GLARE: Agentic Reasoning for Legal Judgment Prediction

TL;DR

GLARE is a novel agentic reasoning framework that enhances legal judgment prediction by dynamically acquiring legal knowledge, improving reasoning depth and interpretability, and demonstrating effectiveness on real-world datasets.

Contribution

Introduces GLARE, an agentic framework that dynamically integrates legal knowledge modules to improve reasoning in legal judgment prediction tasks.

Findings

Effective on real-world datasets

Improves reasoning depth and interpretability

Enhances legal knowledge acquisition

Abstract

Legal judgment prediction (LJP) has become increasingly important in the legal field. In this paper, we identify that existing large language models (LLMs) have significant problems of insufficient reasoning due to a lack of legal knowledge. Therefore, we introduce GLARE, an agentic legal reasoning framework that dynamically acquires key legal knowledge by invoking different modules, thereby improving the breadth and depth of reasoning. Experiments conducted on the real-world dataset verify the effectiveness of our method. Furthermore, the reasoning chain generated during the analysis process can increase interpretability and provide the possibility for practical applications.