“‘GPT-4 Nonfiction’ Tag”,2022-10-17

Bibliography for tag ai/nn/transformer/gpt/4/nonfiction, most recent first: 174 annotations & 205 links (parent).

• See Also
• Gwern
  • “Abs-E (or, Speak Only in the Positive) § text2epositive.py Experiment”, Gwern2024
  • “text2epositive.py”, Gwern2024
  • “date-Guesser.py”, Gwern2024
  • “paragraphizer.py”, Gwern2022
  • “CQK Is The First Unused TLA”, Gwern2023
• Links
  • “Business Spending on AI Surged 500% This Year to $13.8 Billion”
  • “Generative Agent Simulations of 1,000 People”, Park et al 2024
  • “Hidden Persuaders: LLMs’ Political Leaning and Their Influence on Voters”, Potter et al 2024
  • “Can LLMs Be Scammed? A Baseline Measurement Study”, Sehwag et al 2024
  • “SimpleStrat: Diversifying Language Model Generation With Stratification”, Wong et al 2024
  • “MLE-Bench: Evaluating Machine Learning Agents on Machine Learning Engineering”, Chan et al 2024
  • “Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making”, Li et al 2024
  • “Can OpenAI’s o1-Preview Ace the 2023 Putnam Exam?”, Kabasares2024
  • “When a Language Model Is Optimized for Reasoning, Does It Still Show Embers of Autoregression? An Analysis of OpenAI O1”, McCoy et al 2024
  • “Invisible Unicode Text That AI Chatbots Understand and Humans Can’t? Yep, It’s a Thing”
  • “I Quit Teaching Because of ChatGPT”, Livingstone2024
  • “Evaluation of OpenAI O1: Opportunities and Challenges of AGI”, Zhong et al 2024
  • “That Message From Your Doctor? It May Have Been Drafted by ChatGPT-4”
  • “LLMs Still Can’t Plan; Can LRMs? A Preliminary Evaluation of OpenAI’s O1 on PlanBench”, Valmeekam et al 2024
  • “I Have Played a Little Bit With OpenAI’s New Iteration, GPT-4 O1”, Tao2024
  • “Thoughts While Watching Myself Be Automated”, Dynomight2024
  • “Generative AI Can Harm Learning”, Bastani et al 2024
  • “Does Refusal Training in LLMs Generalize to the Past Tense?”, Andriushchenko & Flammarion2024
  • “GPT-4 Is Judged More Human Than Humans in Displaced and Inverted Turing Tests”, Rathi et al 2024
  • “On Scalable Oversight With Weak LLMs Judging Strong LLMs”, Kenton et al 2024
  • “Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs”, Laine et al 2024
  • “Are Large Language Models Consistent over Value-Laden Questions?”, Moore et al 2024
  • “Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation”, Halawi et al 2024
  • “APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets”, Liu et al 2024
  • “A Real-World Test of Artificial Intelligence Infiltration of a University Examinations System: A ‘Turing Test’ Case Study”, Scarfe et al 2024
  • “Connecting the Dots: LLMs Can Infer and Verbalize Latent Structure from Disparate Training Data”, Treutlein et al 2024
  • “OlympicArena: Benchmarking Multi-Discipline Cognitive Reasoning for Superintelligent AI”, Huang et al 2024
  • “What Are the Odds? Language Models Are Capable of Probabilistic Reasoning”, Paruchuri et al 2024
  • “Probing the Decision Boundaries of In-Context Learning in Large Language Models”, Zhao et al 2024
  • “Development Cost of ARC GPT-4o Prototype”, Greenblatt2024
  • “GUI-WORLD: A Dataset for GUI-Oriented Multimodal LLM-Based Agents”, Chen et al 2024
  • “Are We Done With MMLU?”, Gema et al 2024
  • “Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-Modal LLMs in Video Analysis”, Fu et al 2024
  • “LLMs Achieve Adult Human Performance on Higher-Order Theory of Mind Tasks”, Street et al 2024
  • “Intelligent Go-Explore (IGE): Standing on the Shoulders of Giant Foundation Models”, Lu et al 2024
  • “DeTikZify: Synthesizing Graphics Programs for Scientific Figures and Sketches With TikZ”, Belouadi et al 2024
  • “DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data”, Xin et al 2024
  • “Grokked Transformers Are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalization”, Wang et al 2024
  • “Can Language Models Explain Their Own Classification Behavior?”, Sherburn et al 2024
  • “ChatGPT Will Be Able to Talk to You like Scarlett Johansson in Her / Upgrades to ChatGPT’s Voice Mode Bring It Closer to the Vision of a Responsive AI Assistant—And Sam Altman Seems to Know It”, Robison2024
  • “GSM1k: A Careful Examination of Large Language Model Performance on Grade School Arithmetic”, Zhang et al 2024
  • “Aligning LLM Agents by Learning Latent Preference from User Edits”, Gao et al 2024
  • “Automated Social Science: Language Models As Scientist and Subjects”, Manning et al 2024
  • “Enhancing Confidence Expression in Large Language Models Through Learning from Past Experience”, Han et al 2024
  • “LLM Evaluators Recognize and Favor Their Own Generations”, Panickssery et al 2024
  • “Do LLMs Play Dice? Exploring Probability Distribution Sampling in Large Language Models for Behavioral Simulation”, Gu et al 2024
  • “Is ChatGPT Transforming Academics’ Writing Style?”, Geng & Trotta2024
  • “From Words to Numbers: Your Large Language Model Is Secretly A Capable Regressor When Given In-Context Examples”, Vacareanu et al 2024
  • “Election Workers Are Drowning in Records Requests. AI Chatbots Could Make It Worse: Experts Worry That Election Deniers Could Weaponize Chatbots to Overwhelm and Slow down Local Officials”, Elliott2024
  • “Visualization-Of-Thought Elicits Spatial Reasoning in Large Language Models”, Wu et al 2024
  • “FABLES: Evaluating Faithfulness and Content Selection in Book-Length Summarization”, Kim et al 2024
  • “Re-Evaluating GPT-4’s Bar Exam Performance”, Martínez2024
  • “A Peter Thiel-Backed AI Startup, Cognition Labs, Seeks $2 Billion Valuation: Funding round Could Increase Startup’s Valuation Nearly Sixfold in a Matter of Weeks, Reflecting AI Frenzy”, Jin2024
  • “Vulnerability Detection With Code Language Models: How Far Are We?”, Ding et al 2024
  • “Long-Form Factuality in Large Language Models”, Wei et al 2024
  • “Gold-Medalist Coders Build an AI That Can Do Their Job for Them: A New Startup Called Cognition AI Can Turn a User’s Prompt into a Website or Video Game”, Vance2024
  • “Playing NetHack With LLMs: Potential & Limitations As Zero-Shot Agents (NetPlay)”, Jeurissen et al 2024
  • “Teaching Large Language Models an Unseen Language on the Fly”, Zhang et al 2024
  • “Functional Benchmarks for Robust Evaluation of Reasoning Performance, and the Reasoning Gap”, Srivastava et al 2024
  • “Tokenization Counts: the Impact of Tokenization on Arithmetic in Frontier LLMs”, Singh & Strouse2024
  • “ArtPrompt: ASCII Art-Based Jailbreak Attacks against Aligned LLMs”, Jiang et al 2024
  • “Tasks That Language Models Don’t Learn”, Lee & Lim2024
  • “Using Counterfactual Tasks to Evaluate the Generality of Analogical Reasoning in Large Language Models”, Lewis & Mitchell2024
  • “The Non-Effect of Sampling Temperature on Problem Solving in GPT-3.5/GPT-4”, Renze & Guven2024
  • “I Think, Therefore I Am: Benchmarking Awareness of Large Language Models Using AwareBench”, Li et al 2024
  • “Better Call GPT, Comparing Large Language Models Against Lawyers”, Martin et al 2024
  • “I Am a Strange Dataset: Metalinguistic Tests for Language Models”, Thrush et al 2024
  • “GPT-4-V(ision) Is a Human-Aligned Evaluator for Text-To-3D Generation”, Wu et al 2024
  • “A Vision Check-Up for Language Models”, Sharma et al 2024
  • “Leveraging Large Language Models to Boost Dafny’s Developers Productivity”, Silva et al 2024
  • “Originality Dies When Being Average Is Easier”
  • “Testing Theory of Mind in Large Language Models and Humans”
  • “GPT-4 Passes the Bar Exam”, Katz et al 2024
  • “Large Language Models Are Able to Downplay Their Cognitive Abilities to Fit the Persona They Simulate”, Milička et al 2024
  • “WaveCoder: Widespread And Versatile Enhanced Instruction Tuning With Refined Data Generation”, Yu et al 2023
  • “PRER: Modeling Complex Mathematical Reasoning via Large Language Model Based MathAgent”, Liao et al 2023
  • “Can Linguists Distinguish between ChatGPT and Human Writing?: A Study of Research Ethics and Academic Publishing”, Casal & Kessler2023
  • “Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine”, Nori et al 2023
  • “GPQA: A Graduate-Level Google-Proof Q&A Benchmark”, Rein et al 2023
  • 42irrationalist @ “2023-11-19”
  • “Llamas Know What GPTs Don’t Show: Surrogate Models for Confidence Estimation”, Shrivastava et al 2023
  • “Comparing Humans, GPT-4, and GPT-4-V On Abstraction and Reasoning Tasks”, Mitchell et al 2023
