构建首个法律推理树结构基准，支持透明判案与防偏见分析。
A Law Reasoning Benchmark for LLM with Tree-Organized Structures including Factum Probandum, Evidence and Experiences
- 设计包含要件、证据与隐性经验的分层推理框架
- 创建首个众包法律案例数据集，支持结构化推理评估
- 适用于法律AI可解释性研究与智能法庭系统开发
尽管法律应用已取得进展，但法律推理——保障公正裁决的关键——仍缺乏深入探索。本文提出一种透明的法律推理范式，融入分层的要件（factum probandum）、证据与隐性经验，支持公开审查并防止偏见。基于此范式，我们引入一项新任务：给定文本案件描述，输出支持最终判决的分层结构。同时，我们构建了首个众包法律推理数据集，支持全面评估。此外，提出一个采用全套法律分析工具的智能体框架，以应对该挑战任务。该基准为智能法庭中的透明、可问责的AI辅助法律推理铺平道路。
原文摘要 · Abstract (English)
While progress has been made in legal applications, law reasoning, crucial for fair adjudication, remains unexplored. We propose a transparent law reasoning schema enriched with hierarchical factum probandum, evidence, and implicit experience, enabling public scrutiny and preventing bias. Inspired by this schema, we introduce the challenging task, which takes a textual case description and outputs a hierarchical structure justifying the final decision. We also create the first crowd-sourced dataset for this task, enabling comprehensive evaluation. Simultaneously, we propose an agent framework that employs a comprehensive suite of legal analysis tools to address the challenge task. This benchmark paves the way for transparent and accountable AI-assisted law reasoning in the ``Intelligent Court''.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。