2023/04/14 by Ha-Thanh Nguyen, Randy Goebel, Nguyen, Ha-Thanh +7 · 1 citation
Social Sciences · #Artificial Intelligence in Law #Comparative and International Law Studies #FOS: Computer and information sciences #Legal Language and Interpretation #Logic in Computer Science (cs.LO)
paper · pdf · doi:10.48550/arxiv.2304.06912
openalex publication_date 2023/04/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.