The authors in this paper created a benchmark including long-form, open-ended questions and multiple-choice questions to evaluate the performance of a number of different LLMs with respect to legal reasoning. Legal reasoning requires the application of deductive and inductive logic to complex scenarios, often with undefined parameters. Their results show that these models still 鈥渟truggle with open questions that require structured, multi-step legal reasoning.鈥
Legal reasoning is a critical frontier for large language models (LLMs) specifically and artificial intelligence (AI) at large, requiring specialized domain knowledge and advanced reasoning abilities such as precedent interpretation, statutory analysis, and legal inference. Despite progress in general reasoning, legal reasoning remains difficult and under-assessed in NLP research. Moreover, the legal domain is inherently high-stakes and a failure to thoroughly examine the capabilities and limitations of models could lead to serious real-world consequences …
Our analysis reveals substantial variability and limitations in LLM capabilities for addressing MCQs and especially on complex open questions; notably, increasing the number of MCQ options consistently reduces model accuracy. Our evaluation framework offers a scalable approach for assessing legal reasoning quality beyond simple accuracy metrics, thereby facilitating
future research aimed at enhancing the reliability and robustness of LLMs on challenging legal tasks.
