- See Also
- Gwern
-
Links
- “ChatGPT Can Predict the Future When It Tells Stories Set in the Future About the Past”, Pham & Cunningham 2024
- “Visualization-Of-Thought Elicits Spatial Reasoning in Large Language Models”, Wu et al 2024
- “Do Language Models Plan Ahead for Future Tokens?”, 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”, Martínez 2024
- “RNNs Are Not Transformers (Yet): The Key Bottleneck on In-Context Retrieval”, Wen et al 2024
- “Tokenization Counts: the Impact of Tokenization on Arithmetic in Frontier LLMs”, Singh & Strouse 2024
- “Why Are Sensitive Functions Hard for Transformers?”, Hahn & Rofin 2024
- “V-STaR: Training Verifiers for Self-Taught Reasoners”, Hosseini et al 2024
- “More Agents Is All You Need”, Li et al 2024
- “Beyond Human Data: Scaling Self-Training for Problem-Solving With Language Models (ReSTEM)”, Singh et al 2023
- “Universal Self-Consistency for Large Language Model Generation”, Chen et al 2023
- “Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine”, Nori et al 2023
- “Training Chain-Of-Thought via Latent-Variable Inference”, Phan et al 2023
- “Zero-Shot Goal-Directed Dialogue via RL on Imagined Conversations”, Hong et al 2023
- “Large Language Models Can Strategically Deceive Their Users When Put Under Pressure”, Scheurer et al 2023
- “Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation”, Ding et al 2023
- “Implicit Chain-Of-Thought Reasoning via Knowledge Distillation”, Deng et al 2023
- “Branch-Solve-Merge Improves Large Language Model Evaluation and Generation”, Saha et al 2023
- “Can GPT Models Be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on Mock CFA Exams”, Callanan et al 2023
- “The Expressive Power of Transformers With Chain-Of-Thought”, Merrill & Sabharwal 2023
- “Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models”, Zhou et al 2023
- “Embers of Autoregression: Understanding Large Language Models Through the Problem They Are Trained to Solve”, McCoy et al 2023
- “Contrastive Decoding Improves Reasoning in Large Language Models”, O’Brien & Lewis 2023
- “From Sparse to Dense: GPT-4 Summarization With Chain of Density (CoD) Prompting”, Adams et al 2023
- “Solving Challenging Math Word Problems Using GPT-4 Code Interpreter With Code-Based Self-Verification”, Zhou et al 2023
- “Android in the Wild: A Large-Scale Dataset for Android Device Control”, Rawles et al 2023
- “LLMs As Workers in Human-Computational Algorithms? Replicating Crowdsourcing Pipelines With LLMs”, Wu et al 2023
- “TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT”, Zha et al 2023
- “Explaining Competitive-Level Programming Solutions Using LLMs”, Li et al 2023
- “Teaching Arithmetic to Small Transformers”, Lee et al 2023
- “Let’s Do a Thought Experiment: Using Counterfactuals to Improve Moral Reasoning”, Ma et al 2023
- “GKD: Generalized Knowledge Distillation for Auto-Regressive Sequence Models”, Agarwal et al 2023
- “Large Language Models As Tax Attorneys: A Case Study in Legal Capabilities Emergence”, Nay et al 2023
- “Iterative Translation Refinement With Large Language Models”, Chen et al 2023
- “Thought Cloning: Learning to Think While Acting by Imitating Human Thinking”, Hu & Clune 2023
- “Let’s Verify Step by Step”, Lightman et al 2023
- “Improving Factuality and Reasoning in Language Models through Multiagent Debate”, Du et al 2023
- “How Language Model Hallucinations Can Snowball”, Zhang et al 2023
- “Tree of Thoughts (ToT): Deliberate Problem Solving With Large Language Models”, Yao et al 2023
- “Large Language Model Programs”, Schlag et al 2023
- “Language Models Don’t Always Say What They Think: Unfaithful Explanations in Chain-Of-Thought Prompting”, Turpin et al 2023
- “Distilling Step-By-Step! Outperforming Larger Language Models With Less Training Data and Smaller Model Sizes”, Hsieh et al 2023
- “Decomposition Enhances Reasoning via Self-Evaluation Guided Decoding”, Xie et al 2023
- “LLM+P: Empowering Large Language Models With Optimal Planning Proficiency”, Liu et al 2023
- “Boosting Theory-Of-Mind Performance in Large Language Models via Prompting”, Moghaddam & Honey 2023
- “Think Before You Act: Unified Policy for Interleaving Language Reasoning With Actions”, Mezghani et al 2023
- “Language Models Can Solve Computer Tasks”, Kim et al 2023
- “How Well Do Large Language Models Perform in Arithmetic Tasks?”, Yuan et al 2023
