- See Also
-
Links
- “FABLES: Evaluating Faithfulness and Content Selection in Book-Length Summarization”, Kim et al 2024
- “Vulnerability Detection With Code Language Models: How Far Are We?”, Ding et al 2024
- “The NSA Warns That US Adversaries Free to Mine Private Data May Have an AI Edge: Gilbert Herrera, Who Leads Research at the National Security Agency, Says Large Language Models Are Incredibly Useful—And a Bit of a Headache—For America’s Intelligence Machine”, Knight 2024
- “Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews”, Liang et al 2024
- “Tokenization Counts: the Impact of Tokenization on Arithmetic in Frontier LLMs”, Singh & Strouse 2024
-
“
ArtPrompt: ASCII Art-Based Jailbreak Attacks against Aligned LLMs”, Jiang et al 2024 - “The Non-Effect of Sampling Temperature on Problem Solving in GPT-3.5/GPT-4”, Renze & Guven 2024
- “Does Using ChatGPT Result in Human Cognitive Augmentation?”, Fulbright & Morrison 2024
- “TinyGSM: Achieving >80% on GSM8k With Small Language Models”, Liu et al 2023
- “Universal Self-Consistency for Large Language Model Generation”, Chen et al 2023
- “PEARL: Personalizing Large Language Model Writing Assistants With Generation-Calibrated Retrievers”, Mysore et al 2023
- “Zero-Shot Goal-Directed Dialogue via RL on Imagined Conversations”, Hong et al 2023
- “Data Contamination Through the Lens of Time”, Roberts et al 2023
- “Can GPT Models Be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on Mock CFA Exams”, Callanan et al 2023
- “Beyond Memorization: Violating Privacy Via Inference With Large Language Models”, Staab et al 2023
- “GeoLLM: Extracting Geospatial Knowledge from Large Language Models”, Manvi et al 2023
- “Can a Computer Outfake a Human [personality]?”, Phillips & Robie 2023
- “Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models”, Zhou et al 2023
- “Using Large Language Models for Qualitative Analysis Can Introduce Serious Bias”, Ashwin et al 2023
- “Embers of Autoregression: Understanding Large Language Models Through the Problem They Are Trained to Solve”, McCoy et al 2023
- “The Cambridge Law Corpus: A Corpus for Legal AI Research”, Östling et al 2023
- “A Boy Saw 17 Doctors over 3 Years for Chronic Pain. ChatGPT Found the Diagnosis”, Holohan 2023
- “Investigating the Existence of ‘Secret Language’ in Language Models”, Wang et al 2023
- “Are Large Language Models a Threat to Digital Public Goods? Evidence from Activity on Stack Overflow”, Rio-Chanona et al 2023
- “Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events”, Gu et al 2023
- “Explaining Competitive-Level Programming Solutions Using LLMs”, Li et al 2023
- “Lost in the Middle: How Language Models Use Long Contexts”, Liu et al 2023
- “Evaluating Superhuman Models With Consistency Checks”, Fluri et al 2023
- “Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Tasks”, Veselovsky et al 2023
- “Can Large Language Models Democratize Access to Dual-Use Biotechnology?”, Soice et al 2023
- “Iterative Translation Refinement With Large Language Models”, Chen et al 2023
- “Don’t Want Students to Rely on ChatGPT? Have Them Use It: It’s Easy to Forget How Little Students and Educators Understand Generative AI’s Flaws. Once They Actually Try It Out, They’ll See That It Can’t Replace Them”, Howell 2023
- “The Exciting Potential for ChatGPT in Obstetrics and Gynecology”, Grünebaum et al 2023
- “Do GPTs Produce Less Literal Translations?”, Raunak et al 2023
- “The False Promise of Imitating Proprietary LLMs”, Gudibande et al 2023
- “Learning to Generate Novel Scientific Directions With Contextualized Literature-Based Discovery”, Wang et al 2023
- “How Language Model Hallucinations Can Snowball”, Zhang et al 2023
- “LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions”, Wu et al 2023
- “Evaluating Transformer Language Models on Arithmetic Operations Using Number Decomposition”, Muffo et al 2023
- “Generative AI at Work”, Brynjolfsson et al 2023
- “Humans in Humans Out: On GPT Converging Toward Common Sense in Both Success and Failure”, Koralus & Wang-Maścianica 2023
- “Language Models Can Solve Computer Tasks”, Kim et al 2023
- “Performance of ChatGPT on Free-Response, Clinical Reasoning Exams”, Strong et al 2023
- “How Well Do Large Language Models Perform in Arithmetic Tasks?”, Yuan et al 2023
