GLARE：面向法律判决预测的智能体推理框架
GLARE: Agentic Reasoning for Legal Judgment Prediction
AI总结：
针对大语言模型在法律判决预测中因缺乏法律知识导致推理不足的问题，本文提出智能体式推理框架GLARE，通过动态调用模块获取法律知识以提升推理广度与深度，并在真实数据集上验证了有效性。
摘要：
法律判决预测（LJP）在法律领域日益重要。本文指出，现有大语言模型（LLMs）因缺乏法律知识而存在推理不足的显著问题。为此，我们提出GLARE，一个智能体式法律推理框架，通过调用不同模块动态获取关键法律知识，从而提升推理的广度与深度。在真实世界数据集上进行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能够增强可解释性，并为实际应用提供可能性。
英文摘要：
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.
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。
