  We examine how well the state-of-the-art (SOTA) models used in legal
reasoning support abductive reasoning tasks. Abductive reasoning is a form of
logical inference in which a hypothesis is formulated from a set of
observations, and that hypothesis is used to explain the observations. The
ability to formulate such hypotheses is important for lawyers and legal
scholars as it helps them articulate logical arguments, interpret laws, and
develop legal theories. Our motivation is to consider the belief that deep
learning models, especially large language models (LLMs), will soon replace
lawyers 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 to
investigate their capacity for abductive reasoning. To pursue this goal, we
start by building a logic-augmented dataset for abductive reasoning with
498,697 samples and then use it to evaluate the performance of a SOTA model in
the legal field. Our experimental results show that although these models can
perform well on tasks related to some aspects of legal text processing, they
still fall short in supporting abductive reasoning tasks.
