mlsanityData to 14 Oct 2026

Fine-grained Hallucination Detection and Editing for Language Models

arXiv 2401.06855 ↗First posted 12 Jan 2024cs.CLcited by 149

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Abstract (arXiv)

Large language models (LMs) are prone to generate factual errors, which are often called hallucinations. In this paper, we introduce a comprehensive taxonomy of hallucinations and argue that hallucinations manifest in diverse forms, each requiring varying degrees of careful assessments to verify factuality. We propose a novel task of automatic fine-grained hallucination detection and construct a new evaluation benchmark, FavaBench, that includes about one thousand fine-grained human judgments on three LM outputs across various domains. Our analysis reveals that ChatGPT and Llama2-Chat (70B, 7B) exhibit diverse types of hallucinations in the majority of their outputs in information-seeking scenarios. We train FAVA, a retrieval-augmented LM by carefully creating synthetic data to detect and correct fine-grained hallucinations. On our benchmark, our automatic and human evaluations show that FAVA significantly outperforms ChatGPT and GPT-4 on fine-grained hallucination detection, and edits suggested by FAVA improve the factuality of LM-generated text.

Key facts. Cited by 149 papers in the map (data through 14 Oct 2026). Builds on 38 of its 44 references that are in the map. First posted on arXiv 12 Jan 2024.

Builds on

38 of 44 references.

References · 38

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2022Training language models to follow instructions with human feedbackcited by 15,971
  4. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  5. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  6. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,724
  7. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  8. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  9. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
  10. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,375
  11. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,282
  12. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,216
26 more
  1. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 810
  2. 2023OpenAssistant Conversations -- Democratizing Large Language Model Alignmentcited by 721
  3. 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 698
  4. 2020Fact or Fiction: Verifying Scientific Claimscited by 554
  5. 2023How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resourcescited by 408
  6. 2017"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detectioncited by 363
  7. 2021Entity-Based Knowledge Conflicts in Question Answeringcited by 356
  8. 2023Evaluating Verifiability in Generative Search Enginescited by 303
  9. 2021Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metricscited by 287
  10. 2022RARR: Researching and Revising What Language Models Say, Using Language Modelscited by 287
  11. 2023FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarioscited by 251
  12. 2021QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarizationcited by 236
  13. 2021Get Your Vitamin C! Robust Fact Verification with Contrastive Evidencecited by 225
  14. 2022Are Large Pre-Trained Language Models Leaking Your Personal Information?cited by 200
  15. 2021Automated Fact-Checking for Assisting Human Fact-Checkerscited by 144
  16. 2022On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?cited by 138
  17. 2023The Troubling Emergence of Hallucination in Large Language Models -- An Extensive Definition, Quantification, and Prescriptive Remediationscited by 131
  18. 2018Automated Fact Checking: Task formulations, methods and future directionscited by 96
  19. 2024The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Modelscited by 88
  20. 2021Evaluating Attribution in Dialogue Systems: The BEGIN Benchmarkcited by 77
  21. 2023FactKB: Generalizable Factuality Evaluation using Language Models Enhanced with Factual Knowledgecited by 55
  22. 2023PURR: Efficiently Editing Language Model Hallucinations by Denoising Language Model Corruptionscited by 53
  23. 2020Challenges in Information-Seeking QA: Unanswerable Questions and Paragraph Retrievalcited by 44
  24. 2022Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model Infillingcited by 38
  25. 2022Evaluating Factuality in Text Simplificationcited by 34
  26. 2020A Review on Fact Extraction and Verificationcited by 29

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 56; 2025: 48; 2026: 45.

149 papers.

