Stanford Study Finds High Percentage Of Errors Using Large Language Models In Legal Contexts

The study by Stanford University found that hallucinations, or the tendency of large language models (LLMs) to produce content that deviates from actual facts or well-established legal principles and precedents, occurred from 69% to 88% of the time in response to specific legal queries.

The study applied 200,000 queries against each of GPT 3.5, Llama 2, and PaLM 2 models. Although these generative AI programs have supposedly passed bar exams, they failed at some basic tasks performed by junior attorneys. For example, in a task measuring the precedential relationship between two different cases, most LLMs do no better than random guessing. In answering queries about a court's core ruling (or holding), models were found to hallucinate at least 75% of the time.