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

TL;DR

This paper introduces a novel legal judgment prediction framework that incorporates first-order logic rules and contrastive learning to adaptively model legal reasoning, significantly improving prediction accuracy on public datasets.

Contribution

It proposes a rule-enhanced LJP model using FOL formalism and comparative learning, addressing rigidity issues in existing logic-based approaches.

Findings

Superior performance on two public datasets

Effective adaptation of legal reasoning logic

Enhanced prediction accuracy across metrics

Abstract

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}.