mlsanityData to 14 Oct 2026

Chain-of-Verification Reduces Hallucination in Large Language Models

arXiv 2309.11495 ↗First posted 20 Sep 2023cs.CL cs.AIcited by 324

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Cited by 79 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Generation of plausible yet incorrect factual information, termed hallucination, is an unsolved issue in large language models. We study the ability of language models to deliberate on the responses they give in order to correct their mistakes. We develop the Chain-of-Verification (CoVe) method whereby the model first (i) drafts an initial response; then (ii) plans verification questions to fact-check its draft; (iii) answers those questions independently so the answers are not biased by other responses; and (iv) generates its final verified response. In experiments, we show CoVe decreases hallucinations across a variety of tasks, from list-based questions from Wikidata, closed book MultiSpanQA and longform text generation.

Key facts. Cited by 324 papers in the map (data through 14 Oct 2026). Builds on 43 of its 54 references that are in the map. First posted on arXiv 20 Sep 2023.

Builds on

43 of 54 references.

References · 43

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  4. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  5. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
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  7. 2019The Curious Case of Neural Text Degenerationcited by 2,916
  8. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  9. 2019Language Models as Knowledge Bases?cited by 1,936
  10. 2022Language Models (Mostly) Know What They Knowcited by 1,856
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  12. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,282
31 more
  1. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,075
  2. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 900
  3. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 810
  4. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 692
  5. 2020Recipes for building an open-domain chatbotcited by 593
  6. 2023Active Retrieval Augmented Generationcited by 516
  7. 2023Fine-Grained Human Feedback Gives Better Rewards for Language Model Trainingcited by 401
  8. 2023Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedbackcited by 353
  9. 2023How Language Model Hallucinations Can Snowballcited by 311
  10. 2022Teaching language models to support answers with verified quotescited by 292
  11. 2022RARR: Researching and Revising What Language Models Say, Using Language Modelscited by 287
  12. 2021Measuring Attribution in Natural Language Generation Modelscited by 256
  13. 2023FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarioscited by 251
  14. 2020Reducing conversational agents' overconfidence through linguistic calibrationcited by 210
  15. 2023A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generationcited by 206
  16. 2023Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs?cited by 168
  17. 2023SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoningcited by 159
  18. 2023LM vs LM: Detecting Factual Errors via Cross Examinationcited by 156
  19. 2023Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Frameworkcited by 139
  20. 2020On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translationcited by 127
  21. 2023Deductive Verification of Chain-of-Thought Reasoningcited by 124
  22. 2023Do Language Models Know When They're Hallucinating References?cited by 116
  23. 2023Question Decomposition Improves the Faithfulness of Model-Generated Reasoningcited by 108
  24. 2023Improving Language Models via Plug-and-Play Retrieval Feedbackcited by 87
  25. 2023Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedbackcited by 72
  26. 2022Conditional Generation with a Question-Answering Blueprintcited by 44
  27. 2022Contrastive Learning Reduces Hallucination in Conversationscited by 40
  28. 2021Reason first, then respond: Modular Generation for Knowledge-infused Dialoguecited by 32
  29. 2023QUEST: A Retrieval Dataset of Entity-Seeking Queries with Implicit Set Operationscited by 31
  30. 2023Learning to Reason and Memorize with Self-Notescited by 30
  31. 2023Improving Factuality of Abstractive Summarization via Contrastive Reward Learningcited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 25; 2024: 86; 2025: 97; 2026: 116.

324 papers.

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  1. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  2. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  3. 2024A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applicationscited by 431
  4. 2024Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researcherscited by 363
  5. 2024Hallucination is Inevitable: An Innate Limitation of Large Language Modelscited by 332
  6. 2024Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMscited by 259
  7. 2024When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMscited by 240
  8. 2023Fine-tuning Language Models for Factualitycited by 221
  9. 2023Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificitycited by 206
  10. 2024Security and Privacy Challenges of Large Language Models: A Surveycited by 186
  11. 2023Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Modelscited by 184
  12. 2023Can LLM-Generated Misinformation Be Detected?cited by 177
38 more
  1. 2024Large Legal Fictions: Profiling Legal Hallucinations in Large Language Modelscited by 150
  2. 2024Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospectscited by 132
  3. 2024The Emerged Security and Privacy of LLM Agent: A Survey with Case Studiescited by 131
  4. 2023AutoMix: Automatically Mixing Language Modelscited by 125
  5. 2023A Survey of Large Language Models in Medicine: Progress, Application, and Challengecited by 124
  6. 2024Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineeringcited by 103
  7. 2024GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinementscited by 103
  8. 2024InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationalescited by 101
  9. 2024A Review of Large Language Models and Autonomous Agents in Chemistrycited by 99
  10. 2023System 2 Attention (is something you might need too)cited by 89
  11. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  12. 2025Learning to Plan & Reason for Evaluation with Thinking-LLM-as-a-Judgecited by 78
  13. 2024Adaptive Activation Steering: A Tuning-Free LLM Truthfulness Improvement Method for Diverse Hallucinations Categoriescited by 77
  14. 2024RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generationcited by 75
  15. 2023Unleashing the potential of prompt engineering for large language modelscited by 72
  16. 2023Vista-LLaMA: Reducing Hallucination in Video Language Models via Equal Distance to Visual Tokenscited by 71
  17. 2024Building Guardrails for Large Language Modelscited by 65
  18. 2024Language Repository for Long Video Understandingcited by 55
  19. 2024The Limits of Inference Scaling Through Resamplingcited by 53
  20. 2024PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Railscited by 52
  21. 2025Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling Verificationcited by 50
  22. 2023LILO: Learning Interpretable Libraries by Compressing and Documenting Codecited by 49
  23. 2025Large Language Models Hallucination: A Comprehensive Surveycited by 49
  24. 2024Graph-constrained Reasoning: Faithful Reasoning on Knowledge Graphs with Large Language Modelscited by 48
  25. 2025Calibrating Verbal Uncertainty as a Linear Feature to Reduce Hallucinationscited by 48
  26. 2024A Comprehensive Survey of Large Language Models and Multimodal Large Language Models in Medicinecited by 45
  27. 2024Thinking LLMs: General Instruction Following with Thought Generationcited by 45
  28. 2024Modular RAG: Transforming RAG Systems into LEGO-like Reconfigurable Frameworkscited by 42
  29. 2023MechGPT, a language-based strategy for mechanics and materials modeling that connects knowledge across scales, disciplines and modalitiescited by 38
  30. 2024CODE: Contrasting Self-generated Description to Combat Hallucination in Large Multi-modal Modelscited by 38
  31. 2023Chain of Natural Language Inference for Reducing Large Language Model Ungrounded Hallucinationscited by 35
  32. 2025Theoretical Physics Benchmark (TPBench) -- a Dataset and Study of AI Reasoning Capabilities in Theoretical Physicscited by 34
  33. 2024Cycles of Thought: Measuring LLM Confidence through Stable Explanationscited by 29
  34. 2025DREAMS: Density Functional Theory Based Research Engine for Agentic Materials Simulationcited by 29
  35. 2025Recognition through Reasoning: Reinforcing Image Geo-localization with Large Vision-Language Modelscited by 28
  36. 2024RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Modelscited by 27
  37. 2024Confidence Calibration and Rationalization for LLMs via Multi-Agent Deliberationcited by 26
  38. 2024Deductive Beam Search: Decoding Deducible Rationale for Chain-of-Thought Reasoningcited by 24

50 most cited of 324 All 324 (JSON)