mlsanityData to 7 Oct 2026

Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models

arXiv 2401.01301 ↗First posted 2 Jan 2024cs.CL cs.AI cs.CYcited by 144

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Cited by 20 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Do large language models (LLMs) know the law? These models are increasingly being used to augment legal practice, education, and research, yet their revolutionary potential is threatened by the presence of hallucinations -- textual output that is not consistent with legal facts. We present the first systematic evidence of these hallucinations, documenting LLMs' varying performance across jurisdictions, courts, time periods, and cases. Our work makes four key contributions. First, we develop a typology of legal hallucinations, providing a conceptual framework for future research in this area. Second, we find that legal hallucinations are alarmingly prevalent, occurring between 58% of the time with ChatGPT 4 and 88% with Llama 2, when these models are asked specific, verifiable questions about random federal court cases. Third, we illustrate that LLMs often fail to correct a user's incorrect legal assumptions in a contra-factual question setup. Fourth, we provide evidence that LLMs cannot always predict, or do not always know, when they are producing legal hallucinations. Taken together, our findings caution against the rapid and unsupervised integration of popular LLMs into legal tasks. Even experienced lawyers must remain wary of legal hallucinations, and the risks are highest for those who stand to benefit from LLMs the most -- pro se litigants or those without access to traditional legal resources.

Key facts. Cited by 144 papers in the map (data through 7 Oct 2026). Builds on 69 of its 125 references that are in the map. First posted on arXiv 2 Jan 2024.

Builds on

69 of 125 references.

References · 69

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38 more
  1. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,103
  2. 2023Towards Understanding Sycophancy in Language Modelscited by 988
  3. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 946
  4. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 895
  5. 2023Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedbackcited by 788
  6. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 787
  7. 2022Quantifying Memorization Across Neural Language Modelscited by 755
  8. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 751
  9. 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 695
  10. 2023In-Context Retrieval-Augmented Language Modelscited by 694
  11. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
  12. 2023The Internal State of an LLM Knows When It's Lyingcited by 644
  13. 2023DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Modelscited by 367
  14. 2023Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedbackcited by 351
  15. 2023HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Modelscited by 329
  16. 2023LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Modelscited by 328
  17. 2023Trusting Your Evidence: Hallucinate Less with Context-aware Decodingcited by 319
  18. 2023Chain-of-Verification Reduces Hallucination in Large Language Modelscited by 317
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  38. 2023Do Language Models Know When They're Hallucinating References?cited by 115

50 most cited of 69 All 69 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 27; 2025: 45; 2026: 72.

144 papers.

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  1. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 450
  2. 2024Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Toolscited by 170
  3. 2025AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challengescited by 167
  4. 2024Language agents achieve superhuman synthesis of scientific knowledgecited by 121
  5. 2024LLMs Will Always Hallucinate, and We Need to Live With Thiscited by 77
  6. 2025Large Language Models Hallucination: A Comprehensive Surveycited by 45
  7. 2024Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challengescited by 42
  8. 2024Are LLMs Ready for Real-World Materials Discovery?cited by 40
  9. 2025Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistenciescited by 39
  10. 2025A Reasoning-Focused Legal Retrieval Benchmarkcited by 37
  11. 2025Generalization Bias in Large Language Model Summarization of Scientific Researchcited by 30
  12. 2025Estimating LLM Uncertainty with Evidencecited by 26
38 more
  1. 2025OpenAI's Approach to External Red Teaming for AI Models and Systemscited by 26
  2. 2025SciSciGPT: Advancing Human-AI Collaboration in the Science of Sciencecited by 26
  3. 2024Finetuning Language Models to Emit Linguistic Expressions of Uncertaintycited by 25
  4. 2024DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate Hallucinationscited by 23
  5. 2024Generative AI in EU Law: Liability, Privacy, Intellectual Property, and Cybersecuritycited by 21
  6. 2024A Survey on the Honesty of Large Language Modelscited by 21
  7. 2024How well do LLMs cite relevant medical references? An evaluation framework and analysescited by 20
  8. 2024OLAPH: Improving Factuality in Biomedical Long-form Question Answeringcited by 20
  9. 2024BeHonest: Benchmarking Honesty in Large Language Modelscited by 20
  10. 2025Your Pre-trained LLM is Secretly an Unsupervised Confidence Calibratorcited by 19
  11. 2024Chain of Logic: Rule-Based Reasoning with Large Language Modelscited by 18
  12. 2025On Verifiable Legal Reasoning: A Multi-Agent Framework with Formalized Knowledge Representationscited by 16
  13. 2024Can Large Language Models Grasp Legal Theories? Enhance Legal Reasoning with Insights from Multi-Agent Collaborationcited by 14
  14. 2025Explainable Rule Application via Structured Prompting: A Neural-Symbolic Approachcited by 14
  15. 2024Robots in the Middle: Evaluating LLMs in Dispute Resolutioncited by 13
  16. 2025Elevating Legal LLM Responses: Harnessing Trainable Logical Structures and Semantic Knowledge with Legal Reasoningcited by 13
  17. 2026Compound Deception in Elite Peer Review: A Failure Mode Taxonomy of 100 Fabricated Citations at NeurIPS 2025cited by 13
  18. 2024Acceptable Use Policies for Foundation Modelscited by 12
  19. 2025L-MARS: Legal Multi-Agent System with Agentic Search and Citation-Faithfulness Auditcited by 11
  20. 2024The Factuality of Large Language Models in the Legal Domaincited by 10
  21. 2025Test-Time Scaling in Reasoning Models Is Not Effective for Knowledge-Intensive Tasks Yetcited by 10
  22. 2025Investigating the Shortcomings of LLMs in Step-by-Step Legal Reasoningcited by 9
  23. 2025LLM-based HSE Compliance Assessment: Benchmark, Performance, and Advancementscited by 8
  24. 2024Automated Theorem Provers Help Improve Large Language Model Reasoningcited by 7
  25. 2025Challenges for Generative AI in Legal Reasoningcited by 7
  26. 2025Moloch's Bargain: Emergent Misalignment When LLMs Compete for Audiencescited by 7
  27. 2024AI Act and Large Language Models (LLMs): When critical issues and privacy impact require human and ethical oversightcited by 6
  28. 2025A Comprehensive Survey on Legal Summarization: Challenges and Future Directionscited by 6
  29. 2025Incorporating Legal Structure in Retrieval-Augmented Generation: A Case Study on Copyright Fair Usecited by 6
  30. 2025Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Modelscited by 6
  31. 2025HACK: Hallucinations Along Certainty and Knowledge Axescited by 6
  32. 2024It Cannot Be Right If It Was Written by AI: On Lawyers' Preferences of Documents Perceived as Authored by an LLM vs a Humancited by 5
  33. 2024Equitable Access to Justice: Logical LLMs Show Promisecited by 5
  34. 2024Belief in the Machine: Investigating Epistemological Blind Spots of Language Modelscited by 5
  35. 2025AIReg-Bench: Benchmarking Language Models That Assess AI Regulation Compliancecited by 5
  36. 2026LLM Agents in Law: Taxonomy, Applications, and Challengescited by 5
  37. 2026When Silence Is Golden: Can LLMs Learn to Abstain in Temporal QA and Beyond?cited by 5
  38. 2026MARCH: Multi-Agent Reinforced Self-Check for LLM Hallucinationcited by 5

50 most cited of 144 All 144 (JSON)