Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration
Project Overview
The document presents a comprehensive exploration of how generative AI, particularly through the Precedent-Enhanced Legal Judgment Prediction (PLJP) framework, can significantly improve legal decision-making in education and practice. By leveraging both large language models (LLMs) and specialized domain models, the PLJP framework enhances the accuracy of legal judgments through the sophisticated analysis and retrieval of relevant legal precedents. It emphasizes the critical role that precedents play in shaping legal outcomes and addresses the limitations of existing legal judgment prediction methods. The findings from experiments conducted on real-world datasets illustrate the effectiveness of this innovative approach, showcasing its potential to revolutionize legal education by providing students and practitioners with tools that not only predict outcomes but also deepen their understanding of the law through precedent analysis. Overall, the document underscores the transformative potential of generative AI in the legal field, highlighting its capacity to streamline legal processes and enhance educational methodologies.
Key Applications
Precedent-Enhanced Legal Judgment Prediction (PLJP)
Context: The legal domain, particularly for legal professionals and systems involved in predicting court judgments based on case facts.
Implementation: The framework combines LLMs with domain-specific models to retrieve and interpret relevant precedents, improving the accuracy of judgment predictions.
Outcomes: Achieved state-of-the-art performance in legal judgment prediction tasks, demonstrating the effectiveness of incorporating precedents into the prediction process.
Challenges: Existing models struggle with understanding abstract legal labels, and the integration of LLMs with domain-specific models can be complex.
Implementation Barriers
Technical Barrier
LLMs have limitations in prompt length and struggle with complex legal terminology.
Proposed Solutions: The proposed PLJP framework addresses this by combining LLMs with domain models to provide candidate labels and context.
Data Barrier
Potential data leakage during model training due to overlapping datasets.
Proposed Solutions: Creation of a new test set (CJO22) to prevent data leakage and ensure fair evaluation.
Project Team
Yiquan Wu
Researcher
Siying Zhou
Researcher
Yifei Liu
Researcher
Weiming Lu
Researcher
Xiaozhong Liu
Researcher
Yating Zhang
Researcher
Changlong Sun
Researcher
Fei Wu
Researcher
Kun Kuang
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Yiquan Wu, Siying Zhou, Yifei Liu, Weiming Lu, Xiaozhong Liu, Yating Zhang, Changlong Sun, Fei Wu, Kun Kuang
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai