Large Language Models (LLMs) could struggle to fully understand legal theories and perform complex legal reasoning tasks. In this study, we introduce a challenging task (confusing charge prediction) to better evaluate LLMs' understanding of legal theories and reasoning capabilities. We also propose a novel framework: Multi-Agent framework for improving complex Legal Reasoning capability (MALR). MALR employs non-parametric learning, encouraging LLMs to automatically decompose complex legal tasks and mimic human learning process to extract insights from legal rules, helping LLMs better understand legal theories and enhance their legal reasoning abilities. Extensive experiments on multiple real-world datasets demonstrate that the proposed framework effectively addresses complex reasoning issues in practical scenarios, paving the way for more reliable applications in the legal domain.

本研究旨在解决大型语言模型（LLMs）在法律理论理解和复杂法律推理任务中的不足。我们提出了一种新颖的多智能体框架（MALR），该框架利用非参数学习方式，有效地分解复杂的法律任务以模仿人类的学习过程，从而提升LLMs的法律推理能力。实验证明，该框架能够有效应对现实场景中的复杂推理问题，对于法律领域的应用具有重要影响。

大型语言模型能否理解法律理论？通过多智能体协作提升法律推理能力