }, + { + "family": "Leike", + "given": "Jan" + }, + { + "family": "Brown", + "given": "Tom B." + }, + { + "family": "Martic", + "given": "Miljan" + }, + { + "family": "Legg", + "given": "Shane" + }, + { + "family": "Amodei", + "given": "Dario" + } + ], + "id": "27937/TGPDB8WX", + "issued": { + "date-parts": [ + [ + 2023, + 2, + 17 + ] + ] + }, + "note": "arXiv:1706.03741 [cs, stat]", + "number": "arXiv:1706.03741", + "publisher": "arXiv", + "system_id": "zotero|27937/TGPDB8WX", + "title": "Deep reinforcement learning from human preferences", + "type": "article" + }, + "27937/TIAJYHF6": { + "DOI": "10.1108/JD-07-2018-0114", + "URL": "https://doi.org/10.1108/JD-07-2018-0114", + "abstract": "Purpose An overview of the current use of handwritten text recognition (HTR) on archival manuscript material, as provided by the EU H2020 funded Transkribus platform. It explains HTR, demonstrates Transkribus, gives examples of use cases, highlights the affect HTR may have on scholarship, and evidences this turning point of the advanced use of digitised heritage content. The paper aims to discuss these issues. Design/methodology/approach This paper adopts a case study approach, using the development and delivery of the one openly available HTR platform for manuscript material. Findings Transkribus has demonstrated that HTR is now a useable technology that can be employed in conjunction with mass digitisation to generate accurate transcripts of archival material. Use cases are demonstrated, and a cooperative model is suggested as a way to ensure sustainability and scaling of the platform. However, funding and resourcing issues are identified. Research limitations/implications The paper presents results from projects: further user studies could be undertaken involving interviews, surveys, etc. Practical implications Only HTR provided via Transkribus is covered: however, this is the only publicly available platform for HTR on individual collections of historical documents at time of writing and it represents the current state-of-the-art in this field. Social implications The increased access to information contained within historical texts has the potential to be transformational for both institutions and individuals. Originality/value This is the first published overview of how HTR is used by a wide archival studies community, reporting and showcasing current application of handwriting technology in the cultural heritage sector.", + "accessed": { + "date-parts": [ + [ + 2023, + 3, + 27 + ] + ] + }, + "author": [ + { + "family": "Muehlberger", + "given": "Guenter" + }, + { + "family": "Seaward", + "given": "Louise" + }, + { + "family": "Terras", + "given": "Melissa" + }, + { + "family": "Ares Oliveira", + "given": "Sofia" + }, + { + "family": "Bosch", + "given": "Vicente" + }, + { + "family": "Bryan", + "given": "Maximilian" + }, + { + "family": "Colutto", + "given": "Sebastian" + }, + { + "family": "Déjean", + "given": "Hervé" + }, + { + "family": "Diem", + "given": "Markus" + }, + { + "family": "Fiel", + "given": "Stefan" + }, + { + "family": "Gatos", + "given": "Basilis" + }, + { + "family": "Greinoecker", + "given": "Albert" + }, + { + "family": "Grüning", + "given": "Tobias" + }, + { + "family": "Hackl", + "given": "Guenter" + }, + { + "family": "Haukkovaara", + "given": "Vili" + }, + { + "family": "Heyer", + "given": "Gerhard" + }, + { + "family": "Hirvonen", + "given": "Lauri" + }, + { + "family": "Hodel", + "given": "Tobias" + }, + { + "family": "Jokinen", + "given": "Matti" + }, + { + "family": "Kahle", + "given": "Philip" + }, + { + "family": "Kallio", + "given": "Mario" + }, + { + "family": "Kaplan", + "given": "Frederic" + }, + { + "family": "Kleber", + "given": "Florian" + }, + { + "family": "Labahn", + "given": "Roger" + }, + { + "family": "Lang", + "given": "Eva Maria" + }, + { + "family": "Laube", + "given": "Sören" + }, + { + "family": "Leifert", + "given": "Gundram" + }, + { + "family": "Louloudis", + "given": "Georgios" + }, + { + "family": "McNicholl", + "given": "Rory" + }, + { + "family": "Meunier", + "given": "Jean-Luc" + }, + { + "family": "Michael", + "given": "Johannes" + }, + { + "family": "Mühlbauer", + "given": "Elena" + }, + { + "family": "Philipp", + "given": "Nathanael" + }, + { + "family": "Pratikakis", + "given": "Ioannis" + }, + { + "family": "Puigcerver