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RLJP: Legal Judgment Prediction via First-Order Logic Rule-enhanced with Large Language Models
arXiv – CS AI|Yue Zhang, Zhiliang Tian, Shicheng Zhou, Haiyang Wang, Wenqing Hou, Yuying Liu, Xuechen Zhao, Minlie Huang, Ye Wang, Bin Zhou|
🤖AI Summary
Researchers propose RLJP, a new framework for Legal Judgment Prediction that combines first-order logic rules with large language models to improve AI-based legal decision making. The system uses a three-stage approach including Confusion-aware Contrastive Learning to dynamically optimize judgment rules and showed superior performance on public datasets.
Key Takeaways
- →RLJP framework integrates first-order logic formalism with large language models for legal judgment prediction.
- →The system addresses gaps in existing models by incorporating legal reasoning logic and adaptive adjustment mechanisms.
- →Uses a three-stage approach: rule initialization, confusion-aware contrastive learning optimization, and judgment prediction.
- →Experimental results demonstrate superior performance across all metrics on two public datasets.
- →The research code is made publicly available for further development and validation.
#legal-ai#machine-learning#legal-tech#first-order-logic#contrastive-learning#judgment-prediction#llm#research
Read Original →via arXiv – CS AI
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