Awesome

[!IMPORTANT]

• Consider giving our repository a 🌟, so you will receive the latest news (paper list updates, new comments, etc.);
• If you want to cite our work, here is our bibtex entry: CITATION.bib.

📰 News

• 2024/10/26 We have created a relevant WeChat Group (微信群) for discussing reasoning and hallucination in LLMs.
• 2024/09/18 Paper v3.0 and a relevant Twitter thread.
• 2024/08/24 Updated paper list for better user experience. Link. Ongoing updates.
• 2024/07/22 Our paper ranks first on Hugging Face Daily Papers! Link.
• 2024/07/21 Our paper is now available on arXiv. Link.

🎉 Introduction

Welcome to the GitHub repository for our survey paper titled "Internal Consistency and Self-Feedback in Large Language Models: A Survey." The survey's goal is to provide a unified perspective on the self-evaluation and self-updating mechanisms in LLMs, encapsulated within the frameworks of Internal Consistency and Self-Feedback.

This repository includes three key resources:

• expt-consistency-types: Code and results for measuring consistency at different levels.
• expt-gpt4o-responses: Results from five different GPT-4o responses to the same query.
• Paper List: A comprehensive list of references related to our survey.

• 📰 News
• 🎉 Introduction
• 📚 Paper List
  • Related Survey Papers
  • Section IV: Consistency Signal Acquisition
    • Confidence Estimation
    • Hallucination Detection
    • Uncertainty Estimation
    • Verbal Critiquing
    • Faithfulness Measurement
    • Consistency Estimation
  • Section V: Reasoning Elevation
    • Reasoning Topologically
    • Refining with Responses
    • Multi-Agent Collaboration
  • Section VI: Hallucination Alleviation
    • Mitigating Hallucination while Generating
    • Refining the Response Iteratively
    • Activating Truthfulness
    • Decoding Truthfully
  • Section VII: Other Tasks
    • Preference Learning
    • Knowledge Distillation
    • Continuous Learning
    • Data Synthesis
    • Consistency Optimization
    • Decision Making
    • Event Argument Extraction
    • Inference Acceleration
    • Machine Translation
    • Negotiation Optimization
    • Retrieval Augmented Generation
    • Text Classification
  • Section VIII.A: Meta Evaluation
    • Consistency Evaluation
    • Self-Knowledge Evaluation
    • Uncertainty Evaluation
    • Feedback Ability Evaluation
  • Theoretical Perspectives
• 📝 Citation

📚 Paper List

Here we list the most important references cited in our survey, as well as the papers we consider worth noting. This list will be updated regularly.

Related Survey Papers

These are some of the most relevant surveys related to our paper.

• A Survey on the Honesty of Large Language Models
  CUHK, arXiv, 2024 [Paper] [Code]

• Awesome LLM Reasoning
  NTU, GitHub, 2024 [Code]

• Awesome LLM Strawberry
  NVIDIA, GitHub, 2024 [Code]

• Extrinsic Hallucinations in LLMs
  OpenAI, Blog, 2024 [Paper]

• When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs
  PSU, arXiv, 2024 [Paper]

• A Survey on Self-Evolution of Large Language Models
  PKU, arXiv, 2024 [Paper] [Code]

• Demystifying Chains, Trees, and Graphs of Thoughts
  ETH, arXiv, 2024 [Paper]

• Automatically Correcting Large Language Models: Surveying the Landscape of Diverse Automated Correction Strategies
  UCSB, TACL, 2024 [Paper] [Code]

• Uncertainty in Natural Language Processing: Sources, Quantification, and Applications
  Nankai, arXiv, 2023 [Paper]

Section IV: Consistency Signal Acquisition

For various forms of expressions from an LLM, we can obtain various forms of consistency signals, which can help in better updating the expressions.

Confidence Estimation

• Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs
  NUS, ICLR, 2024 [Paper] [Code]

• Linguistic Calibration of Long-Form Generations
  Stanford, ICML, 2024 [Paper] [Code]

• InternalInspector I2: Robust Confidence Estimation in LLMs through Internal States
  VT, arXiv, 2024 [Paper]

• Cycles of Thought: Measuring LLM Confidence through Stable Explanations
  UCLA, arXiv, 2024 [Paper]

• TrustScore: Reference-Free Evaluation of LLM Response Trustworthiness
  UoEdin, arXiv, 2024 [Paper] [Code]

• Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation
  Oxford, ICLR, 2023 [Paper] [Code]

• Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness
  UMD, arXiv, 2023 [Paper]

• Teaching models to express their uncertainty in words
  Oxford, TMLR, 2022 [Paper] [Code]

• Language Models (Mostly) Know What They Know
  Anthropic, arXiv, 2022 [Paper]

Hallucination Detection

• Detecting hallucinations in large language models using semantic entropy
  Oxford, Nature, 2024 [Paper]

• INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection
  Alibaba, ICLR, 2024 [Paper]

• LLM Internal States Reveal Hallucination Risk Faced With a Query
  HKUST, arXiv, 2024 [Paper]

• Teaching Large Language Models to Express Knowledge Boundary from Their Own Signals
  Fudan, arXiv, 2024 [Paper]

• Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method
  SDU, NAACL, 2024 [Paper] [Code]

• LM vs LM: Detecting Factual Errors via Cross Examination
  TAU, EMNLP, 2023 [Paper]

• SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
  Cambridge, EMNLP, 2023 [Paper] [Code]

Uncertainty Estimation

• Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models
  UIUC, TMLR, 2024 [Paper] [Code]

• Uncertainty Estimation of Large Language Models in Medical Question Answering
  HKU, arXiv, 2024 [Paper]

• To Believe or Not to Believe Your LLM
  Google, arXiv, 2024 [Paper]

• Shifting Attention to Relevance: Towards the Uncertainty Estimation of Large Language Models
  DU, ACL, 2024 [Paper] [Code]

• Active Prompting with Chain-of-Thought for Large Language Models
  HUST, arXiv, 2023 [Paper] [Code]

• Uncertainty Estimation in Autoregressive Structured Prediction
  Yandex, ICLR, 2021 [Paper]

• On Hallucination and Predictive Uncertainty in Conditional Language Generation
  UCSB, EACL, 2021 [Paper]

Verbal Critiquing

• LLM Critics Help Catch LLM Bugs
  OpenAI, arXiv, 2024 [Paper]

• Reasons to Reject? Aligning Language Models with Judgments
  Tencent, ACL, 2024 [Paper] [Code]

• Self-critiquing models for assisting human evaluators
  OpenAI, arXiv, 2022 [Paper]

Faithfulness Measurement

• Are self-explanations from Large Language Models faithful?
  Mila, ACL, 2024 [Paper]

• On Measuring Faithfulness or Self-consistency of Natural Language Explanations
  UAH, ACL, 2024 [Paper] [Code]

Consistency Estimation

• Semantic Consistency for Assuring Reliability of Large Language Models
  DTU, arXiv, 2023 [Paper]

Section V: Reasoning Elevation

Enhancing reasoning ability by improving LLM performance on QA tasks through Self-Feedback strategies.

Reasoning Topologically

• DSPy: Compiling Declarative Language Model Calls into State-of-the-Art Pipelines
  Stanford, ICLR, 2024 [Paper] [Code]

• Graph of Thoughts: Solving Elaborate Problems with Large Language Models
  ETH, AAAI, 2024 [Paper] [Code]

• Integrate the Essence and Eliminate the Dross: Fine-Grained Self-Consistency for Free-Form Language Generation
  BIT, ACL, 2024 [Paper] [Code]

• Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
  PKU, arXiv, 2024 [Paper] [Code]

• RATT: A Thought Structure for Coherent and Correct LLM Reasoning
  PSU, arXiv, 2024 [Paper] [Code]

• Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
  Stanford, arXiv, 2024 [Paper] [Code]

• Chain-of-Thought Reasoning Without Prompting
  Google, arXiv, 2024 [Paper]

• Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives
  ZJU, ACL, 2024 [Paper]

• Training Language Models to Self-Correct via Reinforcement Learning
  Google, arXiv, 2024 [Paper]

• LLMs cannot find reasoning errors, but can correct them given the error location
  Cambridge, ACL, 2024 [Paper]

• Forward-Backward Reasoning in Large Language Models for Mathematical Verification
  SUSTech, ACL, 2024 [Paper] [Code]

• LeanReasoner: Boosting Complex Logical Reasoning with Lean
  JHU, NAACL, 2024 [Paper] [Code]

• Just Ask One More Time! Self-Agreement Improves Reasoning of Language Models in (Almost) All Scenarios
  Kuaishou, ACL, 2024 [Paper]

• Soft Self-Consistency Improves Language Model Agents
  UNC-CH, ACL, 2024 [Paper] [Code]

• Self-Evaluation Guided Beam Search for Reasoning
  NUS, NeuIPS, 2023 [Paper] [Code]