- “Is ChatGPT a General-Purpose Natural Language Processing Task Solver?”, Qin et al 2023
- “Predicting Consumer Contracts [With GPT-3]”, Kolt 2023
- “Use GPT-3 Incorrectly: Reduce Costs 40× and Increase Speed by 5×”, Pullen 2023
- “A Judge Just Used ChatGPT to Make a Court Decision: The Case Is the First Time a Court Has Admitted to Using the AI Text Generator’s Answers in a Legal Ruling”, Rose 2023
- “Co-Writing With Opinionated Language Models Affects Users’ Views”, Jakesch et al 2023
- “The inside Story of ChatGPT: How OpenAI Founder Sam Altman Built the World’s Hottest Technology With Billions from Microsoft”, Kahn 2023
- “How Close Is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection”, Guo et al 2023
- “Can GPT-3 Produce New Ideas? Partially Automating Robin Hanson and Others § If You Never Miss a Plane…”, Sempere 2023
- “How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment”, Gilson et al 2023
- “GPT-3 Takes the Bar Exam”, II & Katz 2022
- “Precise Zero-Shot Dense Retrieval without Relevance Labels”, Gao et al 2022
- “Self-Instruct: Aligning Language Models With Self-Generated Instructions”, Wang et al 2022
- “Emergent Analogical Reasoning in Large Language Models”, Webb et al 2022
- “Harvey, Which Uses AI to Answer Legal Questions, Lands Cash from OpenAI”, Wiggers 2022
- “LMentry: A Language Model Benchmark of Elementary Language Tasks”, Efrat et al 2022
- “Self-Ask: Measuring and Narrowing the Compositionality Gap in Language Models (Bamboogle)”, Press et al 2022
- “How Persuasive Is AI-Generated Argumentation? An Analysis of the Quality of an Argumentative Text Produced by the GPT-3 AI Text Generator”, Hinton & Wagemans 2022
- “Out of One, Many: Using Language Models to Simulate Human Samples”, Argyle et al 2022
- “What Does a Platypus Look Like? Generating Customized Prompts for Zero-Shot Image Classification (CuPL)”, Pratt et al 2022
- “Using Large Language Models to Simulate Multiple Humans”, Aher et al 2022
- “Limitations of Language Models in Arithmetic and Symbolic Induction”, Qian et al 2022
- “RealTime QA: What’s the Answer Right Now?”, Kasai et al 2022
- “GODEL: Large-Scale Pre-Training for Goal-Directed Dialog”, Peng et al 2022
- “Can GPT-3 Write an Academic Paper on Itself, With Minimal Human Input?”, GPT-3 et al 2022 (page 2)
- “NaturalProver: Grounded Mathematical Proof Generation With Language Models”, Welleck et al 2022
- “OPT: Open Pre-Trained Transformer Language Models”, Zhang et al 2022
- “InstructGPT: Training Language Models to Follow Instructions With Human Feedback”, Ouyang et al 2022
- “Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?”, Min et al 2022
- “Impact of Pretraining Term Frequencies on Few-Shot Reasoning”, Razeghi et al 2022
- “Contracts in the Age of Smart Readers”, Arbel & Becher 2022
- “Memory-Assisted Prompt Editing to Improve GPT-3 After Deployment”, Madaan et al 2022
- “CommonsenseQA 2.0: Exposing the Limits of AI through Gamification”, Talmor et al 2022
- “Limits of Using Artificial Intelligence and GPT-3 in Patent Prosecution”, Tu et al 2022
- “What Can a Generative Language Model Answer About a Passage?”, Summers-Stay et al 2021
- “Process for Adapting Language Models to Society (PALMS) With Values-Targeted Datasets”, Solaiman & Dennison 2021
- “Scaling Laws for Autoregressive Generative Modeling”, Henighan et al 2020
- “GPT-3: Its Nature, Scope, Limits, and Consequences”, Floridi & Chiriatti 2020
- “MMLU: Measuring Massive Multitask Language Understanding”, Hendrycks et al 2020
- “GPT-3: Language Models Are Few-Shot Learners”, Brown et al 2020
- spolu
- “A Robot Wrote This Entire Article. Are You Scared Yet, Human? We Asked GPT-3, OpenAI’s Powerful New Language Generator, to Write an Essay for Us from Scratch. The Assignment? To Convince Us Robots Come in Peace | For More about GPT-3 and How This Essay Was Written and Edited, Please Read Our Editor’s Note Below”
- Sort By Magic
- Miscellaneous
- Link Bibliography
See Also
Links
“FABLES: Evaluating Faithfulness and Content Selection in Book-Length Summarization”, Kim et al 2024
FABLES: Evaluating faithfulness and content selection in book-length summarization
“Vulnerability Detection With Code Language Models: How Far Are We?”, Ding et al 2024
Vulnerability Detection with Code Language Models: How Far Are We?