Pérez", + "given": "Joan" + }, + { + "family": "Putz", + "given": "Hannelore" + }, + { + "family": "Retsinas", + "given": "George" + }, + { + "family": "Romero", + "given": "Verónica" + }, + { + "family": "Sablatnig", + "given": "Robert" + }, + { + "family": "Sánchez", + "given": "Joan Andreu" + }, + { + "family": "Schofield", + "given": "Philip" + }, + { + "family": "Sfikas", + "given": "Giorgos" + }, + { + "family": "Sieber", + "given": "Christian" + }, + { + "family": "Stamatopoulos", + "given": "Nikolaos" + }, + { + "family": "Strauß", + "given": "Tobias" + }, + { + "family": "Terbul", + "given": "Tamara" + }, + { + "family": "Toselli", + "given": "Alejandro Héctor" + }, + { + "family": "Ulreich", + "given": "Berthold" + }, + { + "family": "Villegas", + "given": "Mauricio" + }, + { + "family": "Vidal", + "given": "Enrique" + }, + { + "family": "Walcher", + "given": "Johanna" + }, + { + "family": "Weidemann", + "given": "Max" + }, + { + "family": "Wurster", + "given": "Herbert" + }, + { + "family": "Zagoris", + "given": "Konstantinos" + } + ], + "container-title": "Journal of Documentation", + "id": "27937/TIAJYHF6", + "issue": "5", + "issued": { + "date-parts": [ + [ + 2019, + 1, + 1 + ] + ] + }, + "page": "954-976", + "shortTitle": "Transforming scholarship in the archives through handwritten text recognition", + "system_id": "zotero|27937/TIAJYHF6", + "title": "Transforming scholarship in the archives through handwritten text recognition: Transkribus as a case study", + "type": "article-journal", + "volume": "75" + }, + "27937/TPGPSRAI": { + "URL": "https://medium.com/@emilymenonbender/on-nyt-magazine-on-ai-resist-the-urge-to-be-impressed-3d92fd9a0edd", + "abstract": "[Now available as an “audiopaper” on my soundcloud. (Please excuse occasional noise from airplanes overhead + my inconsistency about…", + "accessed": { + "date-parts": [ + [ + 2023, + 3, + 28 + ] + ] + }, + "author": [ + { + "family": "Bender", + "given": "Emily M." + } + ], + "container-title": "Medium", + "id": "27937/TPGPSRAI", + "issued": { + "date-parts": [ + [ + 2022, + 5, + 2 + ] + ] + }, + "language": "en", + "shortTitle": "On NYT Magazine on AI", + "system_id": "zotero|27937/TPGPSRAI", + "title": "On NYT Magazine on AI: Resist the Urge to be Impressed", + "type": "post-weblog" + }, + "27937/U534FF7L": { + "URL": "http://arxiv.org/abs/2303.08774", + "abstract": "We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behavior. A core component of this project was developing infrastructure and optimization methods that behave predictably across a wide range of scales. This allowed us to accurately predict some aspects of GPT-4's performance based on models trained with no more than 1/1,000th the compute of GPT-4.", + "accessed": { + "date-parts": [ + [ + 2023, + 3, + 28 + ] + ] + }, + "author": [ + { + "family": "OpenAI", + "given": "" + } + ], + "id": "27937/U534FF7L", + "issued": { + "date-parts": [ + [ + 2023, + 3, + 27 + ] + ] + }, + "note": "arXiv:2303.08774 [cs]", + "number": "arXiv:2303.08774", + "publisher": "arXiv", + "system_id": "zotero|27937/U534FF7L", + "title": "GPT-4 Technical Report", + "type": "article" + }, + "27937/UHZYQM3W": { + "URL": "https://www.newyorker.com/tech/annals-of-technology/whispers-of-ais-modular-future", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 10 + ] + ] + }, + "author": [ + { + "family": "Somers", + "given": "James" + } + ], + "id": "27937/UHZYQM3W", + "issued": { + "date-parts": [ + [ + 2023, + 2, + 1 + ] + ] + }, + "system_id": "zotero|27937/UHZYQM3W", + "title": "Whispers of A.I.’s Modular Future | The New Yorker", + "type": "webpage" + }, + "27937/USYR9HC8": { + "URL": "https://aiandwriting.hcommons.org/2024/10/08/using-the-student-guide-to-ai-literacy/", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 28 + ] + ] + }, + "author": [ + { + "family": "MLA-CCCC Joint Task Force on Writing and AI", + "given": "" + } + ], + "container-title": "MLA-CCCC Joint Task Force on Writing and AI", + "id": "27937/USYR9HC8", + "issued": { + "date-parts": [ + [ + "2004", + 10, + 8 + ] + ] + }, + "language": "en-US", + "system_id": "zotero|27937/USYR9HC8", + "title": "Using the Student Guide to AI Literacy – MLA-CCCC Joint Task Force on Writing and AI", + "type": "post-weblog" + }, + "27937/UYVGUT4C": { + "URL": "http://arxiv.org/abs/2103.00020", + "abstract": "State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP.", + "accessed": { + "date-parts": [ + [ + 2023, + 3, + 28 + ] + ] + }, + "author": [ + { + "family": "Radford", + "given": "Alec" + }, + { + "family": "Kim", + "given": "Jong Wook" + }, + { + "family": "Hallacy", + "given": "Chris" + }, + { + "family": "Ramesh", + "given": "Aditya" + }, + { + "family": "Goh", + "given": "Gabriel" + }, + { + "family": "Agarwal", + "given": "Sandhini" + }, + { + "family": "Sastry", + "given": "Girish" + }, + { + "family": "Askell", + "given": "Amanda" + }, + { + "family": "Mishkin", + "given": "Pamela" + }, + { + "family": "Clark", + "given": "Jack" + }, + { + "family": "Krueger", + "given": "Gretchen" + }, + { + "family": "Sutskever", + "given": "Ilya" + } + ], + "id": "27937/UYVGUT4C", + "issued": { + "date-parts": [ + [ + 2021, + 2, + 26 + ] + ] + }, + "note": "arXiv:2103.00020 [cs]", + "number": "arXiv:2103.00020", + "publisher": "arXiv", + "system_id": "zotero|27937/UYVGUT4C", + "title": "Learning Transferable Visual Models From Natural Language Supervision", + "type": "article" + }, + "27937/VEDFUUBA": { + "URL": "http://arxiv.org/abs/2303.13375", + "abstract": "Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation across various domains, including medicine. We present a comprehensive evaluation of GPT-4, a state-of-the-art LLM, on medical competency examinations and benchmark datasets. GPT-4 is a general-purpose model that is not specialized for medical problems through training or engineered to solve clinical tasks. Our analysis covers two sets of official practice materials for the USMLE, a three-step examination program used to assess clinical competency and grant licensure in the United States. We also evaluate performance on the MultiMedQA suite of benchmark datasets. Beyond measuring model performance, experiments were conducted to investigate the influence of test questions containing both text and images on model performance, probe for memorization of content during training, and study probability calibration, which is of critical importance in high-stakes applications like medicine. Our results show that GPT-4, without any specialized prompt crafting, exceeds the passing score on USMLE by over 20 points and outperforms earlier general-purpose models (GPT-3.5) as well as models specifically fine-tuned on medical knowledge (Med-PaLM, a prompt-tuned version of Flan-PaLM 540B). In addition, GPT-4 is significantly better calibrated than GPT-3.5, demonstrating a much-improved ability to predict the likelihood that its answers are correct. We also explore the behavior of the model qualitatively through a case study that shows the ability of GPT-4 to explain medical reasoning, personalize explanations to students, and interactively craft new counterfactual scenarios around a medical case. Implications of the findings are discussed for potential uses of GPT-4 in medical education, assessment, and clinical practice, with appropriate attention to challenges of accuracy and safety.", + "accessed": { + "date-parts": [ + [ + 2023, + 3, + 28 + ] + ] + }, + "author": [ + { + "family": "Nori", + "given": "Harsha" + }, + { + "family": "King", + "given": "Nicholas" + }, + { + "family": "McKinney", + "given": "Scott Mayer" + }, + { + "family": "Carignan", + "given": "Dean" + }, + { + "family": "Horvitz", + "given": "Eric" + } + ], + "id": "27937/VEDFUUBA", + "issued": { + "date-parts": [ + [ + 2023, + 3, + 20 + ] + ] + }, + "note": "arXiv:2303.13375 [cs]", + "number": "arXiv:2303.13375", + "publisher": "arXiv", + "system_id": "zotero|27937/VEDFUUBA", + "title": "Capabilities of GPT-4 on Medical Challenge Problems", + "type": "article" + }, + "27937/VHJBTADE": { + "ISBN": "978-1-324-09112-7", + "abstract": "\"A brilliant, revelatory account of the Cold War origins of the data-mad, algorithmic twenty-first century, from the author of the acclaimed international bestseller, These Truths. The Simulmatics Corporation, founded in 1959, mined data, targeted voters, accelerated news, manipulated consumers, destabilized politics, and disordered knowledge--decades before Facebook, Google, Amazon, and Cambridge Analytica. Silicon Valley likes to imagine it has no past but the scientists of Simulmatics are the long-dead grandfathers of Mark Zuckerberg and Elon Musk. Borrowing from psychological warfare, they used computers to predict and