用逻辑规则+大模型提升法律判决预测准确率
RLJP: Legal Judgment Prediction via First-Order Logic Rule-enhanced with Large Language Models
- 基于一阶逻辑构建可调整的判决规则框架
- 在两个数据集上所有指标均优于现有方法
- 适合法律AI研究者与司法智能化开发者
法律判决预测（LJP）是法律AI中的关键任务。现有语义增强型LJP模型虽融合判例与法律知识取得良好效果，但忽视了法律推理逻辑这一核心要素——法律判决依赖严谨的逻辑分析。尽管部分方法引入法律逻辑以提升预测质量，其逻辑刚性难以适应具体案件的推理框架，尤其在复杂、长篇案例中表现受限。本文提出一种基于一阶逻辑（FOL）形式化与对比学习（CL）的规则增强型法律判决预测框架，通过类比人类备考过程，采用三阶段方法：首先利用FOL形式化初始化判决规则，精准捕捉复杂推理逻辑；其次提出混淆感知对比学习（CACL），通过包含易混淆案例的测验动态优化规则；最后使用优化后的规则进行判决预测。在两个公开数据集上的实验表明，该方法在各项指标上均表现优异。代码已公开。
原文摘要 · Abstract (English)
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}.
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