• Tree of Thoughts: Deliberate Problem Solving with Large Language Models
  Princeton, NeuIPS, 2023 [Paper] [Code]

• Self-Consistency Improves Chain of Thought Reasoning in Language Models
  Google, ICLR, 2023 [Paper]

• DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines
  Stanford, arXiv, 2023 [Paper] [Code]

• Universal Self-Consistency for Large Language Model Generation
  Google, arXiv, 2023 [Paper]

• Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency
  PKU, ACL, 2023 [Paper] [Code]

• Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution
  Google, arXiv, 2023 [Paper]

• Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP
  Stanford, arXiv, 2023 [Paper] [Code]

• Making Language Models Better Reasoners with Step-Aware Verifier
  PKU, ACL, 2023 [Paper] [Code]

• Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
  Google, NeuIPS, 2022 [Paper]

• Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations
  Washington, EMNLP, 2022 [Paper] [Code]

Refining with Responses

• SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales
  Purdue, EMNLP, 2024 [Paper] [Code]

• Small Language Models Need Strong Verifiers to Self-Correct Reasoning
  UMich, ACL, 2024 [Paper] [Code]

• Fine-Tuning with Divergent Chains of Thought Boosts Reasoning Through Self-Correction in Language Models
  TUDa, arXiv, 2024 [Paper] [Code]

• Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B
  Fudan, arXiv, 2024 [Paper] [Code]

• Teaching Language Models to Self-Improve by Learning from Language Feedback
  NEU, ACL, 2024 [Paper]

• Large Language Models Can Self-Improve At Web Agent Tasks
  UPenn, arXiv, 2024 [Paper]

• Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing
  Tencent, arXiv, 2024 [Paper]

• Can LLMs Learn from Previous Mistakes? Investigating LLMs’ Errors to Boost for Reasoning
  UCSD, ACL, 2024 [Paper] [Code]

• Fine-Grained Self-Endorsement Improves Factuality and Reasoning
  XMU, ACL, 2024 [Paper]

• Mirror: A Multiple-perspective Self-Reflection Method for Knowledge-rich Reasoning
  KCL, ACL, 2024 [Paper] [Code]

• Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation
  CUHK, ACL, 2024 [Paper] [Code]

• Self-Rewarding Language Models
  Meta, arXiv, 2024 [Paper]

• Learning From Mistakes Makes LLM Better Reasoner
  Microsoft, arXiv, 2024 [Paper] [Code]

• Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision
  CMU, NeuIPS, 2023 [Paper] [Code]

• Large Language Models Can Self-Improve
  Illinois, EMNLP, 2023 [Paper]

• Improving Logical Consistency in Pre-Trained Language Models using Natural Language Inference
  Stanford, Stanford CS224N Custom Project, 2022 [Paper]

• Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference
  Stanford, EMNLP, 2022 [Paper] [Code]

Multi-Agent Collaboration

• The Consensus Game: Language Model Generation via Equilibrium Search
  MIT, ICLR, 2024 [Paper]

• Improving Factuality and Reasoning in Language Models through Multiagent Debate
  MIT, ICML, 2024 [Paper] [Code]

• Scaling Large-Language-Model-based Multi-Agent Collaboration
  THU, arXiv, 2024 [Paper] [Code]

• AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning
  ZJU, ACL, 2024 [Paper] [Code]

• ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs
  UNC, ACL, 2024 [Paper] [Code]

• REFINER: Reasoning Feedback on Intermediate Representations
  EPFL, EACL, 2024 [Paper] [Code]

• Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate
  HIT, EMNLP, 2023 [Paper] [Code]

Section VI: Hallucination Alleviation

Improving factual accuracy in open-ended generation and reducing hallucinations through Self-Feedback strategies.

Mitigating Hallucination while Generating

• Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation
  ETH, ICLR, 2024 [Paper] [Code]

• Mitigating Entity-Level Hallucination in Large Language Models
  THU, arXiv, 2024 [Paper] [Code]

• Know the Unknown: An Uncertainty-Sensitive Method for LLM Instruction Tuning
  HKUST, arXiv, 2024 [Paper] [Code]

• Fine-grained Hallucination Detection and Editing for Language Models
  UoW, arXiv, 2024 [Paper] [Code]

• EVER: Mitigating Hallucination in Large Language Models through Real-Time Verification and Rectification
  UNC, arXiv, 2023 [Paper]

