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 "struggle with open questions that require structured, multi-step legal reasoning."
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.
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