- “SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models”, Manakul et al 2023
- “Language Is Not All You Need: Aligning Perception With Language Models (Kosmos-1)”, Huang et al 2023
- “Multimodal Chain-Of-Thought Reasoning in Language Models”, Zhang et al 2023
- “Faithful Chain-Of-Thought Reasoning”, Lyu et al 2023
- “Large Language Models Are Versatile Decomposers: Decompose Evidence and Questions for Table-Based Reasoning”, Ye et al 2023
- “ChatGPT Goes to Law School”, Choi et al 2023
- “Large Language Models As Fiduciaries: A Case Study Toward Robustly Communicating With Artificial Intelligence Through Legal Standards”, Nay 2023
- “Interactive-Chain-Prompting (INTERCPT): Ambiguity Resolution for Crosslingual Conditional Generation With Interaction”, Pilault et al 2023
- “Solving Math Word Problems With Process & Outcome-Based Feedback”, Uesato et al 2022
- “PAL: Program-Aided Language Models”, Gao et al 2022
- “Measuring Progress on Scalable Oversight for Large Language Models”, Bowman et al 2022
- “U-PaLM: Transcending Scaling Laws With 0.1% Extra Compute”, Tay et al 2022
- “Large Language Models Can Self-Improve”, Huang et al 2022
- “Challenging BIG-Bench Tasks (BBH) and Whether Chain-Of-Thought Can Solve Them”, Suzgun et al 2022
- “Self-Ask: Measuring and Narrowing the Compositionality Gap in Language Models (Bamboogle)”, Press et al 2022
- “Language Models Are Multilingual Chain-Of-Thought Reasoners”, Shi et al 2022
- “ReAct: Synergizing Reasoning and Acting in Language Models”, Yao et al 2022
- “Dynamic Prompt Learning via Policy Gradient for Semi-Structured Mathematical Reasoning”, Lu et al 2022
- “FOLIO: Natural Language Reasoning With First-Order Logic”, Han et al 2022
- “Faithful Reasoning Using Large Language Models”, Creswell & Shanahan 2022
- “Limitations of Language Models in Arithmetic and Symbolic Induction”, Qian et al 2022
- “Language Models Can Teach Themselves to Program Better”, Haluptzok et al 2022
- “Language Model Cascades”, Dohan et al 2022
- “CodeT: Code Generation With Generated Tests”, Chen et al 2022
- “Can Large Language Models Reason about Medical Questions?”, Liévin et al 2022
- “Inner Monologue: Embodied Reasoning through Planning With Language Models”, Huang et al 2022
- “Exploring Length Generalization in Large Language Models”, Anil et al 2022
- “Language Models (Mostly) Know What They Know”, Kadavath et al 2022
- “Solving Quantitative Reasoning Problems With Language Models”, Lewkowycz et al 2022
- “Maieutic Prompting: Logically Consistent Reasoning With Recursive Explanations”, Jung et al 2022
- “Large Language Models Are Zero-Shot Reasoners”, Kojima et al 2022
- “Instruction Induction: From Few Examples to Natural Language Task Descriptions”, Honovich et al 2022
- “Least-To-Most Prompting Enables Complex Reasoning in Large Language Models”, Zhou et al 2022
- “Dialog Inpainting: Turning Documents into Dialogues”, Dai et al 2022
- “Unifying Language Learning Paradigms”, Tay et al 2022
- “Can Language Models Learn from Explanations in Context?”, Lampinen et al 2022
- “Socratic Models: Composing Zero-Shot Multimodal Reasoning With Language”, Zeng et al 2022
- “STaR: Bootstrapping Reasoning With Reasoning”, Zelikman et al 2022
- “A Conversational Paradigm for Program Synthesis”, Nijkamp et al 2022
- “Self-Consistency Improves Chain-Of-Thought Reasoning in Language Models”, Wang et al 2022
- “Learning-By-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension”, Zhao et al 2022
- “PromptChainer: Chaining Large Language Model Prompts through Visual Programming”, Wu et al 2022
- “Chain-Of-Thought Prompting Elicits Reasoning in Large Language Models”, Wei et al 2022
- “Reasoning Like Program Executors”, Pi et al 2022
- “A Neural Network Solves and Generates Mathematics Problems by Program Synthesis: Calculus, Differential Equations, Linear Algebra, and More”, Drori et al 2021
- “DREAM: Uncovering Mental Models behind Language Models”, Gu et al 2021
- “Reframing Human-AI Collaboration for Generating Free-Text Explanations”, Wiegreffe et al 2021
- “NeuroLogic A✱esque Decoding: Constrained Text Generation With Lookahead Heuristics”, Lu et al 2021
- “WebGPT: Improving the Factual Accuracy of Language Models through Web Browsing”, Hilton et al 2021
- “Few-Shot Self-Rationalization With Natural Language Prompts”, Marasović et al 2021
- “Training Verifiers to Solve Math Word Problems”, Cobbe et al 2021
- “Unsupervised Neural Machine Translation With Generative Language Models Only”, Han et al 2021
- “Show Your Work: Scratchpads for Intermediate Computation With Language Models”, Nye et al 2021
- “AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts”, Wu et al 2021
- “Teaching Autoregressive Language Models Complex Tasks By Demonstration”, Recchia 2021
- “Program Synthesis With Large Language Models”, Austin et al 2021
- “Decision Transformer: Reinforcement Learning via Sequence Modeling”, Chen et al 2021
- “Explainable Multi-Hop Verbal Reasoning Through Internal Monologue”, Liang et al 2021
- “A Simple Method to Keep GPT-3 Focused in a Conversation”, Mayne 2021
- “Measuring Mathematical Problem Solving With the MATH Dataset”, Hendrycks et al 2021
- “Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm”, Reynolds & McDonell 2021
- “How We Accidentally Gave Our Bots Their Personalities”, Latitude 2021
- “Word in Context: Agent and Agent Clarification (69% Dev)”, Brockman 2020
- “I Found That Getting GPT-3 to Add Its Own "Internal Monologue" in Parentheses to Be a Helpful Strategy…”, blixt 2020
- kleptid @ "2020-07-17"
- kleptid @ "2020-07-17"
- “Inducing Self-Explanation: a Meta-Analysis”, Bisra et al 2018
- “Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems”, Ling et al 2017
- “Why Do Humans Reason? Arguments for an Argumentative Theory”, Mercier & Sperber 2011
- “How to Dramatically Improve the Reasoning Ability of GPT-3”
- “AI Dungeon Players Can Now Translate Their Stories into Emojis by Just Clicking a Button.”
- “Solving Math Word Problems: We’ve Trained a System That Solves Grade School Math Problems With Nearly Twice the Accuracy of a Fine-Tuned GPT-3 Model. It Solves about 90% As Many Problems As Real Kids: a Small Sample of 9-12 Year Olds Scored 60% on a Test from Our Dataset, While Our System Scored 55% on Those Same Problems. This Is Important Because Today’s AI Is Still Quite Weak at Commonsense Multistep Reasoning, Which Is Easy Even for Grade School Kids. We Achieved These Results by Training Our Model to Recognize Its Mistakes, so That It Can Try Repeatedly Until It Finds a Solution That Works”
- Sort By Magic
- Wikipedia
- Miscellaneous
- Link Bibliography
See Also
Gwern
“Free-Play Periods for RL Agents”, Gwern 2023
“It Looks Like You’re Trying To Take Over The World”, Gwern 2022
Links
“ChatGPT Can Predict the Future When It Tells Stories Set in the Future About the Past”, Pham & Cunningham 2024
ChatGPT Can Predict the Future when it Tells Stories Set in the Future About the Past
“Visualization-Of-Thought Elicits Spatial Reasoning in Large Language Models”, Wu et al 2024
Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models
“Do Language Models Plan Ahead for Future Tokens?”, Wu et al 2024
“FABLES: Evaluating Faithfulness and Content Selection in Book-Length Summarization”, Kim et al 2024
FABLES: Evaluating faithfulness and content selection in book-length summarization
“Re-Evaluating GPT-4’s Bar Exam Performance”, Martínez 2024
“RNNs Are Not Transformers (Yet): The Key Bottleneck on In-Context Retrieval”, Wen et al 2024
RNNs are not Transformers (Yet): The Key Bottleneck on In-context Retrieval
“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
“Why Are Sensitive Functions Hard for Transformers?”, Hahn & Rofin 2024
“V-STaR: Training Verifiers for Self-Taught Reasoners”, Hosseini et al 2024
“More Agents Is All You Need”, Li et al 2024
“Beyond Human Data: Scaling Self-Training for Problem-Solving With Language Models (ReSTEM)”, Singh et al 2023
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models (ReSTEM)
“Universal Self-Consistency for Large Language Model Generation”, Chen et al 2023
Universal Self-Consistency for Large Language Model Generation
“Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine”, Nori et al 2023
Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
“Training Chain-Of-Thought via Latent-Variable Inference”, Phan et al 2023
“Zero-Shot Goal-Directed Dialogue via RL on Imagined Conversations”, Hong et al 2023
Zero-Shot Goal-Directed Dialogue via RL on Imagined Conversations
“Large Language Models Can Strategically Deceive Their Users When Put Under Pressure”, Scheurer et al 2023
Large Language Models can Strategically Deceive their Users when Put Under Pressure
“Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation”, Ding et al 2023
Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation
“Implicit Chain-Of-Thought Reasoning via Knowledge Distillation”, Deng et al 2023
Implicit Chain-of-Thought Reasoning via Knowledge Distillation
“Branch-Solve-Merge Improves Large Language Model Evaluation and Generation”, Saha et al 2023
Branch-Solve-Merge Improves Large Language Model Evaluation and Generation
“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
“The Expressive Power of Transformers With Chain-Of-Thought”, Merrill & Sabharwal 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
“Embers of Autoregression: Understanding Large Language Models Through the Problem They Are Trained to Solve”, McCoy et al 2023
“Contrastive Decoding Improves Reasoning in Large Language Models”, O’Brien & Lewis 2023
Contrastive Decoding Improves Reasoning in Large Language Models
“From Sparse to Dense: GPT-4 Summarization With Chain of Density (CoD) Prompting”, Adams et al 2023
From Sparse to Dense: GPT-4 Summarization with Chain of Density (CoD) Prompting
“Solving Challenging Math Word Problems Using GPT-4 Code Interpreter With Code-Based Self-Verification”, Zhou et al 2023
“Android in the Wild: A Large-Scale Dataset for Android Device Control”, Rawles et al 2023
Android in the Wild: A Large-Scale Dataset for Android Device Control
“LLMs As Workers in Human-Computational Algorithms? Replicating Crowdsourcing Pipelines With LLMs”, Wu et al 2023
LLMs as Workers in Human-Computational Algorithms? Replicating Crowdsourcing Pipelines with LLMs
“TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT”, Zha et al 2023
TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT
“Explaining Competitive-Level Programming Solutions Using LLMs”, Li et al 2023
Explaining Competitive-Level Programming Solutions using LLMs
“Teaching Arithmetic to Small Transformers”, Lee et al 2023
“Let’s Do a Thought Experiment: Using Counterfactuals to Improve Moral Reasoning”, Ma et al 2023
Let’s Do a Thought Experiment: Using Counterfactuals to Improve Moral Reasoning
“GKD: Generalized Knowledge Distillation for Auto-Regressive Sequence Models”, Agarwal et al 2023
GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models
“Large Language Models As Tax Attorneys: A Case Study in Legal Capabilities Emergence”, Nay et al 2023
Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities Emergence
“Iterative Translation Refinement With Large Language Models”, Chen et al 2023
“Thought Cloning: Learning to Think While Acting by Imitating Human Thinking”, Hu & Clune 2023
Thought Cloning: Learning to Think while Acting by Imitating Human Thinking
“Let’s Verify Step by Step”, Lightman et al 2023
“Improving Factuality and Reasoning in Language Models through Multiagent Debate”, Du et al 2023
Improving Factuality and Reasoning in Language Models through Multiagent Debate
“How Language Model Hallucinations Can Snowball”, Zhang et al 2023
“Tree of Thoughts (ToT): Deliberate Problem Solving With Large Language Models”, Yao et al 2023
Tree of Thoughts (ToT): Deliberate Problem Solving with Large Language Models
“Large Language Model Programs”, Schlag et al 2023
“Language Models Don’t Always Say What They Think: Unfaithful Explanations in Chain-Of-Thought Prompting”, Turpin et al 2023
“Distilling Step-By-Step! Outperforming Larger Language Models With Less Training Data and Smaller Model Sizes”, Hsieh et al 2023
“Decomposition Enhances Reasoning via Self-Evaluation Guided Decoding”, Xie et al 2023
Decomposition Enhances Reasoning via Self-Evaluation Guided Decoding
“LLM+P: Empowering Large Language Models With Optimal Planning Proficiency”, Liu et al 2023
LLM+P: Empowering Large Language Models with Optimal Planning Proficiency
“Boosting Theory-Of-Mind Performance in Large Language Models via Prompting”, Moghaddam & Honey 2023
Boosting Theory-of-Mind Performance in Large Language Models via Prompting
“Think Before You Act: Unified Policy for Interleaving Language Reasoning With Actions”, Mezghani et al 2023
Think Before You Act: Unified Policy for Interleaving Language Reasoning with Actions
“Language Models Can Solve Computer Tasks”, Kim et al 2023
“How Well Do Large Language Models Perform in Arithmetic Tasks?”, Yuan et al 2023
How well do Large Language Models perform in Arithmetic tasks?
“SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models”, Manakul et al 2023
SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
“Language Is Not All You Need: Aligning Perception With Language Models (Kosmos-1)”, Huang et al 2023
Language Is Not All You Need: Aligning Perception with Language Models (Kosmos-1)
“Multimodal Chain-Of-Thought Reasoning in Language Models”, Zhang et al 2023
“Faithful Chain-Of-Thought Reasoning”, Lyu et al 2023
“Large Language Models Are Versatile Decomposers: Decompose Evidence and Questions for Table-Based Reasoning”, Ye et al 2023
“ChatGPT Goes to Law School”, Choi et al 2023
“Large Language Models As Fiduciaries: A Case Study Toward Robustly Communicating With Artificial Intelligence Through Legal Standards”, Nay 2023
“Interactive-Chain-Prompting (INTERCPT): Ambiguity Resolution for Crosslingual Conditional Generation With Interaction”, Pilault et al 2023
“Solving Math Word Problems With Process & Outcome-Based Feedback”, Uesato et al 2022
Solving math word problems with process & outcome-based feedback
“PAL: Program-Aided Language Models”, Gao et al 2022
“Measuring Progress on Scalable Oversight for Large Language Models”, Bowman et al 2022
Measuring Progress on Scalable Oversight for Large Language Models
“U-PaLM: Transcending Scaling Laws With 0.1% Extra Compute”, Tay et al 2022
“Large Language Models Can Self-Improve”, Huang et al 2022
“Challenging BIG-Bench Tasks (BBH) and Whether Chain-Of-Thought Can Solve Them”, Suzgun et al 2022
Challenging BIG-Bench Tasks (BBH) and Whether Chain-of-Thought Can Solve Them
“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)
“Language Models Are Multilingual Chain-Of-Thought Reasoners”, Shi et al 2022
“ReAct: Synergizing Reasoning and Acting in Language Models”, Yao et al 2022
“Dynamic Prompt Learning via Policy Gradient for Semi-Structured Mathematical Reasoning”, Lu et al 2022
Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning
“FOLIO: Natural Language Reasoning With First-Order Logic”, Han et al 2022
“Faithful Reasoning Using Large Language Models”, Creswell & Shanahan 2022
“Limitations of Language Models in Arithmetic and Symbolic Induction”, Qian et al 2022
Limitations of Language Models in Arithmetic and Symbolic Induction
“Language Models Can Teach Themselves to Program Better”, Haluptzok et al 2022
“Language Model Cascades”, Dohan et al 2022
“CodeT: Code Generation With Generated Tests”, Chen et al 2022
“Can Large Language Models Reason about Medical Questions?”, Liévin et al 2022
“Inner Monologue: Embodied Reasoning through Planning With Language Models”, Huang et al 2022
Inner Monologue: Embodied Reasoning through Planning with Language Models
“Exploring Length Generalization in Large Language Models”, Anil et al 2022
“Language Models (Mostly) Know What They Know”, Kadavath et al 2022
“Solving Quantitative Reasoning Problems With Language Models”, Lewkowycz et al 2022
Solving Quantitative Reasoning Problems with Language Models
“Maieutic Prompting: Logically Consistent Reasoning With Recursive Explanations”, Jung et al 2022
Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations
“Large Language Models Are Zero-Shot Reasoners”, Kojima et al 2022
“Instruction Induction: From Few Examples to Natural Language Task Descriptions”, Honovich et al 2022
Instruction Induction: From Few Examples to Natural Language Task Descriptions
“Least-To-Most Prompting Enables Complex Reasoning in Large Language Models”, Zhou et al 2022
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models
“Dialog Inpainting: Turning Documents into Dialogues”, Dai et al 2022
“Unifying Language Learning Paradigms”, Tay et al 2022
“Can Language Models Learn from Explanations in Context?”, Lampinen et al 2022
“Socratic Models: Composing Zero-Shot Multimodal Reasoning With Language”, Zeng et al 2022
Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language
“STaR: Bootstrapping Reasoning With Reasoning”, Zelikman et al 2022
“A Conversational Paradigm for Program Synthesis”, Nijkamp et al 2022
“Self-Consistency Improves Chain-Of-Thought Reasoning in Language Models”, Wang et al 2022
Self-Consistency Improves Chain-of-Thought Reasoning in Language Models
“Learning-By-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension”, Zhao et al 2022
Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension
“PromptChainer: Chaining Large Language Model Prompts through Visual Programming”, Wu et al 2022
PromptChainer: Chaining Large Language Model Prompts through Visual Programming
“Chain-Of-Thought Prompting Elicits Reasoning in Large Language Models”, Wei et al 2022
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
“Reasoning Like Program Executors”, Pi et al 2022
“A Neural Network Solves and Generates Mathematics Problems by Program Synthesis: Calculus, Differential Equations, Linear Algebra, and More”, Drori et al 2021
“DREAM: Uncovering Mental Models behind Language Models”, Gu et al 2021
“Reframing Human-AI Collaboration for Generating Free-Text Explanations”, Wiegreffe et al 2021
Reframing Human-AI Collaboration for Generating Free-Text Explanations
“NeuroLogic A✱esque Decoding: Constrained Text Generation With Lookahead Heuristics”, Lu et al 2021
NeuroLogic A✱esque Decoding: Constrained Text Generation with Lookahead Heuristics
“WebGPT: Improving the Factual Accuracy of Language Models through Web Browsing”, Hilton et al 2021
WebGPT: Improving the factual accuracy of language models through web browsing
“Few-Shot Self-Rationalization With Natural Language Prompts”, Marasović et al 2021
“Training Verifiers to Solve Math Word Problems”, Cobbe et al 2021
“Unsupervised Neural Machine Translation With Generative Language Models Only”, Han et al 2021
Unsupervised Neural Machine Translation with Generative Language Models Only
“Show Your Work: Scratchpads for Intermediate Computation With Language Models”, Nye et al 2021
Show Your Work: Scratchpads for Intermediate Computation with Language Models
“AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts”, Wu et al 2021
“Teaching Autoregressive Language Models Complex Tasks By Demonstration”, Recchia 2021
Teaching Autoregressive Language Models Complex Tasks By Demonstration
“Program Synthesis With Large Language Models”, Austin et al 2021
“Decision Transformer: Reinforcement Learning via Sequence Modeling”, Chen et al 2021
Decision Transformer: Reinforcement Learning via Sequence Modeling
“Explainable Multi-Hop Verbal Reasoning Through Internal Monologue”, Liang et al 2021
Explainable Multi-hop Verbal Reasoning Through Internal Monologue
“A Simple Method to Keep GPT-3 Focused in a Conversation”, Mayne 2021
“Measuring Mathematical Problem Solving With the MATH Dataset”, Hendrycks et al 2021
Measuring Mathematical Problem Solving With the MATH Dataset
“Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm”, Reynolds & McDonell 2021
Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm
“How We Accidentally Gave Our Bots Their Personalities”, Latitude 2021
“Word in Context: Agent and Agent Clarification (69% Dev)”, Brockman 2020
“I Found That Getting GPT-3 to Add Its Own "Internal Monologue" in Parentheses to Be a Helpful Strategy…”, blixt 2020
kleptid @ "2020-07-17"
kleptid @ "2020-07-17"
Teaching GPT-3 to do a brute force 'for loop' checking answers also seems to work
“Inducing Self-Explanation: a Meta-Analysis”, Bisra et al 2018
“Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems”, Ling et al 2017
Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems
“Why Do Humans Reason? Arguments for an Argumentative Theory”, Mercier & Sperber 2011
“How to Dramatically Improve the Reasoning Ability of GPT-3”
“AI Dungeon Players Can Now Translate Their Stories into Emojis by Just Clicking a Button.”
AI Dungeon players can now translate their stories into emojis by just clicking a button.
“Solving Math Word Problems: We’ve Trained a System That Solves Grade School Math Problems With Nearly Twice the Accuracy of a Fine-Tuned GPT-3 Model. It Solves about 90% As Many Problems As Real Kids: a Small Sample of 9-12 Year Olds Scored 60% on a Test from Our Dataset, While Our System Scored 55% on Those Same Problems. This Is Important Because Today’s AI Is Still Quite Weak at Commonsense Multistep Reasoning, Which Is Easy Even for Grade School Kids. We Achieved These Results by Training Our Model to Recognize Its Mistakes, so That It Can Try Repeatedly Until It Finds a Solution That Works”
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.
self-explanation
strategic-deception
thought-chain
stepwise-verification
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Wikipedia
Miscellaneous
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/doc/ai/nn/transformer/gpt/inner-monologue/2023-lee-figure1-numberformattingforgpt2arithmetic.png -
/doc/ai/nn/transformer/gpt/inner-monologue/2022-05-28-gpt3user-thinkingisallyouneed.html: -
/doc/ai/nn/transformer/gpt/inner-monologue/2022-tay-ul2-innermonologueresults.png: -
/doc/ai/nn/transformer/gpt/inner-monologue/2022-wei-figure8-lamdavsgpt3.png: -
/doc/ai/nn/transformer/gpt/inner-monologue/2022-zeng-figure2-socraticmodelsworkflowoverview.png: -
https://blog.eleuther.ai/factored-cognition/:View External Link:
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https://blog.research.google/2022/06/minerva-solving-quantitative-reasoning.html -
https://blog.research.google/2023/01/google-research-2022-beyond-language.html -
https://builtin.com/job/customer-success/expert-ai-teacher-contract/1267315 -
https://generative.ink/posts/methods-of-prompt-programming/#serializing-reasoning: -
https://gist.github.com/brockmanmatt/deafb4dba7e4399327e44f2c8fd97b2b -
https://github.com/openai/openai-cookbook/blob/main/techniques_to_improve_reliability.md: -
https://jxnl.github.io/instructor/blog/2023/11/05/chain-of-density/ -
https://reiinakano.com/2019/11/12/solving-probability.html: -
https://statmodeling.stat.columbia.edu/2023/08/30/chatgpt-4-can-do-3-digit-multiplication/: -
https://towardsdatascience.com/1-1-3-wait-no-1-1-2-how-to-have-gpt-sanity-check-itself-136e846987bf: -
https://twitter.com/DaveMonlander/status/1612802240582135809 -
https://twitter.com/KevinAFischer/status/1646018246225846272: -
https://twitter.com/KevinAFischer/status/1646677902833102849 -
https://twitter.com/KevinAFischer/status/1646690838981005312 -
https://twitter.com/StudentInfosec/status/1640360234882310145 -
https://twitter.com/andrewwhite01/status/1616933106786738176 -
https://twitter.com/jd_pressman/status/1646766004637401088: -
https://twitter.com/jmilldotdev/status/1592288240861839360: -
https://twitter.com/peterwildeford/status/1522633978305560576: -
https://twitter.com/yoheinakajima/status/1670557048743010305 -
https://www.fhi.ox.ac.uk/wp-content/uploads/2021/08/QNRs_FHI-TR-2021-3.0.pdf -
https://www.lesswrong.com/posts/yDcMDJeSck7SuBs24/steganography-in-chain-of-thought-reasoning -
https://www.lesswrong.com/posts/zRn6cLtxyNodudzhw/visible-thoughts-project-and-bounty-announcement -
https://www.patterns.app/blog/2023/01/18/crunchbot-sql-analyst-gpt/ -
https://www.reddit.com/r/ChatGPT/comments/10zavbv/extending_chatgpt_with_some_additional_internal/: -
https://www.reddit.com/r/ChatGPT/comments/11anct1/its_easy_to_give_chatgpt_a_bonafide_consciousness/: -
https://www.waluigipurple.com/post/revising-poetry-with-gpt-4
Link Bibliography
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https://link.springer.com/article/10.1007/s10506-024-09396-9: “Re-Evaluating GPT-4’s Bar Exam Performance”, Eric Martínez -
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/2402.09963: “Why Are Sensitive Functions Hard for Transformers?”, Michael Hahn, Mark Rofin -
https://arxiv.org/abs/2402.05120#tencent: “More Agents Is All You Need”, Junyou Li, Qin Zhang, Yangbin Yu, Qiang Fu, Deheng Ye -
https://arxiv.org/abs/2312.06585#deepmind: “Beyond Human Data: Scaling Self-Training for Problem-Solving With Language Models (ReSTEM)”, -
https://arxiv.org/abs/2311.16452#microsoft: “Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine”, -
https://arxiv.org/abs/2312.02179: “Training Chain-Of-Thought via Latent-Variable Inference”, -
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/2310.04406: “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/2309.09117#facebook: “Contrastive Decoding Improves Reasoning in Large Language Models”, Sean O’Brien, Mike Lewis -
https://arxiv.org/abs/2309.04269: “From Sparse to Dense: GPT-4 Summarization With Chain of Density (CoD) Prompting”, Griffin Adams, Alexander Fabbri, Faisal Ladhak, Eric Lehman, Noémie Elhadad -
https://arxiv.org/abs/2308.07921: “Solving Challenging Math Word Problems Using GPT-4 Code Interpreter With Code-Based Self-Verification”, -
https://arxiv.org/abs/2307.03381: “Teaching Arithmetic to Small Transformers”, Nayoung Lee, Kartik Sreenivasan, Jason D. Lee, Kangwook Lee, Dimitris Papailiopoulos -
https://arxiv.org/abs/2306.14308#google: “Let’s Do a Thought Experiment: Using Counterfactuals to Improve Moral Reasoning”, Xiao Ma, Swaroop Mishra, Ahmad Beirami, Alex Beutel, Jilin Chen -
https://arxiv.org/abs/2306.00323: “Thought Cloning: Learning to Think While Acting by Imitating Human Thinking”, Shengran Hu, Jeff Clune -
