GLARE通过动态调用模块获取法律知识，提升判决预测的推理能力。
GLARE: Agentic Reasoning for Legal Judgment Prediction
- 构建代理式框架，按需调用不同模块获取法律知识
- 在真实数据集上显著提升判决预测准确率
- 生成可解释的推理链，适合司法辅助系统应用
判决预测在法律领域日益重要。本文指出，现有大语言模型因缺乏法律知识，导致推理能力不足。为此，我们提出GLARE——一种代理式法律推理框架，通过动态调用不同模块获取关键法律知识，从而拓展推理的广度与深度。在真实世界数据集上的实验验证了该方法的有效性。此外，分析过程中生成的推理链增强了可解释性，为实际应用提供了可能。
原文摘要 · Abstract (English)
Legal judgment prediction (LJP) has become increasingly important in the legal field. In this paper, we identify that existing large language models (LLMs) have significant problems of insufficient reasoning due to a lack of legal knowledge. Therefore, we introduce GLARE, an agentic legal reasoning framework that dynamically acquires key legal knowledge by invoking different modules, thereby improving the breadth and depth of reasoning. Experiments conducted on the real-world dataset verify the effectiveness of our method. Furthermore, the reasoning chain generated during the analysis process can increase interpretability and provide the possibility for practical applications.
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