direct human behavior, deploying their \"People Machine\" from New York, Cambridge, and Saigon for clients that included John Kennedy's presidential campaign, the New York Times, Young & Rubicam, and, during the Vietnam War, the Department of Defense. Jill Lepore, distinguished Harvard historian and New Yorker staff writer, unearthed from the archives the almost unbelievable story of this long-vanished corporation, and of the women hidden behind it. In the 1950s and 1960s, Lepore argues, Simulmatics invented the future by building the machine in which the world now finds itself trapped and tormented, algorithm by algorithm\"-- Provided by publisher", + "author": [ + { + "family": "Lepore", + "given": "Jill" + } + ], + "event-place": "New York, NY", + "id": "27937/VHJBTADE", + "issued": { + "date-parts": [ + [ + 2021 + ] + ] + }, + "language": "eng", + "note": "OCLC: 1233267158", + "number-of-pages": "415", + "publisher": "Liveright Publishing Corporation, a division of W.W. Norton & Company", + "publisher-place": "New York, NY", + "shortTitle": "If then", + "system_id": "zotero|27937/VHJBTADE", + "title": "If then: how the Simulmatics Corporation invented the future", + "type": "book" + }, + "27937/VXGSAGTI": { + "ISBN": "9780471268512", + "abstract": "Written for practitioners of data mining, data cleaning and database management.Presents a technical treatment of data quality including process, metrics, tools and algorithms.Focuses on developing an evolving modeling strategy through an iterative data exploration loop and incorporation of domain knowledge.Addresses methods of detecting, quantifying and correcting data quality issues that can have a significant impact on findings and decisions, using commercially available tools as well as new algorithmic approaches.Uses case studies to illustrate applications in real life scenarios.Highlights new approaches and methodologies, such as the DataSphere space partitioning and summary based analysis techniques.Exploratory Data Mining and Data Cleaning will serve as an important reference for serious data analysts who need to analyze large amounts of unfamiliar data, managers of operations databases, and students in undergraduate or graduate level courses dealing with large scale data analys is and data mining.", + "author": [ + { + "family": "Dasu", + "given": "Tamraparni" + }, + { + "family": "Johnson", + "given": "Theodore" + } + ], + "edition": "1st edition", + "event-place": "New York", + "id": "27937/VXGSAGTI", + "issued": { + "date-parts": [ + [ + 2003, + 5, + 9 + ] + ] + }, + "language": "English", + "number-of-pages": "224", + "publisher": "Wiley-Interscience", + "publisher-place": "New York", + "system_id": "zotero|27937/VXGSAGTI", + "title": "Exploratory Data Mining and Data Cleaning", + "type": "book" + }, + "27937/X4D92B7V": { + "ISBN": "9781509526406", + "abstract": "From everyday apps to complex algorithms, Ruha Benjamin cuts through tech-industry hype to understand how emerging technologies can reinforce White supremacy and deepen social inequity.Benjamin argues that automation, far from being a sinister story of racist programmers scheming on the dark web, has the potential to hide, speed up, and deepen discrimination while appearing neutral and even benevolent when compared to the racism of a previous era. Presenting the concept of the “New Jim Code,” she shows how a range of discriminatory designs encode inequity by explicitly amplifying racial hierarchies; by ignoring but thereby replicating social divisions; or by aiming to fix racial bias but ultimately doing quite the opposite. Moreover, she makes a compelling case for race itself as a kind of technology, designed to stratify and sanctify social injustice in the architecture of everyday life.This illuminating guide provides conceptual tools for decoding tech promises with sociologically informed skepticism. In doing so, it challenges us to question not only the technologies we are sold but also the ones we ourselves manufacture.", + "author": [ + { + "family": "Benjamin", + "given": "Ruha" + } + ], + "edition": "1st edition", + "event-place": "Medford, MA", + "id": "27937/X4D92B7V", + "issued": { + "date-parts": [ + [ + 2019, + 6, + 17 + ] + ] + }, + "language": "English", + "number-of-pages": "172", + "publisher": "Polity", + "publisher-place": "Medford, MA", + "shortTitle": "Race