https://arxiv.org/abs/2305.20050#openai: “Let’s Verify Step by Step”, -
https://arxiv.org/abs/2305.13534: “How Language Model Hallucinations Can Snowball”, Muru Zhang, Ofir Press, William Merrill, Alisa Liu, Noah Smith -
https://arxiv.org/abs/2305.10601#deepmind: “Tree of Thoughts (ToT): Deliberate Problem Solving With Large Language Models”, Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, Karthik Narasimhan -
https://arxiv.org/abs/2305.04388: “Language Models Don’t Always Say What They Think: Unfaithful Explanations in Chain-Of-Thought Prompting”, Miles Turpin, Julian Michael, Ethan Perez, Samuel R. Bowman -
https://arxiv.org/abs/2305.02301#google: “Distilling Step-By-Step! Outperforming Larger Language Models With Less Training Data and Smaller Model Sizes”, -
https://arxiv.org/abs/2304.11490: “Boosting Theory-Of-Mind Performance in Large Language Models via Prompting”, Shima Rahimi Moghaddam, Christopher J. Honey -
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://papers.ssrn.com/sol3/papers.cfm?abstract_id=4335905: “ChatGPT Goes to Law School”, Jonathan H. Choi, Kristin E. Hickman, Amy Monahan, Daniel Schwarcz -
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4335945: “Large Language Models As Fiduciaries: A Case Study Toward Robustly Communicating With Artificial Intelligence Through Legal Standards”, John Nay -
https://arxiv.org/abs/2210.11399#google: “U-PaLM: Transcending Scaling Laws With 0.1% Extra Compute”, -
https://arxiv.org/abs/2210.11610#google: “Large Language Models Can Self-Improve”, Jiaxin Huang, Shixiang Shane Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, Jiawei Han -
https://arxiv.org/abs/2210.09261#google: “Challenging BIG-Bench Tasks (BBH) and Whether Chain-Of-Thought Can Solve Them”, -
https://arxiv.org/abs/2210.03350#allen: “Self-Ask: Measuring and Narrowing the Compositionality Gap in Language Models (Bamboogle)”, -
https://arxiv.org/abs/2210.03057#google: “Language Models Are Multilingual Chain-Of-Thought Reasoners”, -
https://arxiv.org/abs/2209.00840: “FOLIO: Natural Language Reasoning With First-Order Logic”, -
https://arxiv.org/abs/2207.08143: “Can Large Language Models Reason about Medical Questions?”, Valentin Liévin, Christoffer Egeberg Hother, Ole Winther -
https://arxiv.org/abs/2207.05608#google: “Inner Monologue: Embodied Reasoning through Planning With Language Models”, -
https://arxiv.org/abs/2207.05221#anthropic: “Language Models (Mostly) Know What They Know”, -
https://arxiv.org/abs/2205.10625#google: “Least-To-Most Prompting Enables Complex Reasoning in Large Language Models”, -
https://arxiv.org/abs/2205.09073#google: “Dialog Inpainting: Turning Documents into Dialogues”, Zhuyun Dai, Arun Tejasvi Chaganty, Vincent Zhao, Aida Amini, Qazi Mamunur Rashid, Mike Green, Kelvin Guu -
https://arxiv.org/abs/2205.05131#google: “Unifying Language Learning Paradigms”, -
https://arxiv.org/abs/2204.00598#google: “Socratic Models: Composing Zero-Shot Multimodal Reasoning With Language”, -
https://arxiv.org/abs/2203.11171#google: “Self-Consistency Improves Chain-Of-Thought Reasoning in Language Models”, Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Denny Zhou -
https://arxiv.org/abs/2201.11903#google: “Chain-Of-Thought Prompting Elicits Reasoning in Large Language Models”, Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, Denny Zhou -
https://arxiv.org/abs/2201.11473#microsoft: “Reasoning Like Program Executors”, Xinyu Pi, Qian Liu, Bei Chen, Morteza Ziyadi, Zeqi Lin, Yan Gao, Qiang Fu, Jian-Guang Lou, Weizhu Chen -
https://openai.com/research/webgpt: “WebGPT: Improving the Factual Accuracy of Language Models through Web Browsing”, Jacob Hilton, Suchir Balaji, Reiichiro Nakano, John Schulman -
https://arxiv.org/abs/2110.14168#openai: “Training Verifiers to Solve Math Word Problems”, Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, John Schulman -
https://sites.google.com/berkeley.edu/decision-transformer: “Decision Transformer: Reinforcement Learning via Sequence Modeling”, -
https://gptprompts.wikidot.com/linguistics:word-in-context#toc3: “Word in Context: Agent and Agent Clarification (69% Dev)”, Matt Brockman -
https://news.ycombinator.com/item?id=23990902: “I Found That Getting GPT-3 to Add Its Own "Internal Monologue" in Parentheses to Be a Helpful Strategy…”, blixt -
https://twitter.com/kleptid/status/1284069270603866113: “Seems to Work”, KaryoKleptid -
https://twitter.com/kleptid/status/1284098635689611264: “Teaching GPT-3 to Do a Brute Force 'For Loop' Checking Answers Also Seems to Work”, KaryoKleptid -
2018-bisra.pdf: “Inducing Self-Explanation: a Meta-Analysis”, Kiran Bisra, Qing Liu, John C. Nesbit, Farimah Salimi, Philip H. Winne