Interpretable Legal Judgment Reasoning Framework: Improving Forecasts of Case Outcomes with Multi-Source Knowledge

Abstract

This paper presents an interpretable legal judgment reasoning framework that aims to improve both the accuracy and interpretability of legal judgment predictions. The framework covers five key areas: limitations of existing methods, the role of factual logic in judgments, integration of external legal knowledge, handling of long-tail and ambiguous cases, and overall interpretability. The methodology adopted is qualitative, involving experimental data analysis and user feedback. This framework combines factually based logic with legal knowledge using a chain prompt reasoning module and a contrastive knowledge fusing module. Therefore, the result shows considerable improvement in terms of prediction accuracy and interpretability. These advances will fill important gaps in the existing literature on LJP research and represent a dynamic, transparent approach to judicial decision-making