AppellateGen：上诉法律判决生成的基准测试
AppellateGen: A Benchmark for Appellate Legal Judgment Generation
AI总结：
本文提出AppellateGen基准测试，包含7351个案例对，用于生成上诉阶段的法律判决，通过模拟司法流程验证模型在上诉推理中的逻辑一致性与挑战。
摘要：
法律判决生成是法律智能中的关键任务。然而，现有研究主要集中在第一审，依赖静态的事实到判决映射，忽视了上诉（第二审）审查的辩证性质。为此，我们引入AppellateGen，一个包含7,351个案例对的上诉法律判决生成基准。该任务要求模型通过推理初始判决和证据更新来起草具有法律约束力的判决，从而建模审判阶段的因果依赖。我们进一步提出基于司法标准操作规程（SOP）的法律多代理系统（SLMAS），用于模拟司法流程，将其生成过程分解为问题识别、检索和起草等离散阶段。实验结果表明，尽管SLMAS提高了逻辑一致性，但上诉推理的复杂性仍然是当前大语言模型的重大挑战。数据集和代码可在：https://anonymous.4open.science/r/AppellateGen-5763 公开获取。
英文摘要：
Legal judgment generation is a critical task in legal intelligence. However, existing research in legal judgment generation has predominantly focused on first-instance trials, relying on static fact-to-verdict mappings while neglecting the dialectical nature of appellate (second-instance) review. To address this, we introduce AppellateGen, a benchmark for second-instance legal judgment generation comprising 7,351 case pairs. The task requires models to draft legally binding judgments by reasoning over the initial verdict and evidentiary updates, thereby modeling the causal dependency between trial stages. We further propose a judicial Standard Operating Procedure (SOP)-based Legal Multi-Agent System (SLMAS) to simulate judicial workflows, which decomposes the generation process into discrete stages of issue identification, retrieval, and drafting. Experimental results indicate that while SLMAS improves logical consistency, the complexity of appellate reasoning remains a substantial challenge for current LLMs. The dataset and code are publicly available at: https://anonymous.4open.science/r/AppellateGen-5763.
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。
