面向大型语言模型的显式三段论法律推理框架
An Explicit Syllogistic Legal Reasoning Framework for Large Language Models
AI总结：
本文提出SyLeR框架，通过树状分层检索构建法律大前提，并结合监督微调预热与结构感知强化学习，使LLMs能够进行显式、可解释的三段论法律推理，显著提升了跨领域、多语言场景下的回答准确性与可信度。
摘要：
三段论推理对于稳健的法律决策至关重要，它使法律专业人员能够通过将一般原则应用于具体案件事实来得出逻辑结论。尽管大型语言模型（LLMs）能够回答法律问题，但它们通常难以进行显式的三段论推理。其输出往往是隐式的、非结构化的，因此可解释性和可信度较低。为克服这些局限性，我们提出了SyLeR，这是一个旨在使LLMs能够执行显式三段论法律推理的新型框架。SyLeR采用树状分层检索机制来综合相关法律法规和判例，从而构建全面的大前提。随后进行两阶段微调过程：初始的监督微调预热建立对三段论推理的基础理解，而由结构感知奖励机制引导的强化学习则优化模型生成多样化、逻辑严密且结构良好的推理路径的能力。我们进行了广泛实验以评估SyLeR的性能。我们的评估涵盖多个维度，包括域内和跨域用户群体（法律外行和从业者）、多种语言（中文和法语）以及各种LLM骨干网络（特定领域和开放域LLMs）。结果一致表明，SyLeR显著提高了回答准确性，并可靠地生成了显式、可解释且值得信赖的法律推理。
英文摘要：
Syllogistic reasoning is crucial for sound legal decision-making, allowing legal professionals to draw logical conclusions by applying general principles to specific case facts. While large language models (LLMs) can answer legal questions, they often struggle with explicit syllogistic reasoning. Their outputs tend to be implicit, unstructured, and consequently, less explainable and trustworthy. To overcome these limitations, we introduce SyLeR, a novel framework designed to enable LLMs to perform explicit syllogistic legal reasoning. SyLeR employs a tree-structured hierarchical retrieval mechanism to synthesize relevant legal statutes and precedents, thereby constructing comprehensive major premises. This is followed by a two-stage fine-tuning process: an initial supervised fine-tuning warm-up establishes a foundational understanding of syllogistic reasoning, while reinforcement learning, guided by a structure-aware reward mechanism, refines the model's capacity to generate diverse, logically sound, and well-structured reasoning paths. We conducted extensive experiments to evaluate SyLeR's performance. Our evaluations spanned diverse dimensions, including both in-domain and cross-domain user groups (legal laypersons and practitioners), multiple languages (Chinese and French), and various LLM backbones (legal-specific and open-domain LLMs). The results consistently demonstrate that SyLeR significantly enhances response accuracy and reliably produces explicit, explainable, and trustworthy legal reasoning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