• Chain-of-Verification Reduces Hallucination in Large Language Models
  Meta, arXiv, 2023 [Paper]

• PURR: Efficiently Editing Language Model Hallucinations by Denoising Language Model Corruptions
  UCI, arXiv, 2023 [Paper]

• RARR: Researching and Revising What Language Models Say, Using Language Models
  CMU, ACL, 2023 [Paper] [Code]

Refining the Response Iteratively

• From Code to Correctness: Closing the Last Mile of Code Generation with Hierarchical Debugging
  SJTU, arXiv, 2024 [Paper] [Code]

• Teaching Large Language Models to Self-Debug
  Google, ICLR, 2024 [Paper]

• LLMs can learn self-restraint through iterative self-reflection
  ServiceNow, arXiv, 2024 [Paper]

• Reflexion: Language Agents with Verbal Reinforcement Learning
  Northeastern, NeuIPS, 2023 [Paper] [Code]

• Generating Sequences by Learning to Self-Correct
  AI2, ICLR, 2023 [Paper]

• MAF: Multi-Aspect Feedback for Improving Reasoning in Large Language Models
  UCSB, EMNLP, 2023 [Paper] [Code]

• Self-Refine: Iterative Refinement with Self-Feedback
  CMU, NeuIPS, 2023 [Paper] [Code]

• PEER: A Collaborative Language Model
  Meta, ICLR, 2023 [Paper]

• Re3: Generating Longer Stories With Recursive Reprompting and Revision
  Berkeley, EMNLP, 2023 [Paper] [Code]

Activating Truthfulness

• Truth Forest: Toward Multi-Scale Truthfulness in Large Language Models through Intervention without Tuning
  BUAA, AAAI, 2024 [Paper] [Code]

• Look Within, Why LLMs Hallucinate: A Causal Perspective
  NUDT, arXiv, 2024 [Paper]

• Retrieval Head Mechanistically Explains Long-Context Factuality
  PKU, arXiv, 2024 [Paper] [Code]

• TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space
  ICT, ACL, 2024 [Paper] [Code]

• Inference-Time Intervention: Eliciting Truthful Answers from a Language Model
  Harvard, NeuIPS, 2023 [Paper] [Code]

• Fine-tuning Language Models for Factuality
  Stanford, arXiv, 2023 [Paper]

Decoding Truthfully

• Diver: Large Language Model Decoding with Span-Level Mutual Information Verification
  IA, arXiv, 2024 [Paper]

• SED: Self-Evaluation Decoding Enhances Large Language Models for Better Generation
  FDU, arXiv, 2024 [Paper]

• Enhancing Contextual Understanding in Large Language Models through Contrastive Decoding
  Edin, arXiv, 2024 [Paper] [Code]

• DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models
  MIT, ICLR, 2024 [Paper] [Code]

• Trusting Your Evidence: Hallucinate Less with Context-aware Decoding
  UoW, arXiv, 2023 [Paper]

• Contrastive Decoding: Open-ended Text Generation as Optimization
  Stanford, ACL, 2023 [Paper] [Code]

Section VII: Other Tasks

In addition to tasks aimed at improving consistency (enhancing reasoning and alleviating hallucinations), there are other tasks that also utilize Self-Feedback strategies.

Preference Learning

• Aligning Large Language Models from Self-Reference AI Feedback with one General Principle
  FDU, arXiv, 2024 [Paper] [Code]

• Aligning Large Language Models with Self-generated Preference Data
  KAIST, arXiv, 2024 [Paper]

• Self-Alignment of Large Language Models via Monopolylogue-based Social Scene Simulation
  SJTU, PMLR, 2024 [Paper] [Code]

• Self-Improving Robust Preference Optimization
  Cohere, arXiv, 2024 [Paper]

• Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
  UCLA, PMLR, 2024 [Paper] [Code]

• Self-Play Preference Optimization for Language Model Alignment
  UCLA, arXiv, 2024 [Paper] [Code]

• ChatGLM-Math: Improving Math Problem-Solving in Large Language Models with a Self-Critique Pipeline
  Zhipu, arXiv, 2024 [Paper] [Code]

• SALMON: Self-Alignment with Instructable Reward Models
  IBM, ICLR, 2024 [Paper] [Code]

• Self-Specialization: Uncovering Latent Expertise within Large Language Models
  GT, ACL, 2024 [Paper]

• BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset
  PKU, NeurIPS, 2023 [Paper] [Code]

• Safe RLHF: Safe Reinforcement Learning from Human Feedback
  PKU, arXiv, 2023 [Paper] [Code]