  • “In Search of the Long-Tail: Systematic Generation of Long-Tail Inferential Knowledge via Logical Rule Guided Search”, Li et al 2023
  • “The Impact of Large Language Models on Scientific Discovery: a Preliminary Study Using GPT-4”, AI4Science & Quantum2023
  • “Accuracy of a Vision-Language Model on Challenging Medical Cases”, Buckley et al 2023
  • “Large Language Models Can Strategically Deceive Their Users When Put Under Pressure”, Scheurer et al 2023
  • “Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves”, Deng et al 2023
  • “Augmenting Large Language Models With Chemistry Tools”, Bran et al 2023
  • “FANToM: A Benchmark for Stress-Testing Machine Theory of Mind in Interactions”, Kim et al 2023
  • “Branch-Solve-Merge Improves Large Language Model Evaluation and Generation”, Saha et al 2023
  • “Eureka: Human-Level Reward Design via Coding Large Language Models”, Ma et al 2023
  • “Set-Of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4-V”, Yang et al 2023
  • “Large Language Model Prediction Capabilities: Evidence from a Real-World Forecasting Tournament”, Schoenegger & Park2023
  • “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
  • “Large Language Models Can Replicate Cross-Cultural Differences in Personality”, Niszczota et al 2023
  • “Beyond Memorization: Violating Privacy Via Inference With Large Language Models”, Staab et al 2023
  • “SWE-Bench: Can Language Models Resolve Real-World GitHub Issues?”, Jimenez et al 2023
  • “Can a Computer Outfake a Human [Personality]?”, Phillips & Robie2023
  • “Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models”, Zhou et al 2023
  • “FreshLLMs: Refreshing Large Language Models With Search Engine Augmentation”, Vu et al 2023
  • “Police Officers Are Starting to Use AI to Write Crime Reports”
  • “Can Large Language Models Provide Useful Feedback on Research Papers? A Large-Scale Empirical Analysis”, Liang et al 2023
  • “Low-Resource Languages Jailbreak GPT-4”, Yong et al 2023
  • “An Evolutionary Model of Personality Traits Related to Cooperative Behavior Using a Large Language Model”, Suzuki & Arita2023
  • “UltraFeedback: Boosting Language Models With High-Quality Feedback”, Cui et al 2023
  • “MTOB: A Benchmark for Learning to Translate a New Language from One Grammar Book”, Tanzer 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
  • “The Reversal Curse: LLMs Trained on “A Is B” Fail to Learn “B Is A””, Berglund et al 2023
  • “From Sparse to Dense: GPT-4 Summarization With Chain of Density (CoD) Prompting”, Adams et al 2023
  • “Devising and Detecting Phishing: Large Language Models vs. Smaller Human Models”, Heiding et al 2023
  • “ExpeL: LLM Agents Are Experiential Learners”, Zhao et al 2023
  • “LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models”, Guha et al 2023
  • “Solving Challenging Math Word Problems Using GPT-4 Code Interpreter With Code-Based Self-Verification”, Zhou et al 2023
  • “OpenAI Cribbed Our Tax Example, But Can GPT-4 Really Do Tax?”, Blair-Stanek et al 2023
  • “Testing GPT-4 With Wolfram Alpha and Code Interpreter Plug-Ins on Math and Science Problems”, Davis & Aaronson2023
  • “The ConceptARC Benchmark: Evaluating Understanding and Generalization in the ARC Domain”, Moskvichev et al 2023
  • “I’m a Screenwriter. These AI Jokes Give Me Nightmares”, Rich2023
  • “A LLM Assisted Exploitation of AI-Guardian”, Carlini2023
  • “OpenAI Worries About What Its Chatbot Will Say About People’s Faces: An Advanced Version of ChatGPT Can Analyze Images and Is Already Helping the Blind. But Its Ability to Put a Name to a Face Is One Reason the Public Doesn’t Have Access to It”, Hill2023
  • “GPT-4, an Artificial Intelligence Large Language Model, Exhibits High Levels of Accuracy on Dermatology Specialty Certificate Exam Questions”, Shetty et al 2023
  • “Machine-Assisted Social Psychology Hypothesis Generation”, Banker et al 2023
  • “Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events”, Gu et al 2023
  • “Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration”, Wang et al 2023
  • “Explaining Competitive-Level Programming Solutions Using LLMs”, Li et al 2023