After Technology", + "system_id": "zotero|27937/X4D92B7V", + "title": "Race After Technology: Abolitionist Tools for the New Jim Code", + "type": "book" + }, + "27937/XBWIZZJJ": { + "URL": "http://arxiv.org/abs/2304.03442", + "abstract": "Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools. In this paper, we introduce generative agents--computational software agents that simulate believable human behavior. Generative agents wake up, cook breakfast, and head to work; artists paint, while authors write; they form opinions, notice each other, and initiate conversations; they remember and reflect on days past as they plan the next day. To enable generative agents, we describe an architecture that extends a large language model to store a complete record of the agent's experiences using natural language, synthesize those memories over time into higher-level reflections, and retrieve them dynamically to plan behavior. We instantiate generative agents to populate an interactive sandbox environment inspired by The Sims, where end users can interact with a small town of twenty five agents using natural language. In an evaluation, these generative agents produce believable individual and emergent social behaviors: for example, starting with only a single user-specified notion that one agent wants to throw a Valentine's Day party, the agents autonomously spread invitations to the party over the next two days, make new acquaintances, ask each other out on dates to the party, and coordinate to show up for the party together at the right time. We demonstrate through ablation that the components of our agent architecture--observation, planning, and reflection--each contribute critically to the believability of agent behavior. By fusing large language models with computational, interactive agents, this work introduces architectural and interaction patterns for enabling believable simulations of human behavior.", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 23 + ] + ] + }, + "author": [ + { + "family": "Park", + "given": "Joon Sung" + }, + { + "family": "O'Brien", + "given": "Joseph C." + }, + { + "family": "Cai", + "given": "Carrie J." + }, + { + "family": "Morris", + "given": "Meredith Ringel" + }, + { + "family": "Liang", + "given": "Percy" + }, + { + "family": "Bernstein", + "given": "Michael S." + } + ], + "id": "27937/XBWIZZJJ", + "issued": { + "date-parts": [ + [ + 2023, + 8, + 6 + ] + ] + }, + "note": "arXiv:2304.03442", + "number": "arXiv:2304.03442", + "publisher": "arXiv", + "shortTitle": "Generative Agents", + "system_id": "zotero|27937/XBWIZZJJ", + "title": "Generative Agents: Interactive Simulacra of Human Behavior", + "type": "article" + }, + "27937/XEUKQDPE": { + "DOI": "10.18637/jss.v059.i10", + "URL": "https://doi.org/10.18637/jss.v059.i10", + "abstract": "A huge amount of effort is spent cleaning data to get it ready for analysis, but there has been little research on how to make data cleaning as easy and effective as possible. This paper tackles a small, but important, component of data cleaning: data tidying. Tidy datasets are easy to manipulate, model and visualize, and have a specific structure: each variable is a column, each observation is a row, and each type of observational unit is a table. This framework makes it easy to tidy messy datasets because only a small set of tools are needed to deal with a wide range of un-tidy datasets. This structure also makes it easier to develop tidy tools for data analysis, tools that both input and output tidy datasets. The advantages of a consistent data structure and matching tools are demonstrated with a case study free from mundane data manipulation chores.", + "accessed": { + "date-parts": [ + [ + 2023, + 3, + 28 + ] + ] + }, + "author": [ + { + "family": "Wickham", + "given": "Hadley" + } + ], + "container-title": "Journal of Statistical Software", + "id": "27937/XEUKQDPE", + "issued": { + "date-parts": [ + [ + 2014, + 9, + 12 + ] + ] + }, + "language": "en", + "page": "1-23", + "system_id": "zotero|27937/XEUKQDPE", + "title": "Tidy Data", + "type": "article-journal", + "volume": "59" + }, + "27937/XQYUJV5F": { + "DOI": "10.1093/ahr/rhad362", + "URL": "https://academic.oup.com/ahr/article/128/3/1345/7282240", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 25 + ] + ] + }, + "author": [ + { + "family": "Meadows", + "given": "R. Darrell" + }, + { + "family": "Sternfeld", + "given": "Joshua" + } + ], + "container-title": "The American Historical Review", + "id": "27937/XQYUJV5F", + "issue": "3", + "issued": { + "date-parts": [ + [ + 2023, + 9, + 26 + ] + ] + }, + "language": "en", + "page": "1345-1349", + "system_id": "zotero|27937/XQYUJV5F", + "title": "Artificial Intelligence and the Practice of History", + "type": "article-journal", + "volume": "128" + }, + "27937/YVTAGDKZ": { + "ISBN": "9780300209570", + "abstract": "The hidden costs of artificial intelligence—from natural resources and labor to privacy, equality, and freedom\"This study argues that [artificial intelligence] is neither artificial nor particularly intelligent. . . . A fascinating history of the data on which machine-learning systems are trained.\"—New Yorker\"A valuable corrective to much of the hype surrounding AI and a useful instruction manual for the future.\"—John Thornhill, Financial Times\"It’s a masterpiece, and I haven’t been able to stop thinking about it.\"—Karen Hao, senior editor, MIT Tech Review What happens when artificial intelligence saturates political life and depletes the planet? How is AI shaping our understanding of ourselves and our societies? Drawing on more than a decade of research, award‑winning scholar Kate Crawford reveals how AI is a technology of extraction: from the minerals drawn from the earth, to the labor pulled from low-wage information workers, to the data taken from every action and expression. This book reveals how this planetary network is fueling a shift toward undemocratic governance and increased inequity. Rather than taking a narrow focus on code and algorithms, Crawford offers us a material and political perspective on what it takes to make AI and how it centralizes power. This is an urgent account of what is at stake as technology companies use artificial intelligence to reshape the world.", + "author": [ + { + "family": "Crawford", + "given": "Kate" + } + ], + "event-place": "New Haven", + "id": "27937/YVTAGDKZ", + "issued": { + "date-parts": [ + [ + 2021, + 4, + 6 + ] + ] + }, + "language": "English", + "number-of-pages": "336", + "publisher": "Yale University Press", + "publisher-place": "New Haven", + "shortTitle": "Atlas of AI", + "system_id": "zotero|27937/YVTAGDKZ", + "title": "Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence", + "type": "book" + }, + "27937/YWJAQ4V8": { + "ISBN": "978-0-19-803513-8", + "abstract": "Oral history is vital to our understanding of the cultures and experiences of the past. Unlike written history, oral history forever captures people's feelings, expressions, and nuances of language. But what exactly is oral history? How reliable is the information gathered by oral history? And what does it take to become an oral historian? Donald A. Ritchie, a leading expert in the field, answers these questions and in particular, explains the principles and guidelines created by the Oral History Association to ensure the professional standards of oral historians. Doing Oral History has becom", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 9 + ] + ] + }, + "author": [ + { + "family": "Ritchie", + "given": "Donald A." + } + ], + "edition": "2nd ed", + "event-place": "Cary", + "id": "27937/YWJAQ4V8", + "issued": { + "date-parts": [ + [ + 2003 + ] + ] + }, + "language": "eng", + "note": "OCLC: 1049804116", + "number-of-pages": "290", + "publisher": "Oxford University Press, USA", + "publisher-place": "Cary", + "shortTitle": "Doing Oral History", + "system_id": "zotero|27937/YWJAQ4V8", + "title": "Doing Oral History: a Practical Guide", + "type": "book" + }, + "27937/Z44J4BKC": { + "DOI": "10.1093/jamia/ocad259", + "URL": "https://doi.org/10.1093/jamia/ocad259", + "abstract": "The study highlights the potential of large language models, specifically GPT-3.5 and GPT-4, in processing complex clinical data and extracting meaningful information with minimal training data. By developing and refining prompt-based strategies, we can significantly enhance the models’ performance, making them viable tools for clinical NER tasks and possibly reducing the reliance on extensive annotated datasets.This study quantifies the capabilities of GPT-3.5 and GPT-4 for clinical named entity recognition (NER) tasks and proposes task-specific prompts to improve their performance.We