arXiv 2505.212812026-03-04cs.AI

通过一阶逻辑规则增强的大语言模型进行法律判决预测

RLJP: Legal Judgment Prediction via First-Order Logic Rule-enhanced with Large Language Models

College of Computer Science and Technology, National University of Defense and Technology（国防科技大学计算机科学与技术学院）；College of Electronic Engineering, National University of Defense Technology（国防科技大学电子工程学院）；School of Data and Computer Science, Shandong Women’s University（山东女子大学数据与计算机科学学院）；Institute for Artificial Intelligence, Tsinghua University（清华大学人工智能研究院）

Yue Zhang, Zhiliang Tian, Shicheng Zhou, Haiyang Wang, Wenqing Hou, Yuying Liu, Xuechen Zhao, Minlie Huang, Ye Wang, Bin Zhou

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

本文提出基于一阶逻辑和对比学习的规则增强方法，通过三阶段流程提升法律判决预测的适应性和准确性。

摘要：

法律判决预测（LJP）是法律人工智能中的关键任务。现有的语义增强LJP模型整合了司法先例和法律知识以实现高性能。但是它们忽视了法律推理逻辑，这是法律判决中需要严格逻辑分析的重要组成部分。尽管一些方法利用法律推理逻辑进行高质量预测，但其逻辑刚性阻碍了适应案件特定的逻辑框架，特别是在复杂案件中，这些案件往往冗长且详细。本文提出了一种基于一阶逻辑（FOL）形式化和比较学习（CL）的规则增强法律判决预测框架，以开发一种适应性调整机制用于法律判决逻辑，并进一步提升LJP的性能。受人类考试准备过程的启发，我们的方法采用三阶段方法：首先，我们使用FOL形式化初始化判断规则以准确捕捉复杂的推理逻辑；接下来，我们提出了一种困惑意识对比学习（CACL），通过包含可混淆案例的测验动态优化判断规则；最后，我们利用优化后的判断规则进行法律判决预测。在两个公开数据集上的实验结果表明，在所有指标上均表现出色。代码已公开{https://anonymous.4open.science/r/RLJP-FDF1}。

英文摘要：

Legal Judgment Prediction (LJP) is a pivotal task in legal AI. Existing semantic-enhanced LJP models integrate judicial precedents and legal knowledge for high performance. But they neglect legal reasoning logic, a critical component of legal judgments requiring rigorous logical analysis. Although some approaches utilize legal reasoning logic for high-quality predictions, their logic rigidity hinders adaptation to case-specific logical frameworks, particularly in complex cases that are lengthy and detailed. This paper proposes a rule-enhanced legal judgment prediction framework based on first-order logic (FOL) formalism and comparative learning (CL) to develop an adaptive adjustment mechanism for legal judgment logic and further enhance performance in LJP. Inspired by the process of human exam preparation, our method follows a three-stage approach: first, we initialize judgment rules using the FOL formalism to capture complex reasoning logic accurately; next, we propose a Confusion-aware Contrastive Learning (CACL) to dynamically optimize the judgment rules through a quiz consisting of confusable cases; finally, we utilize the optimized judgment rules to predict legal judgments. Experimental results on two public datasets show superior performance across all metrics. The code is publicly available{https://anonymous.4open.science/r/RLJP-FDF1}.

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。