  • “Hoodwinked: Deception and Cooperation in a Text-Based Game for Language Models”, O’Gara2023
  • “LeanDojo: Theorem Proving With Retrieval-Augmented Language Models”, Yang et al 2023
  • “ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews”, D’Arcy et al 2023
  • “Understanding Social Reasoning in Language Models With Language Models”, Gandhi et al 2023
  • “Evaluating Superhuman Models With Consistency Checks”, Fluri et al 2023
  • “Evaluating the Robustness of Text-To-Image Diffusion Models against Real-World Attacks”, Gao et al 2023
  • “ChessGPT: Bridging Policy Learning and Language Modeling”, Feng et al 2023
  • “Large Language Models As Tax Attorneys: A Case Study in Legal Capabilities Emergence”, Nay et al 2023
  • “Can Large Language Models Democratize Access to Dual-Use Biotechnology?”, Soice et al 2023
  • “Let’s Verify Step by Step”, Lightman et al 2023
  • “GPT4GEO: How a Language Model Sees the World’s Geography”, Roberts et al 2023
  • “LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-Based Representations”, Xu et al 2023
  • “Learning to Generate Novel Scientific Directions With Contextualized Literature-Based Discovery”, Wang et al 2023
  • “WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on Wikipedia”, Semnani et al 2023
  • “How Language Model Hallucinations Can Snowball”, Zhang et al 2023
  • “C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models”, Huang et al 2023
  • “Large Language Models Can Be Used To Effectively Scale Spear Phishing Campaigns”, Hazell2023
  • “Boosting Theory-Of-Mind Performance in Large Language Models via Prompting”, Moghaddam & Honey2023
  • “Today Was the First Day That I Could Definitively Say That GPT-4 Has Saved Me a Substantial Amount of Tedious Work”, Tao2023
  • “Humans in Humans Out: On GPT Converging Toward Common Sense in Both Success and Failure”, Koralus & Wang-Maścianica2023
  • “Advances in Apparent Conceptual Physics Reasoning in GPT-4”, West2023
  • “Performance of ChatGPT on Free-Response, Clinical Reasoning Exams”, Strong et al 2023
  • “Reflexion: Language Agents With Verbal Reinforcement Learning”, Shinn et al 2023
  • “How Well Do Large Language Models Perform in Arithmetic Tasks?”, Yuan et al 2023
  • “GPT-4 Technical Report § Limitations: Calibration”, OpenAI 2023 (page 12 org openai)
  • “Salesforce Announces Einstein GPT, the World’s First Generative AI for CRM”, Salesforce2023
  • “Large Language Models Are State-Of-The-Art Evaluators of Translation Quality”, Kocmi & Federmann2023
  • “Not What You’ve Signed up For: Compromising Real-World LLM-Integrated Applications With Indirect Prompt Injection”, Greshake et al 2023
  • “Harvey, Which Uses AI to Answer Legal Questions, Lands Cash from OpenAI”, Wiggers2022
  • “Janus”
  • “Something Weird Is Happening With LLMs and Chess”, Dynomight2024
  • “Trading Off Compute in Training and Inference”
  • “A Basic Test of OpenAI’s Structured Output Feature against Financial Disclosure Reports and a Newspaper’s Police Blotter”
  • “Prompt Engineering Techniques With Azure OpenAI”
  • “LLM Powered Autonomous Agents”
  • “There’s a Running Theme in Here of Programming Problems LLMs Solve Where It’s…”
  • “Prompting Diverse Ideas: Increasing AI Idea Variance”
  • “OpenAI API § Prompt Caching”
  • “Situational Awareness and Out-Of-Context Reasoning § GPT-4-Base Has Non-Zero Longform Performance”, Evans2024
  • “I Finally Got ChatGPT to Sound like Me”, lsusr 2024
  • “Connecting the Dots: LLMs Can Infer & Verbalize Latent Structure from Training Data”
  • “How Good Are LLMs at Doing ML on an Unknown Dataset?”
  • “Language Models Model Us”
  • “The Case for More Ambitious Language Model Evals”
  • “What Kind of Writer Is ChatGPT?”
  • “AI Will Increase the Quantity—And Quality—Of Phishing Scams”
  • “Is Finetuning GPT-4o worth It?”
  • michael_nielsen
  • Sort By Magic
    • llm-evaluation llm-feedback multimodal-llms model-robustness instruction-tuning generative-assistance
    • knowledge-extraction
    • context-learning
    • translation-benchmarking
    • language-analytics
    • benchmarking
    • ai-startups, ethics-in-ai, automation-impact, legal-ai, turing-test
• Miscellaneous
• Bibliography