evaluated these models on 2 clinical NER tasks: (1) to extract medical problems, treatments, and tests from clinical notes in the MTSamples corpus, following the 2010 i2b2 concept extraction shared task, and (2) to identify nervous system disorder-related adverse events from safety reports in the vaccine adverse event reporting system (VAERS). To improve the GPT models' performance, we developed a clinical task-specific prompt framework that includes (1) baseline prompts with task description and format specification, (2) annotation guideline-based prompts, (3) error analysis-based instructions, and (4) annotated samples for few-shot learning. We assessed each prompt's effectiveness and compared the models to BioClinicalBERT.Using baseline prompts, GPT-3.5 and GPT-4 achieved relaxed F1 scores of 0.634, 0.804 for MTSamples and 0.301, 0.593 for VAERS. Additional prompt components consistently improved model performance. When all 4 components were used, GPT-3.5 and GPT-4 achieved relaxed F1 socres of 0.794, 0.861 for MTSamples and 0.676, 0.736 for VAERS, demonstrating the effectiveness of our prompt framework. Although these results trail BioClinicalBERT (F1 of 0.901 for the MTSamples dataset and 0.802 for the VAERS), it is very promising considering few training samples are needed.The study’s findings suggest a promising direction in leveraging LLMs for clinical NER tasks. However, while the performance of GPT models improved with task-specific prompts, there's a need for further development and refinement. LLMs like GPT-4 show potential in achieving close performance to state-of-the-art models like BioClinicalBERT, but they still require careful prompt engineering and understanding of task-specific knowledge. The study also underscores the importance of evaluation schemas that accurately reflect the capabilities and performance of LLMs in clinical settings.While direct application of GPT models to clinical NER tasks falls short of optimal performance, our task-specific prompt framework, incorporating medical knowledge and training samples, significantly enhances GPT models' feasibility for potential clinical applications.", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 16 + ] + ] + }, + "author": [ + { + "family": "Hu", + "given": "Yan" + }, + { + "family": "Chen", + "given": "Qingyu" + }, + { + "family": "Du", + "given": "Jingcheng" + }, + { + "family": "Peng", + "given": "Xueqing" + }, + { + "family": "Keloth", + "given": "Vipina Kuttichi" + }, + { + "family": "Zuo", + "given": "Xu" + }, + { + "family": "Zhou", + "given": "Yujia" + }, + { + "family": "Li", + "given": "Zehan" + }, + { + "family": "Jiang", + "given": "Xiaoqian" + }, + { + "family": "Lu", + "given": "Zhiyong" + }, + { + "family": "Roberts", + "given": "Kirk" + }, + { + "family": "Xu", + "given": "Hua" + } + ], + "container-title": "Journal of the American Medical Informatics Association", + "id": "27937/Z44J4BKC", + "issue": "9", + "issued": { + "date-parts": [ + [ + 2024, + 9, + 1 + ] + ] + }, + "journalAbbreviation": "Journal of the American Medical Informatics Association", + "page": "1812-1820", + "system_id": "zotero|27937/Z44J4BKC", + "title": "Improving large language models for clinical named entity recognition via prompt engineering", + "type": "article-journal", + "volume": "31" + }, + "27937/ZJW9AI49": { + "DOI": "10.3138/chr.694", + "URL": "https://www.utpjournals.press/doi/abs/10.3138/chr.694", + "abstract": "It all seems so orderly and comprehensive. Instead of firing up the microfilm reader to navigate the Globe and Mail or the Toronto Star, one needs only to log into online newspaper databases. A keyword search, for a particular event, person, or cultural phenomenon, brings up a list of research findings. Previously impossible research projects can now be attempted. This process has fundamentally reshaped Canadian historical scholarship. We can see this in Canadian history dissertations. In 1998, a year with 67 dissertations, the Toronto Star was cited 74 times. However it was cited 753 times in 2010, a year with 69 dissertations. Similar data appears in the Canadian Historical Review (CHR), a prestigious peer-reviewed journal. Databases are skewing our research. We are witnessing the application of commercial Optical Character Recognition (OCR) technology – originally and primarily designed for the