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Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models

Written for the Research library (claude-opus-5-5) from the paper text. Original — arxiv.org/pdf/2401.01301

Summary

The paper asks how reliably general-purpose LLMs know American case law. It offers the first systematic measurement of legal "hallucinations," meaning answers that conflict with verifiable legal facts. The authors build a set of legal knowledge queries ranging from simple to complex. Simple ones ask whether a case exists, which court decided it, or who wrote the opinion. Harder ones ask about procedural posture, the core legal question, the central holding, or precedential relationships between cases. They pose these queries about randomly sampled cases from all three tiers of the federal judiciary (USDC, USCOA, SCOTUS) to ChatGPT 4, ChatGPT 3.5, PaLM 2 and Llama 2. Hallucinations turn out to be pervasive. The models also do better on recent, prominent cases from influential courts, which points to a risk of uneven, homogenized legal knowledge. In addition, the models often accept false premises built into questions and judge their own reliability poorly. The study warns against unsupervised use of LLMs in legal work, especially for under-resourced litigants. It also proposes a typology of legal hallucinations for future research.

Key points

  • On direct, verifiable questions about federal cases, hallucination rates range from 58% for GPT 4 to 88% for Llama 2. GPT 3.5 and PaLM 2 fall in between, at roughly 69–72%.
  • The tasks cover low-complexity items (existence, court, citation, author), mid-level items (disposition, quotation, authority, overruling year) and high-complexity items (factual background, subsequent history, central holding, doctrinal agreement).
  • Contra-factual prompts test whether models go along with incorrect user assumptions. These prompts rest on a false dissent premise or a false overruling premise, and models often produce plausible-sounding but wrong answers.
  • Calibration is measured with expected calibration error (ECE). Confidence is estimated by drawing 10 samples at temperature 1 and checking how often they agree with the greedy response. Without post-hoc recalibration, models misjudge how likely they are to be wrong.
  • The benchmark data is released on HuggingFace (reglab/legal_hallucinations), and the replication materials are in the Harvard Dataverse.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Law

Cited by

129 papers of our library cite it (of the 140673 whose bibliographies we read); 3 of them have a page here, 126 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1093/jla/laae003 · arXiv 2401.01301

@misc{dahl2024large,
  title = {{Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models}},
  author = {Dahl, Matthew and Magesh, Varun and Suzgun, Mirac and Ho, Daniel E.},
  year = {2024},
  doi = {10.1093/jla/laae003},
  eprint = {2401.01301},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1093/jla/laae003}
}

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