Caveat Lector: Large Language Models in Legal Practice

Abstract

The current fascination with large language models, or LLMs, derives from the fact that many users lack the expertise to evaluate the quality of the generated text. LLMs may therefore appear more capable than they actually are. The dangerous combination of fluency and superficial plausibility leads to the temptation to trust the generated text and creates the risk of overreliance. Who would not trust perfect legalese? Relying recent findings in both technical and legal scholarship, this Article counterbalances the overly optimistic predictions as to the role of LLMs in legal practice. Integrating LLMs into legal workstreams without a better comprehension of their limitations, will create inefficiencies if not outright risks. Notwithstanding their unprecedented ability to generate text, LLMs do not understand text. Without the ability to understand meaning, LLMs will remain unable to use language, to acquire knowledge and to perform complex reasoning tasks. Trained to model language on the basis of stochastic word predictions, LLMs cannot distinguish fact from fiction. Their knowledge of the law is limited to word strings memorized in their parameters. It is also incomplete and largely incorrect. LLMs operate at the level of word distributions, not at the level of verified facts. The resulting propensity to hallucinate, to produce statements that are incorrect but appear helpful and relevant, is alarming in high-risk areas like legal services. At present, lawyers should beware of relying on text generated by LLMs.