mlsanityData to 7 Oct 2026

SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models

arXiv 2303.08896 ↗First posted 15 Mar 2023cs.CLcited by 787

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Cited by 111 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Generative Large Language Models (LLMs) such as GPT-3 are capable of generating highly fluent responses to a wide variety of user prompts. However, LLMs are known to hallucinate facts and make non-factual statements which can undermine trust in their output. Existing fact-checking approaches either require access to the output probability distribution (which may not be available for systems such as ChatGPT) or external databases that are interfaced via separate, often complex, modules. In this work, we propose "SelfCheckGPT", a simple sampling-based approach that can be used to fact-check the responses of black-box models in a zero-resource fashion, i.e. without an external database. SelfCheckGPT leverages the simple idea that if an LLM has knowledge of a given concept, sampled responses are likely to be similar and contain consistent facts. However, for hallucinated facts, stochastically sampled responses are likely to diverge and contradict one another. We investigate this approach by using GPT-3 to generate passages about individuals from the WikiBio dataset, and manually annotate the factuality of the generated passages. We demonstrate that SelfCheckGPT can: i) detect non-factual and factual sentences; and ii) rank passages in terms of factuality. We compare our approach to several baselines and show that our approach has considerably higher AUC-PR scores in sentence-level hallucination detection and higher correlation scores in passage-level factuality assessment compared to grey-box methods.

Key facts. Cited by 787 papers in the map (data through 7 Oct 2026). Builds on 36 of its 47 references that are in the map. First posted on arXiv 15 Mar 2023.

Builds on

36 of 47 references.

References · 36

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
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24 more
  1. 2017A Broad-Coverage Challenge Corpus for Sentence Understanding through Inferencecited by 2,665
  2. 2022Emergent Abilities of Large Language Modelscited by 2,483
  3. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  4. 2022Language Models (Mostly) Know What They Knowcited by 1,783
  5. 2021DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharingcited by 1,219
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  11. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 95; 2024: 156; 2025: 225; 2026: 311.

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  2. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,411
  3. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,259
  4. 2023A Survey on Evaluation of Large Language Modelscited by 1,227
  5. 2023WizardLM: Empowering large pre-trained language models to follow complex instructionscited by 1,116
  6. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 751
  7. 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 695
  8. 2023Prompt Injection attack against LLM-integrated Applicationscited by 568
  9. 2024Large Language Models: A Surveycited by 506
  10. 2023OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocationcited by 468
  11. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 408
  12. 2023Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignmentcited by 394
38 more
  1. 2023DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Modelscited by 367
  2. 2023A Survey of Hallucination in Large Foundation Modelscited by 365
  3. 2023NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Railscited by 347
  4. 2025Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscationcited by 338
  5. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  6. 2023HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Modelscited by 329
  7. 2023ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledgecited by 324
  8. 2023Chain-of-Verification Reduces Hallucination in Large Language Modelscited by 317
  9. 2024INSIDE: LLMs' Internal States Retain the Power of Hallucination Detectioncited by 317
  10. 2024Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Securitycited by 257
  11. 2024Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMscited by 246
  12. 2023Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategiescited by 236
  13. 2024When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMscited by 230
  14. 2024LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinationscited by 230
  15. 2023Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificitycited by 204
  16. 2023A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generationcited by 202
  17. 2023Evaluating Large Language Models: A Comprehensive Surveycited by 200
  18. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 188
  19. 2023LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examplescited by 177
  20. 2023Can LLM-Generated Misinformation Be Detected?cited by 173
  21. 2024Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Toolscited by 170
  22. 2024A Comprehensive Study of Knowledge Editing for Large Language Modelscited by 157
  23. 2024Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similaritiescited by 156
  24. 2024Fine-grained Hallucination Detection and Editing for Language Modelscited by 146
  25. 2024Large Legal Fictions: Profiling Legal Hallucinations in Large Language Modelscited by 144
  26. 2024Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoningcited by 136
  27. 2023Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Surveycited by 134
  28. 2024Language Models with Conformal Factuality Guaranteescited by 128
  29. 2023Hallucinations in Large Multilingual Translation Modelscited by 126
  30. 2024ST-WebAgentBench: A Benchmark for Evaluating Safety and Trustworthiness in Web Agentscited by 126
  31. 2023Factuality Challenges in the Era of Large Language Modelscited by 125
  32. 2023Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigationcited by 123
  33. 2023A Survey of Large Language Models in Medicine: Progress, Application, and Challengecited by 123
  34. 2023Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthinesscited by 121
  35. 2024To Believe or Not to Believe Your LLMcited by 120
  36. 2024Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQLcited by 118
  37. 2023Do Language Models Know When They're Hallucinating References?cited by 115
  38. 2025A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deploymentcited by 110

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