mlsanityData to 7 Oct 2026

HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models

arXiv 2305.11747 ↗First posted 19 May 2023cs.CLcited by 329

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Cited by 33 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Large language models (LLMs), such as ChatGPT, are prone to generate hallucinations, i.e., content that conflicts with the source or cannot be verified by the factual knowledge. To understand what types of content and to which extent LLMs are apt to hallucinate, we introduce the Hallucination Evaluation benchmark for Large Language Models (HaluEval), a large collection of generated and human-annotated hallucinated samples for evaluating the performance of LLMs in recognizing hallucination. To generate these samples, we propose a ChatGPT-based two-step framework, i.e., sampling-then-filtering. Besides, we also hire some human labelers to annotate the hallucinations in ChatGPT responses. The empirical results suggest that ChatGPT is likely to generate hallucinated content in specific topics by fabricating unverifiable information (i.e., about 19.5% responses). Moreover, existing LLMs face great challenges in recognizing the hallucinations in texts. However, our experiments also prove that providing external knowledge or adding reasoning steps can help LLMs recognize hallucinations. Our benchmark can be accessed at https://github.com/RUCAIBox/HaluEval.

Key facts. Cited by 329 papers in the map (data through 7 Oct 2026). Builds on 23 of its 36 references that are in the map. First posted on arXiv 19 May 2023.

Builds on

23 of 36 references.

References · 23

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  5. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  6. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  7. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  8. 2023Evaluating Object Hallucination in Large Vision-Language Modelscited by 1,696
  9. 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,281
  10. 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 888
  11. 2022GLM-130B: An Open Bilingual Pre-trained Modelcited by 837
  12. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 787
11 more
  1. 2023StructGPT: A General Framework for Large Language Model to Reason over Structured Datacited by 339
  2. 2021Measuring Attribution in Natural Language Generation Modelscited by 253
  3. 2021Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarizationcited by 128
  4. 2019Handling Divergent Reference Texts when Evaluating Table-to-Text Generationcited by 119
  5. 2022FaithDial: A Faithful Benchmark for Information-Seeking Dialoguecited by 108
  6. 2021Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Groundingcited by 106
  7. 2021Evaluating Attribution in Dialogue Systems: The BEGIN Benchmarkcited by 75
  8. 2021DialFact: A Benchmark for Fact-Checking in Dialoguecited by 62
  9. 2021Complex Knowledge Base Question Answering: A Surveycited by 52
  10. 2023Diving Deep into Modes of Fact Hallucinations in Dialogue Systemscited by 32
  11. 2023The Web Can Be Your Oyster for Improving Large Language Modelscited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 30; 2024: 104; 2025: 96; 2026: 99.

329 papers.

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  1. 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 695
  2. 2024Large Language Models: A Surveycited by 506
  3. 2024The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearningcited by 488
  4. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  5. 2024Hallucination is Inevitable: An Innate Limitation of Large Language Modelscited by 329
  6. 2023Large Language Model Unlearningcited by 288
  7. 2023Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificitycited by 204
  8. 2023Can LLM-Generated Misinformation Be Detected?cited by 173
  9. 2023Sociotechnical Safety Evaluation of Generative AI Systemscited by 154
  10. 2024Large Legal Fictions: Profiling Legal Hallucinations in Large Language Modelscited by 144
  11. 2023Factuality Challenges in the Era of Large Language Modelscited by 125
  12. 2024Benchmarking LLMs via Uncertainty Quantificationcited by 124
38 more
  1. 2023A Survey of Large Language Models in Medicine: Progress, Application, and Challengecited by 123
  2. 2025A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deploymentcited by 110
  3. 2024SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationalescited by 102
  4. 2025Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoningcited by 101
  5. 2024HaloScope: Harnessing Unlabeled LLM Generations for Hallucination Detectioncited by 96
  6. 2025Uncertainty Quantification and Confidence Calibration in Large Language Models: A Surveycited by 93
  7. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  8. 2025HalluLens: LLM Hallucination Benchmarkcited by 88
  9. 2024FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows"cited by 85
  10. 2024BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Modelscited by 84
  11. 2023Cognitive Mirage: A Review of Hallucinations in Large Language Modelscited by 83
  12. 2025Unified Multimodal Understanding and Generation Models: Advances, Challenges, and Opportunitiescited by 81
  13. 2024NetLLM: Adapting Large Language Models for Networkingcited by 76
  14. 2023Can Large Language Models Understand Real-World Complex Instructions?cited by 69
  15. 2023FELM: Benchmarking Factuality Evaluation of Large Language Modelscited by 66
  16. 2024Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligencecited by 65
  17. 2024Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systemscited by 63
  18. 2024Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats in LLM-Based Agentscited by 63
  19. 2025SimpleVQA: Multimodal Factuality Evaluation for Multimodal Large Language Modelscited by 61
  20. 2024Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress?cited by 56
  21. 2024Spectral Editing of Activations for Large Language Model Alignmentcited by 54
  22. 2024Visual Agents as Fast and Slow Thinkerscited by 53
  23. 2024Evaluating LLMs at Detecting Errors in LLM Responsescited by 49
  24. 2025The FACTS Grounding Leaderboard: Benchmarking LLMs' Ability to Ground Responses to Long-Form Inputcited by 49
  25. 2025Calibrating Verbal Uncertainty as a Linear Feature to Reduce Hallucinationscited by 48
  26. 2024ALI-Agent: Assessing LLMs' Alignment with Human Values via Agent-based Evaluationcited by 46
  27. 2024Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Eracited by 45
  28. 2025Large Language Models Hallucination: A Comprehensive Surveycited by 45
  29. 2026DeepSearchQA: Bridging the Comprehensiveness Gap for Deep Research Agentscited by 44
  30. 2024WirelessLLM: Empowering Large Language Models Towards Wireless Intelligencecited by 43
  31. 2024WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipediacited by 43
  32. 2024Cheating Automatic LLM Benchmarks: Null Models Achieve High Win Ratescited by 42
  33. 2024SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysiscited by 40
  34. 2024Detecting Hallucinations in Large Language Model Generation: A Token Probability Approachcited by 40
  35. 2023Chainpoll: A high efficacy method for LLM hallucination detectioncited by 39
  36. 2024Memory3: Language Modeling with Explicit Memorycited by 39
  37. 2024A Survey on Large Language Model Hallucination via a Creativity Perspectivecited by 38
  38. 2024Digital Forgetting in Large Language Models: A Survey of Unlearning Methodscited by 38

50 most cited of 329 All 329 (JSON)

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  1. 2025Theoretical Foundations and Mitigation of Hallucination in Large Language Models
  2. 2024LongHalQA: Long-Context Hallucination Evaluation for MultiModal Large Language Models
  3. 2024Fine-grained Hallucination Detection and Editing for Language Models
  4. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
  5. 2024ODE: Open-Set Evaluation of Hallucinations in Multimodal Large Language Models
  6. 2024HaluEval-Wild: Evaluating Hallucinations of Language Models in the Wild
  7. 2026Scalable Token-Level Hallucination Detection in Large Language Models
  8. 2025(Im)possibility of Automated Hallucination Detection in Large Language Models