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 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.

翻译：我们研究了法律推理中最先进（SOTA）模型在支持溯因推理任务中的表现。溯因推理是一种逻辑推理形式，其中从一组观察中构建假设，并利用该假设解释这些观察。构建此类假设的能力对律师和法律学者至关重要，因为它有助于他们阐述逻辑论点、解释法律并发展法学理论。我们的研究动机源于一种观点：深度学习模型，尤其是大型语言模型（LLMs），由于在法律文本处理相关任务中表现出色，可能很快取代律师。但我们认为，要实现这一点，需要某种形式的溯因假设形成能力。换言之，随着LLMs日益普及和强大，我们希望探究它们的溯因推理能力。为此，我们首先构建了一个包含498,697个样本的逻辑增强型溯因推理数据集，并利用该数据集评估了法律领域SOTA模型的性能。实验结果表明，尽管这些模型在法律文本处理的某些相关任务上表现良好，但在支持溯因推理任务方面仍存在不足。