“The NSA Warns That US Adversaries Free to Mine Private Data May Have an AI Edge: Gilbert Herrera, Who Leads Research at the National Security Agency, Says Large Language Models Are Incredibly Useful—And a Bit of a Headache—For America’s Intelligence Machine”, Knight 2024
“Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews”, Liang et al 2024
“Tokenization Counts: the Impact of Tokenization on Arithmetic in Frontier LLMs”, Singh & Strouse 2024
Tokenization counts: the impact of tokenization on arithmetic in frontier LLMs
“ArtPrompt: ASCII Art-Based Jailbreak Attacks against Aligned LLMs”, Jiang et al 2024
ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMs
“The Non-Effect of Sampling Temperature on Problem Solving in GPT-3.5/GPT-4”, Renze & Guven 2024
The Non-Effect of Sampling Temperature on Problem Solving in GPT-3.5/GPT-4
“Does Using ChatGPT Result in Human Cognitive Augmentation?”, Fulbright & Morrison 2024
“TinyGSM: Achieving >80% on GSM8k With Small Language Models”, Liu et al 2023
“Universal Self-Consistency for Large Language Model Generation”, Chen et al 2023
Universal Self-Consistency for Large Language Model Generation
“PEARL: Personalizing Large Language Model Writing Assistants With Generation-Calibrated Retrievers”, Mysore et al 2023
PEARL: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers
“Zero-Shot Goal-Directed Dialogue via RL on Imagined Conversations”, Hong et al 2023
Zero-Shot Goal-Directed Dialogue via RL on Imagined Conversations
“Data Contamination Through the Lens of Time”, Roberts et al 2023
“Can GPT Models Be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on Mock CFA Exams”, Callanan et al 2023
Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Exams
“Beyond Memorization: Violating Privacy Via Inference With Large Language Models”, Staab et al 2023
Beyond Memorization: Violating Privacy Via Inference with Large Language Models
“GeoLLM: Extracting Geospatial Knowledge from Large Language Models”, Manvi et al 2023
GeoLLM: Extracting Geospatial Knowledge from Large Language Models
“Can a Computer Outfake a Human [personality]?”, Phillips & Robie 2023
“Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models”, Zhou et al 2023
Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models
“Using Large Language Models for Qualitative Analysis Can Introduce Serious Bias”, Ashwin et al 2023
Using Large Language Models for Qualitative Analysis Can Introduce Serious Bias
“Embers of Autoregression: Understanding Large Language Models Through the Problem They Are Trained to Solve”, McCoy et al 2023
“The Cambridge Law Corpus: A Corpus for Legal AI Research”, Östling et al 2023
“A Boy Saw 17 Doctors over 3 Years for Chronic Pain. ChatGPT Found the Diagnosis”, Holohan 2023
A boy saw 17 doctors over 3 years for chronic pain. ChatGPT found the diagnosis
“Investigating the Existence of ‘Secret Language’ in Language Models”, Wang et al 2023
Investigating the Existence of ‘Secret Language’ in Language Models
“Are Large Language Models a Threat to Digital Public Goods? Evidence from Activity on Stack Overflow”, Rio-Chanona et al 2023
Are Large Language Models a Threat to Digital Public Goods? Evidence from Activity on Stack Overflow
“Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events”, Gu et al 2023
“Explaining Competitive-Level Programming Solutions Using LLMs”, Li et al 2023
Explaining Competitive-Level Programming Solutions using LLMs
“Lost in the Middle: How Language Models Use Long Contexts”, Liu et al 2023
“Evaluating Superhuman Models With Consistency Checks”, Fluri et al 2023
“Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Tasks”, Veselovsky et al 2023
“Can Large Language Models Democratize Access to Dual-Use Biotechnology?”, Soice et al 2023
Can large language models democratize access to dual-use biotechnology?
