LegalReasoner: A Multi-Stage Framework for Legal Judgment Prediction via Large Language Models and Knowledge Integration

Abstract

Legal judgment prediction (LJP) presents a formidable challenge in artificial intelligence, demanding intricate comprehension of legal texts, nuanced interpretation of statutes, and complex reasoning over multifaceted case elements. While recent advancements in natural language processing have shown promise, existing approaches often struggle to capture the sophisticated interplay between facts, legal principles, and precedents that characterize legal decision-making. This paper introduces LegalReasoner, a novel multi-stage framework that leverages large language models (LLMs) and integrates domain-specific knowledge for enhanced legal judgment prediction. Our approach encompasses four key stages: 1) legal knowledge infusion, where we pre-train an LLM on a vast corpus of legal literature using contrastive learning techniques; 2) case-law retrieval, employing a graph neural network to identify relevant precedents and statutes; 3) multi-hop reasoning, utilizing a transformer-based architecture with a hierarchical attention mechanism to navigate complex legal arguments; and 4) judgment synthesis, where we employ a generative adversarial network to produce coherent and legally sound judgments. We evaluate LegalReasoner on two diverse datasets: the European Court of Human Rights (ECHR) cases and the Chinese AI and Law Challenge (CAIL2018). Our framework demonstrates substantial improvements over state-of-the-art baselines, achieving an average accuracy increase of 7.8% across all datasets. Furthermore, we conduct extensive ablation studies and interpretability analyses to elucidate the contributions of each component and provide insights into the model’s decision-making process. Our work not only advances the field of automated legal reasoning but also offers a transparent and explainable system that could serve as a valuable tool for legal professionals. By bridging the gap between AI and legal expertise, LegalReasoner paves the way for more efficient, consistent, and fair legal decision-making processes.

Keywords

• Legal judgment prediction
• large language models
• knowledge integration
• multi-hop reasoning