• Aligning Large Language Models through Synthetic Feedback
  NAVER, arXiv, 2023 [Paper] [Code]

• OpenAssistant Conversations -- Democratizing Large Language Model Alignment
  Unaffiliated, arXiv, 2023 [Paper] [Code]

• The Capacity for Moral Self-Correction in Large Language Models
  Anthropic, arXiv, 2023 [Paper]

• Constitutional AI: Harmlessness from AI Feedback
  Anthropic, arXiv, 2022 [Paper] [Code]

• Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
  Anthropic, arXiv, 2022 [Paper] [Code]

Knowledge Distillation

• Beyond Imitation: Leveraging Fine-grained Quality Signals for Alignment
  RUC, ICLR, 2024 [Paper] [Code]

• On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes
  Google, ICLR, 2024 [Paper]

• Self-Refine Instruction-Tuning for Aligning Reasoning in Language Models
  Idiap, arXiv, 2024 [Paper]

• Personalized Distillation: Empowering Open-Sourced LLMs with Adaptive Learning for Code Generation
  NTU, EMNLP, 2023 [Paper] [Code]

• SelFee: Iterative Self-Revising LLM Empowered by Self-Feedback Generation
  KAIST, Blog, 2023 [Paper]

• Reinforced Self-Training (ReST) for Language Modeling
  Google, arXiv, 2023 [Paper]

• Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing
  Washington, arXiv, 2023 [Paper]

• Self-Knowledge Distillation with Progressive Refinement of Targets
  LG, ICCV, 2021 [Paper] [Code]

• Revisiting Knowledge Distillation via Label Smoothing Regularization
  NUS, CVPR, 2020 [Paper]

• Self-Knowledge Distillation in Natural Language Processing
  Handong, RANLP, 2019 [Paper]

Continuous Learning

• Self-Tuning: Instructing LLMs to Effectively Acquire New Knowledge through Self-Teaching
  CUHK, arXiv, 2024 [Paper]

• Self-Evolving GPT: A Lifelong Autonomous Experiential Learner
  HIT, ACL, 2024 [Paper] [Code]

Data Synthesis

• Self-Instruct: Aligning Language Models with Self-Generated Instructions
  Washington, ACL, 2023 [Paper] [Code]

• Self-training Improves Pre-training for Natural Language Understanding
  Facebook, arXiv, 2020 [Paper]

Consistency Optimization

• Improving the Robustness of Large Language Models via Consistency Alignment
  SDU, LREC-COLING, 2024 [Paper]

Decision Making

• Can Large Language Models Play Games? A Case Study of A Self-Play Approach
  Northwestern, arXiv, 2024 [Paper]

Event Argument Extraction

• ULTRA: Unleash LLMs' Potential for Event Argument Extraction through Hierarchical Modeling and Pair-wise Refinement
  UMich, ACL, 2024 [Paper]

Inference Acceleration

• Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding
  ZJU, ACL, 2024 [Paper] [Code]

Machine Translation

• TasTe: Teaching Large Language Models to Translate through Self-Reflection
  HIT, ACL, 2024 [Paper] [Code]

Negotiation Optimization

• Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback
  Edin, arXiv, 2023 [Paper] [Code]

Retrieval Augmented Generation

• Improving Retrieval Augmented Language Model with Self-Reasoning
  Baidu, arXiv, 2024 [Paper]

Text Classification

• Text Classification Using Label Names Only: A Language Model Self-Training Approach
  Illinois, EMNLP, 2020 [Paper] [Code]

Section VIII.A: Meta Evaluation

Some common evaluation benchmarks.

Consistency Evaluation

• Can Large Language Models Always Solve Easy Problems if They Can Solve Harder Ones?
  PKU, arXiv, 2024 [Paper] [Code]

• Cross-Lingual Consistency of Factual Knowledge in Multilingual Language Models
  RUG, EMNLP, 2023 [Paper] [Code]

• Predicting Question-Answering Performance of Large Language Models through Semantic Consistency
  IBM, GEM, 2023 [Paper] [Code]

• BECEL: Benchmark for Consistency Evaluation of Language Models
  Oxford, Coling, 2022 [Paper] [Code]

• Measuring and Improving Consistency in Pretrained Language Models
  BIU, TACL, 2021 [Paper] [Code]

Self-Knowledge Evaluation

• Can I understand what I create? Self-Knowledge Evaluation of Large Language Models
  THU, arXiv, 2024 [Paper]

