GLARE: Agentic Reasoning for Legal Judgment Prediction
This work addresses a domain-specific problem for legal professionals by improving reasoning in legal judgment prediction, though it appears incremental as it builds on existing LLM frameworks.
The authors tackled the problem of insufficient reasoning in legal judgment prediction by large language models due to lack of legal knowledge, introducing GLARE, an agentic legal reasoning framework that dynamically acquires key legal knowledge to improve reasoning breadth and depth, with experiments on a real-world dataset verifying its effectiveness.
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.