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  1. 2024When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMscited by 240
  2. 2024A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendationscited by 85
  3. 2024Unified Hallucination Detection for Multimodal Large Language Modelscited by 75
  4. 2024Sufficient Context: A New Lens on Retrieval Augmented Generation Systemscited by 72
  5. 2024Attribute First, then Generate: Locally-attributable Grounded Text Generationcited by 54
  6. 2024Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Surveycited by 53
  7. 2024Internal Consistency and Self-Feedback in Large Language Models: A Surveycited by 50
  8. 2025Large Language Models Hallucination: A Comprehensive Surveycited by 49
  9. 2025Calibrating Verbal Uncertainty as a Linear Feature to Reduce Hallucinationscited by 48
  10. 2024FLAME: Factuality-Aware Alignment for Large Language Modelscited by 47
  11. 2024A Closer Look at Claim Decompositioncited by 41
  12. 2024Lifelong Knowledge Editing for LLMs with Retrieval-Augmented Continuous Prompt Learningcited by 40
38 more
  1. 2024BERGEN: A Benchmarking Library for Retrieval-Augmented Generationcited by 31
  2. 2024Are Large Vision Language Models up to the Challenge of Chart Comprehension and Reasoning? An Extensive Investigation into the Capabilities and Limitations of LVLMscited by 30
  3. 2025Toward Generalizable Evaluation in the LLM Era: A Survey Beyond Benchmarkscited by 28
  4. 2024HaluEval-Wild: Evaluating Hallucinations of Language Models in the Wildcited by 27
  5. 2024Learning to Refine with Fine-Grained Natural Language Feedbackcited by 27
  6. 2025SemEval-2025 Task 3: Mu-SHROOM, the Multilingual Shared Task on Hallucinations and Related Observable Overgeneration Mistakescited by 27
  7. 2024WildHallucinations: Evaluating Long-form Factuality in LLMs with Real-World Entity Queriescited by 26
  8. 2024FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMscited by 24
  9. 2025Mask-DPO: Generalizable Fine-grained Factuality Alignment of LLMscited by 24
  10. 2024TimeChara: Evaluating Point-in-Time Character Hallucination of Role-Playing Large Language Modelscited by 22
  11. 2024AmbigDocs: Reasoning across Documents on Different Entities under the Same Namecited by 21
  12. 2024A Probabilistic Framework for LLM Hallucination Detection via Belief Tree Propagationcited by 21
  13. 2024On Mitigating Code LLM Hallucinations with API Documentationcited by 21
  14. 2024UniSumEval: Towards Unified, Fine-Grained, Multi-Dimensional Summarization Evaluation for LLMscited by 21
  15. 2024Atomic Fact Decomposition Helps Attributed Question Answeringcited by 21
  16. 2024Multi-FAct: Assessing Factuality of Multilingual LLMs using FActScorecited by 18
  17. 2024From Single to Multi: How LLMs Hallucinate in Multi-Document Summarizationcited by 17
  18. 2026AgentHallu: Benchmarking Automated Hallucination Attribution of LLM-based Agentscited by 16
  19. 2024TLDR: Token-Level Detective Reward Model for Large Vision Language Modelscited by 15
  20. 2024Improving Model Factuality with Fine-grained Critique-based Evaluatorcited by 15
  21. 2024Teaching LLMs to Abstain across Languages via Multilingual Feedbackcited by 14
  22. 2024Certainly Uncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awarenesscited by 14
  23. 2024ANAH-v2: Scaling Analytical Hallucination Annotation of Large Language Modelscited by 14
  24. 2024From Loops to Oops: Fallback Behaviors of Language Models Under Uncertaintycited by 14
  25. 2024Halu-J: Critique-Based Hallucination Judgecited by 14
  26. 2024Attribution Analysis Meets Model Editing: Advancing Knowledge Correction in Vision Language Models with VisEditcited by 14
  27. 2024Localizing Factual Inconsistencies in Attributable Text Generationcited by 14
  28. 2025HalluVerse25: Fine-grained Multilingual Benchmark Dataset for LLM Hallucinationscited by 14
  29. 2025Epistemic Alignment: A Mediating Framework for User-LLM Knowledge Deliverycited by 14
  30. 2025When Models Lie, We Learn: Multilingual Span-Level Hallucination Detection with PsiloQAcited by 14
  31. 2025Can LLMs Correct Themselves? A Benchmark of Self-Correction in LLMscited by 14
  32. 2026HALP: Detecting Hallucinations in Vision-Language Models without Generating a Single Tokencited by 14
  33. 2024Verifiable by Design: Aligning Language Models to Quote from Pre-Training Datacited by 13
  34. 2025HalluciNot: Hallucination Detection Through Context and Common Knowledge Verificationcited by 13
  35. 2025LAQuer: Localized Attribution Queries in Content-grounded Generationcited by 13
  36. 2024FactAlign: Long-form Factuality Alignment of Large Language Modelscited by 12
  37. 2024FG-PRM: Fine-grained Hallucination Detection and Mitigation in Language Model Mathematical Reasoningcited by 12
  38. 2025Out of Style: RAG's Fragility to Linguistic Variationcited by 11

50 most cited of 149 All 149 (JSON)

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  1. 2023HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models
  2. 2026Decomposed Entailment for Factuality Checking and Hallucination Detection
  3. 2024THaMES: An End-to-End Tool for Hallucination Mitigation and Evaluation in Large Language Models
  4. 2026Scalable Token-Level Hallucination Detection in Large Language Models
  5. 2025Theoretical Foundations and Mitigation of Hallucination in Large Language Models
  6. 2024Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback
  7. 2026ToolHACE: A Dataset for Detecting Hallucinations in Tool-Based LLM Responses
  8. 2025A Survey of Multimodal Hallucination Evaluation and Detection