• Can AI Assistants Know What They Don't Know?
  Fudan, arXiv, 2024 [Paper] [Code]

• Do Large Language Models Know What They Don’t Know?
  Fudan, ACL, 2023 [Paper] [Code]

Uncertainty Evaluation

• UBENCH: Benchmarking Uncertainty in Large Language Models with Multiple Choice Questions
  Nankai, arXiv, 2024 [Paper] [Code]

• Benchmarking LLMs via Uncertainty Quantification
  Tencent, arXiv, 2024 [Paper] [Code]

Feedback Ability Evaluation

• CriticBench: Benchmarking LLMs for Critique-Correct Reasoning
  THU, ACL, 2024 [Paper] [Code]

Theoretical Perspectives

Some theoretical research on Internal Consistency and Self-Feedback strategies.

• AI models collapse when trained on recursively generated data
  Oxford, Nature, 2024 [Paper]

• A Theoretical Understanding of Self-Correction through In-context Alignment
  MIT, ICML, 2024 [Paper]

• Large Language Models Cannot Self-Correct Reasoning Yet
  Google, ICLR, 2024 [Paper]

• LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations
  Technion, arXiv, 2024 [Paper] [Code]

• When Can Transformers Count to n?
  NYU, arXiv, 2024 [Paper]

• Large Language Models as Reliable Knowledge Bases?
  UoE, arXiv, 2024 [Paper]

• States Hidden in Hidden States: LLMs Emerge Discrete State Representations Implicitly
  THU, arXiv, 2024 [Paper]

• Large Language Models have Intrinsic Self-Correction Ability
  UB, arXiv, 2024 [Paper]

• What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering
  NECLab, arXiv, 2024 [Paper] [Code]

• Large Language Models Must Be Taught to Know What They Don't Know
  NYU, arXiv, 2024 [Paper] [Code]

• Are LLMs classical or nonmonotonic reasoners? Lessons from generics
  UvA, ACL, 2024 [Paper] [Code]

• On the Intrinsic Self-Correction Capability of LLMs: Uncertainty and Latent Concept
  MSU, arXiv, 2024 [Paper]

• Calibrating Reasoning in Language Models with Internal Consistency
  SJTU, arXiv, 2024 [Paper]

• Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?
  TAU, arXiv, 2024 [Paper]

• Grokked Transformers are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalization
  OSU, arXiv, 2024 [Paper] [Code]

• SELF-[IN]CORRECT: LLMs Struggle with Refining Self-Generated Responses
  JHU, arXiv, 2024 [Paper]

• Masked Thought: Simply Masking Partial Reasoning Steps Can Improve Mathematical Reasoning Learning of Language Models
  RUC, ACL, 2024 [Paper] [Code]

• Do Large Language Models Latently Perform Multi-Hop Reasoning?
  TAU, arXiv, 2024 [Paper]

• Pride and Prejudice: LLM Amplifies Self-Bias in Self-Refinement
  UCSB, ACL, 2024 [Paper] [Code]

• The Impact of Reasoning Step Length on Large Language Models
  Rutgers, ACL, 2024 [Paper] [Code]

• Can Large Language Models Really Improve by Self-critiquing Their Own Plans?
  ASU, NeurIPS, 2023 [Paper]

• GPT-4 Doesn’t Know It’s Wrong: An Analysis of Iterative Prompting for Reasoning Problems
  ASU, NeurIPS, 2023 [Paper]

• Lost in the Middle: How Language Models Use Long Contexts
  Stanford, TACL, 2023 [Paper]

• How Language Model Hallucinations Can Snowball
  NYU, arXiv, 2023 [Paper] [Code]

• On the Principles of Parsimony and Self-Consistency for the Emergence of Intelligence
  UCB, FITEE, 2022 [Paper]

• On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?
  Washington, FAccT, 2021 [Paper]

• How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering
  CMU, TACL, 2021 [Paper] [Code]

• Language Models as Knowledge Bases?
  Facebook, EMNLP, 2019 [Paper] [Code]

📝 Citation

@article{liang2024internal,
  title={Internal consistency and self-feedback in large language models: A survey},
  author={Liang, Xun and Song, Shichao and Zheng, Zifan and Wang, Hanyu and Yu, Qingchen and Li, Xunkai and Li, Rong-Hua and Wang, Yi and Wang, Zhonghao and Xiong, Feiyu and Li, Zhiyu},
  journal={arXiv preprint arXiv:2407.14507},
  year={2024}
}