“Iterative Translation Refinement With Large Language Models”, Chen et al 2023
“Don’t Want Students to Rely on ChatGPT? Have Them Use It: It’s Easy to Forget How Little Students and Educators Understand Generative AI’s Flaws. Once They Actually Try It Out, They’ll See That It Can’t Replace Them”, Howell 2023
“The Exciting Potential for ChatGPT in Obstetrics and Gynecology”, Grünebaum et al 2023
The exciting potential for ChatGPT in obstetrics and gynecology
“Do GPTs Produce Less Literal Translations?”, Raunak et al 2023
“The False Promise of Imitating Proprietary LLMs”, Gudibande et al 2023
“Learning to Generate Novel Scientific Directions With Contextualized Literature-Based Discovery”, Wang et al 2023
Learning to Generate Novel Scientific Directions with Contextualized Literature-based Discovery
“How Language Model Hallucinations Can Snowball”, Zhang et al 2023
“LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions”, Wu et al 2023
LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions
“Evaluating Transformer Language Models on Arithmetic Operations Using Number Decomposition”, Muffo et al 2023
Evaluating Transformer Language Models on Arithmetic Operations Using Number Decomposition
“Generative AI at Work”, Brynjolfsson et al 2023
“Humans in Humans Out: On GPT Converging Toward Common Sense in Both Success and Failure”, Koralus & Wang-Maścianica 2023
Humans in Humans Out: On GPT Converging Toward Common Sense in both Success and Failure
“Language Models Can Solve Computer Tasks”, Kim et al 2023
“Performance of ChatGPT on Free-Response, Clinical Reasoning Exams”, Strong et al 2023
Performance of ChatGPT on free-response, clinical reasoning exams
“How Well Do Large Language Models Perform in Arithmetic Tasks?”, Yuan et al 2023
How well do Large Language Models perform in Arithmetic tasks?
“Is ChatGPT a General-Purpose Natural Language Processing Task Solver?”, Qin et al 2023
Is ChatGPT a General-Purpose Natural Language Processing Task Solver?
“Predicting Consumer Contracts [With GPT-3]”, Kolt 2023
“Use GPT-3 Incorrectly: Reduce Costs 40× and Increase Speed by 5×”, Pullen 2023
Use GPT-3 incorrectly: reduce costs 40× and increase speed by 5×
“A Judge Just Used ChatGPT to Make a Court Decision: The Case Is the First Time a Court Has Admitted to Using the AI Text Generator’s Answers in a Legal Ruling”, Rose 2023
“Co-Writing With Opinionated Language Models Affects Users’ Views”, Jakesch et al 2023
Co-Writing with Opinionated Language Models Affects Users’ Views
“The inside Story of ChatGPT: How OpenAI Founder Sam Altman Built the World’s Hottest Technology With Billions from Microsoft”, Kahn 2023
“How Close Is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection”, Guo et al 2023
How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection
“Can GPT-3 Produce New Ideas? Partially Automating Robin Hanson and Others § If You Never Miss a Plane…”, Sempere 2023
“How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment”, Gilson et al 2023
“GPT-3 Takes the Bar Exam”, II & Katz 2022
“Precise Zero-Shot Dense Retrieval without Relevance Labels”, Gao et al 2022
“Self-Instruct: Aligning Language Models With Self-Generated Instructions”, Wang et al 2022
Self-Instruct: Aligning Language Models with Self-Generated Instructions
“Emergent Analogical Reasoning in Large Language Models”, Webb et al 2022
“Harvey, Which Uses AI to Answer Legal Questions, Lands Cash from OpenAI”, Wiggers 2022
Harvey, which uses AI to answer legal questions, lands cash from OpenAI
“LMentry: A Language Model Benchmark of Elementary Language Tasks”, Efrat et al 2022
LMentry: A Language Model Benchmark of Elementary Language Tasks
