Large Legal Fictions: Profiling Legal Hallucinations in Large Language
  Models
Matthew Dahl Varun Magesh Mirac Suzgun Daniel E. Ho
Large language models (LLMs) have the potential to transform the practice of
law, but this potential is threatened by the presence of legal hallucinations
-- responses from these models that are not consistent with legal facts. We
investigate the extent of these hallucinations using an original suite of legal
queries, comparing LLMs' responses to structured legal metadata and examining
their consistency. Our work makes four key contributions: (1) We develop a
typology of legal hallucinations, providing a conceptual framework for future
research in this area. (2) We find that legal hallucinations are alarmingly
prevalent, occurring between 69% of the time with ChatGPT 3.5 and 88% with
Llama 2, when these models are asked specific, verifiable questions about
random federal court cases. (3) We illustrate that LLMs often fail to correct a
user's incorrect legal assumptions in a contra-factual question setup. (4) We
provide evidence that LLMs cannot always predict, or do not always know, when
they are producing legal hallucinations. Taken together, these 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.