NyayaRAG: Realistic Legal Judgment Prediction with RAG under the Indian Common Law System
This work addresses the challenge of automating judicial outcome forecasting for the Indian legal system, though it is incremental as it builds on existing RAG methods with domain-specific adaptations.
The paper tackled the problem of Legal Judgment Prediction in the Indian common law system by proposing NyayaRAG, a Retrieval-Augmented Generation framework that incorporates factual case descriptions, legal statutes, and precedents, resulting in significant improvements in predictive accuracy and explanation quality.
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.