“Self-Ask: Measuring and Narrowing the Compositionality Gap in Language Models (Bamboogle)”, Press et al 2022
Self-Ask: Measuring and Narrowing the Compositionality Gap in Language Models (Bamboogle)
“How Persuasive Is AI-Generated Argumentation? An Analysis of the Quality of an Argumentative Text Produced by the GPT-3 AI Text Generator”, Hinton & Wagemans 2022
“Out of One, Many: Using Language Models to Simulate Human Samples”, Argyle et al 2022
Out of One, Many: Using Language Models to Simulate Human Samples
“What Does a Platypus Look Like? Generating Customized Prompts for Zero-Shot Image Classification (CuPL)”, Pratt et al 2022
“Using Large Language Models to Simulate Multiple Humans”, Aher et al 2022
“Limitations of Language Models in Arithmetic and Symbolic Induction”, Qian et al 2022
Limitations of Language Models in Arithmetic and Symbolic Induction
“RealTime QA: What’s the Answer Right Now?”, Kasai et al 2022
“GODEL: Large-Scale Pre-Training for Goal-Directed Dialog”, Peng et al 2022
“Can GPT-3 Write an Academic Paper on Itself, With Minimal Human Input?”, GPT-3 et al 2022 (page 2)
Can GPT-3 write an academic paper on itself, with minimal human input?
“NaturalProver: Grounded Mathematical Proof Generation With Language Models”, Welleck et al 2022
NaturalProver: Grounded Mathematical Proof Generation with Language Models
“OPT: Open Pre-Trained Transformer Language Models”, Zhang et al 2022
“InstructGPT: Training Language Models to Follow Instructions With Human Feedback”, Ouyang et al 2022
InstructGPT: Training language models to follow instructions with human feedback
“Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?”, Min et al 2022
Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?
“Impact of Pretraining Term Frequencies on Few-Shot Reasoning”, Razeghi et al 2022
Impact of Pretraining Term Frequencies on Few-Shot Reasoning
“Contracts in the Age of Smart Readers”, Arbel & Becher 2022
“Memory-Assisted Prompt Editing to Improve GPT-3 After Deployment”, Madaan et al 2022
Memory-assisted prompt editing to improve GPT-3 after deployment
“CommonsenseQA 2.0: Exposing the Limits of AI through Gamification”, Talmor et al 2022
CommonsenseQA 2.0: Exposing the Limits of AI through Gamification
“Limits of Using Artificial Intelligence and GPT-3 in Patent Prosecution”, Tu et al 2022
Limits of Using Artificial Intelligence and GPT-3 in Patent Prosecution
“What Can a Generative Language Model Answer About a Passage?”, Summers-Stay et al 2021
What Can a Generative Language Model Answer About a Passage?
“Process for Adapting Language Models to Society (PALMS) With Values-Targeted Datasets”, Solaiman & Dennison 2021
Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets
“Scaling Laws for Autoregressive Generative Modeling”, Henighan et al 2020
“GPT-3: Its Nature, Scope, Limits, and Consequences”, Floridi & Chiriatti 2020
“MMLU: Measuring Massive Multitask Language Understanding”, Hendrycks et al 2020
“GPT-3: Language Models Are Few-Shot Learners”, Brown et al 2020
spolu
“A Robot Wrote This Entire Article. Are You Scared Yet, Human? We Asked GPT-3, OpenAI’s Powerful New Language Generator, to Write an Essay for Us from Scratch. The Assignment? To Convince Us Robots Come in Peace | For More about GPT-3 and How This Essay Was Written and Edited, Please Read Our Editor’s Note Below”
Sort By Magic
Annotations sorted by machine learning into inferred 'tags'. This provides an alternative way to browse: instead of by date order, one can browse in topic order. The 'sorted' list has been automatically clustered into multiple sections & auto-labeled for easier browsing.
