用法律条文和判例增强案件预测，提升印度法系判决准确性。
NyayaRAG: Realistic Legal Judgment Prediction with RAG under the Indian Common Law System
- 引入法条与判例作为外部知识，结合案情生成判决。
- 融合法律知识后准确率显著提升，解释更符合司法逻辑。
- 适合法律AI研究者与司法智能化开发者参考。
法律判决预测（LJP）已成为人工智能在法律领域的重要方向，旨在自动化司法结果预测并提升法律推理的可解释性。以往针对印度语境的方法主要依赖案件内部内容（如事实、争议点和推理），但常忽略普通法体系的核心特征——对法律条文和判例的依赖。本文提出NyayaRAG，一种检索增强生成（RAG）框架，通过提供案件事实描述、相关法律条文及语义检索的先例案例，模拟真实法庭场景。该框架采用面向印度法律体系的专用流程，评估三类输入组合在判决预测与法律解释生成上的效果。我们使用标准词法与语义指标，以及基于大模型的G-Eval等评估器进行分析。结果表明，将结构化法律知识融入事实输入，显著提升了预测准确率与解释质量。
原文摘要 · Abstract (English)
Legal Judgment Prediction (LJP) has emerged as a key area in AI for law, aiming to automate judicial outcome forecasting and enhance interpretability in legal reasoning. While previous approaches in the Indian context have relied on internal case content such as facts, issues, and reasoning, they often overlook a core element of common law systems, which is reliance on statutory provisions and judicial precedents. In this work, we propose NyayaRAG, a Retrieval-Augmented Generation (RAG) framework that simulates realistic courtroom scenarios by providing models with factual case descriptions, relevant legal statutes, and semantically retrieved prior cases. NyayaRAG evaluates the effectiveness of these combined inputs in predicting court decisions and generating legal explanations using a domain-specific pipeline tailored to the Indian legal system. We assess performance across various input configurations using both standard lexical and semantic metrics as well as LLM-based evaluators such as G-Eval. Our results show that augmenting factual inputs with structured legal knowledge significantly improves both predictive accuracy and explanation quality.
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