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We examine how well the state-of-the-art (SOTA) models used in legalreasoning support abductive reasoning tasks. Abductive reasoning is a form oflogical inference in which a hypothesis is formulated from a set ofobservations, and that hypothesis is used to explain the observations. Theability to formulate such hypotheses is important for lawyers and legalscholars as it helps them articulate logical arguments, interpret laws, anddevelop legal theories. Our motivation is to consider the belief that deeplearning models, especially large language models (LLMs), will soon replacelawyers because they perform well on tasks related to legal text processing.But to do so, we believe, requires some form of abductive hypothesis formation.In other words, while LLMs become more popular and powerful, we want toinvestigate their capacity for abductive reasoning. To pursue this goal, westart by building a logic-augmented dataset for abductive reasoning with498,697 samples and then use it to evaluate the performance of a SOTA model inthe legal field. Our experimental results show that although these models canperform well on tasks related to some aspects of legal text processing, theystill fall short in supporting abductive reasoning tasks.

How well do SOTA legal reasoning models support abductive reasoning?