Beginning with the newest annotation, it uses the embedding of each annotation to attempt to create a list of nearest-neighbor annotations, creating a progression of topics. For more details, see the link.
llm-research
legaltech
gpt-applications
language-models
Miscellaneous
-
https://automated.beehiiv.com/p/aiimmunity-challenge-lessons-clinical-research-exam -
https://chat.openai.com/share/25124525-0bad-4c13-ae5a-ae4beac60360: -
https://davidabell.substack.com/p/playing-around-with-machine-translation -
https://dropbox.tech/machine-learning/prompt-injection-with-control-characters-openai-chatgpt-llm -
https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2812620 -
https://jxnl.github.io/instructor/blog/2023/11/05/chain-of-density/ -
https://openai.com/blog/function-calling-and-other-api-updates#function-calling -
https://restofworld.org/2023/ai-revolution-outsourced-workers/ -
https://twitter.com/kenshinsamurai9/status/1662510532585291779 -
https://twitter.com/michaeltefula/status/1285505897108832257 -
https://www.ft.com/content/9aeb482d-f781-45c0-896f-38fdcc912139:View External Link:
https://www.ft.com/content/9aeb482d-f781-45c0-896f-38fdcc912139 -
https://www.integrity-research.com/ai-fails-insider-trading-test/ -
https://www.nytimes.com/2023/06/08/business/khan-ai-gpt-tutoring-bot.html -
https://www.nytimes.com/2023/12/13/technology/chatbot-cheating-schools-students.html -
https://www.reddit.com/r/ChatGPT/comments/15et6f2/well_i_got_what_i_asked_for/ -
https://www.reddit.com/r/OpenAI/comments/xlvygv/artifical_intelligence_allows_me_to_get_straight/: -
https://www.supersimple.io/blog/gpt-4-fine-tuning-early-access -
https://www.vice.com/en/article/5d93p3/what-happens-when-you-ask-ai-to-control-your-life -
https://www.wired.com/story/china-chatgpt-opportunists-grifters-hard-at-work/
Link Bibliography
-
https://arxiv.org/abs/2403.18624: “Vulnerability Detection With Code Language Models: How Far Are We?”, -
https://www.wired.com/story/fast-forward-nsa-warns-us-adversaries-private-data-ai-edge/: “The NSA Warns That US Adversaries Free to Mine Private Data May Have an AI Edge: Gilbert Herrera, Who Leads Research at the National Security Agency, Says Large Language Models Are Incredibly Useful—And a Bit of a Headache—For America’s Intelligence Machine”, Will Knight -
https://arxiv.org/abs/2402.14903: “Tokenization Counts: the Impact of Tokenization on Arithmetic in Frontier LLMs”, Aaditya K. Singh, D. J. Strouse -
https://arxiv.org/abs/2402.11753: “ArtPrompt: ASCII Art-Based Jailbreak Attacks against Aligned LLMs”, Fengqing Jiang, Zhangchen Xu, Luyao Niu, Zhen Xiang, Bhaskar Ramasubramanian, Bo Li, Radha Poovendran -
https://arxiv.org/abs/2310.08678: “Can GPT Models Be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on Mock CFA Exams”, -
https://arxiv.org/abs/2310.06213: “GeoLLM: Extracting Geospatial Knowledge from Large Language Models”, Rohin Manvi, Samar Khanna, Gengchen Mai, Marshall Burke, David Lobell, Stefano Ermon -
2023-phillips.pdf: “Can a Computer Outfake a Human [personality]?”, Jane Phillips, Chet Robie -
https://arxiv.org/abs/2310.04406: “Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models”, Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman, Haohan Wang, Yu-Xiong Wang -
https://arxiv.org/abs/2309.12269: “The Cambridge Law Corpus: A Corpus for Legal AI Research”, -
https://arxiv.org/abs/2307.06439#microsoft: “Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events”, -
https://arxiv.org/abs/2305.15717: “The False Promise of Imitating Proprietary LLMs”, Arnav Gudibande, Eric Wallace, Charlie Snell, Xinyang Geng, Hao Liu, Pieter Abbeel, Sergey Levine, Dawn Song -
https://arxiv.org/abs/2305.13534: “How Language Model Hallucinations Can Snowball”, Muru Zhang, Ofir Press, William Merrill, Alisa Liu, Noah Smith -
