Factual accuracy and hallucination reduction in language models for legal information processing

Develop reliable techniques to improve factual accuracy and reduce hallucinations in outputs from large language models used in legal information-processing applications, enabling dependable use in consequential legal settings.

Background

The authors identify hallucinations—incorrect or fabricated outputs—as a key limitation of LLMs in legal contexts, posing significant hurdles to adoption for consequential tasks.

Despite many efforts to improve factuality, the paper asserts that achieving robust factual accuracy remains an unsolved research problem, necessitating close verification of model outputs prior to use.