efficient digitization of large reams of corporate and legal documents, conventionally formatted – to historical sources. The results are, unsurprisingly, a mixed bag. In this article, I make two arguments. Firstly, online historical databases have profoundly shaped Canadian historiography. In a shift that is rarely – if ever – made explicit, Canadian historians have profoundly reacted to the availability of online databases. Secondly, historians need to understand how OCR works, in order to bring a level of methodological rigor to their work that use these sources.", + "accessed": { + "date-parts": [ + [ + 2023, + 3, + 31 + ] + ] + }, + "author": [ + { + "family": "Milligan", + "given": "Ian" + } + ], + "container-title": "Canadian Historical Review", + "id": "27937/ZJW9AI49", + "issue": "4", + "issued": { + "date-parts": [ + [ + 2013, + 12 + ] + ] + }, + "page": "540-569", + "shortTitle": "Illusionary Order", + "system_id": "zotero|27937/ZJW9AI49", + "title": "Illusionary Order: Online Databases, Optical Character Recognition, and Canadian History, 1997–2010", + "type": "article-journal", + "volume": "94" + }, + "27937/ZS9JDNGD": { + "URL": "http://arxiv.org/abs/2009.03300", + "abstract": "We propose a new test to measure a text model's multitask accuracy. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability. We find that while most recent models have near random-chance accuracy, the very largest GPT-3 model improves over random chance by almost 20 percentage points on average. However, on every one of the 57 tasks, the best models still need substantial improvements before they can reach expert-level accuracy. Models also have lopsided performance and frequently do not know when they are wrong. Worse, they still have near-random accuracy on some socially important subjects such as morality and law. By comprehensively evaluating the breadth and depth of a model's academic and professional understanding, our test can be used to analyze models across many tasks and to identify important shortcomings.", + "accessed": { + "date-parts": [ + [ + 2023, + 4, + 2 + ] + ] + }, + "author": [ + { + "family": "Hendrycks", + "given": "Dan" + }, + { + "family": "Burns", + "given": "Collin" + }, + { + "family": "Basart", + "given": "Steven" + }, + { + "family": "Zou", + "given": "Andy" + }, + { + "family": "Mazeika", + "given": "Mantas" + }, + { + "family": "Song", + "given": "Dawn" + }, + { + "family": "Steinhardt", + "given": "Jacob" + } + ], + "id": "27937/ZS9JDNGD", + "issued": { + "date-parts": [ + [ + 2021, + 1, + 12 + ] + ] + }, + "note": "arXiv:2009.03300 [cs]", + "number": "arXiv:2009.03300", + "publisher": "arXiv", + "system_id": "zotero|27937/ZS9JDNGD", + "title": "Measuring Massive Multitask Language Understanding", + "type": "article" + }, + "27937/ZXTQBIJU": { + "URL": "https://huggingface.co/datasets/PleIAs/Post-OCR-Correction", + "abstract": "We’re on a journey to advance and democratize artificial intelligence through open source and open science.", + "accessed": { + "date-parts": [ + [ + 2024, + 10, + 14 + ] + ] + }, + "author": [ + { + "family": "PleIAs", + "given": "" + } + ], + "container-title": "PleIAs/Post-OCR-Correction · Datasets at Hugging Face", + "id": "27937/ZXTQBIJU", + "issued": { + "date-parts": [ + [ + 2024, + 6, + 9 + ] + ] + }, + "system_id": "zotero|27937/ZXTQBIJU", + "title": "PleIAs/Post-OCR-Correction · Datasets at Hugging Face", + "type": "webpage" + } + } + }, + "style": "chicago-note-bibliography.csl" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/article.ipynb b/article.ipynb index 4bbdd32..9625ba6 100644 --- a/article.ipynb +++ b/article.ipynb @@ -413,7 +413,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 4, "metadata": { "collapsed": false, "editable": true, @@ -423,16 +423,12 @@ "slideshow": { "slide_type": "" }, - "tags": [ - "table-1-*" - ] + "tags": [] }, "outputs": [ { "data": { - "text/html": [ - "" - ], + "image/png": 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", "text/plain": [ "" ] @@ -445,9 +441,7 @@ "from IPython.display import Image\n", "from IPython.display import display\n", "from IPython.display import Markdown\n", - "\n", - "table_1_url = 'media/Table 1 - MMLU Benchmark Performance.png'\n", - "display(Image(url=table_1_url, width=850))" + "display(Image('./media/Table 1 - MMLU Benchmark Performance.png'))" ] }, {