[2505.21281] RLJP: Legal Judgment Prediction via First-Order Logic Rule-enhanced with Large Language Models

Computer Science > Artificial Intelligence arXiv:2505.21281 (cs) [Submitted on 27 May 2025 (v1), last revised 4 Mar 2026 (this version, v2)] Title:RLJP: Legal Judgment Prediction via First-Order Logic Rule-enhanced with Large Language Models Authors:Yue Zhang, Zhiliang Tian, Shicheng Zhou, Haiyang Wang, Wenqing Hou, Yuying Liu, Xuechen Zhao, Minlie Huang, Ye Wang, Bin Zhou View a PDF of the paper titled RLJP: Legal Judgment Prediction via First-Order Logic Rule-enhanced with Large Language Models, by Yue Zhang and 9 other authors View PDF HTML (experimental) 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...