https://www.medrxiv.org/content/10.1101/2023.03.24.23287731.full: “Performance of ChatGPT on Free-Response, Clinical Reasoning Exams”, -
https://arxiv.org/abs/2304.02015#alibaba: “How Well Do Large Language Models Perform in Arithmetic Tasks?”, Zheng Yuan, Hongyi Yuan, Chuanqi Tan, Wei Wang, Songfang Huang -
https://arxiv.org/abs/2302.06476: “Is ChatGPT a General-Purpose Natural Language Processing Task Solver?”, Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, Diyi Yang -
2022-kolt.pdf: “Predicting Consumer Contracts [With GPT-3]”, Noam Kolt -
https://www.vice.com/en/article/k7bdmv/judge-used-chatgpt-to-make-court-decision: “A Judge Just Used ChatGPT to Make a Court Decision: The Case Is the First Time a Court Has Admitted to Using the AI Text Generator’s Answers in a Legal Ruling”, Janus Rose -
https://arxiv.org/abs/2302.00560: “Co-Writing With Opinionated Language Models Affects Users’ Views”, Maurice Jakesch, Advait Bhat, Daniel Buschek, Lior Zalmanson, Mor Naaman -
https://mededu.jmir.org/2023/1/e45312/: “How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment”, Aidan Gilson, Conrad W. Safranek, Thomas Huang, Vimig Socrates, Ling Chi, Richard Andrew Taylor, David Chartash -
https://arxiv.org/abs/2212.14402: “GPT-3 Takes the Bar Exam”, Michael Bommarito II, Daniel Martin Katz -
https://arxiv.org/abs/2212.10496: “Precise Zero-Shot Dense Retrieval without Relevance Labels”, Luyu Gao, Xueguang Ma, Jimmy Lin, Jamie Callan -
https://arxiv.org/abs/2212.10560: “Self-Instruct: Aligning Language Models With Self-Generated Instructions”, Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah Smith, Daniel Khashabi, Hannaneh Hajishirzi -
https://techcrunch.com/2022/11/23/harvey-which-uses-ai-to-answer-legal-questions-lands-cash-from-openai/: “Harvey, Which Uses AI to Answer Legal Questions, Lands Cash from OpenAI”, Kyle Wiggers -
https://arxiv.org/abs/2210.03350#allen: “Self-Ask: Measuring and Narrowing the Compositionality Gap in Language Models (Bamboogle)”, -
https://content.iospress.com/articles/argument-and-computation/aac210026: “How Persuasive Is AI-Generated Argumentation? An Analysis of the Quality of an Argumentative Text Produced by the GPT-3 AI Text Generator”, Martin Hinton, Jean H. M. Wagemans -
https://arxiv.org/abs/2209.03320: “What Does a Platypus Look Like? Generating Customized Prompts for Zero-Shot Image Classification (CuPL)”, Sarah Pratt, Rosanne Liu, Ali Farhadi -
2022-gpt3.pdf#page=2: “Can GPT-3 Write an Academic Paper on Itself, With Minimal Human Input?”, GPT-3, Almira Osmanovic-Thunström, Steinn Steingrimsson -
https://arxiv.org/abs/2205.12910#allen: “NaturalProver: Grounded Mathematical Proof Generation With Language Models”, Sean Welleck, Jiacheng Liu, Ximing Lu, Hannaneh Hajishirzi, Yejin Choi -
https://arxiv.org/abs/2202.12837#facebook: “Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?”, Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, Luke Zettlemoyer -
https://arxiv.org/abs/2201.05320#allen: “CommonsenseQA 2.0: Exposing the Limits of AI through Gamification”, Alon Talmor, Ori Yoran, Ronan Le Bras, Chandra Bhagavatula, Yoav Goldberg, Yejin Choi, Jonathan Berant -
2022-tu.pdf: “Limits of Using Artificial Intelligence and GPT-3 in Patent Prosecution”, Sean Tu, Amy Cyphert, Sam Perl -
https://aclanthology.org/2021.mrqa-1.7.pdf: “What Can a Generative Language Model Answer About a Passage?”, Douglas Summers-Stay, Claire Bonial, Clare Voss -
https://arxiv.org/abs/2010.14701#openai: “Scaling Laws for Autoregressive Generative Modeling”, -
https://arxiv.org/abs/2009.03300: “MMLU: Measuring Massive Multitask Language Understanding”, Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, Jacob Steinhardt