AILQA: Evaluating AI-Driven Legal Question Answering Systems for the Indian Legal System

2026-07-21

AI Summary

This study addresses the challenges posed by the complexity of India’s legal system and the limited accuracy of existing AI-powered question-answering systems by developing and evaluating a retrieval-augmented generation (RAG) framework tailored to the Indian legal context. Integrating large language models with embedding models, this work presents the first systematic validation of RAG architecture in Indian legal settings and innovatively adopts the All India Bar Examination (AIBE) as a practical evaluation benchmark. Comprehensive assessments—including lexical and semantic metrics alongside multidimensional reviews by legal experts—demonstrate that RAG substantially enhances the quality of responses to complex legal queries. Notably, some generated answers exhibit such precise detail that they outperform official reference answers under specific evaluation criteria, highlighting the potential of AI to support legal reasoning.

Abstract

This comprehensive study introduces an advanced Artificial Intelligence for Indian Legal Question Answering (AILQA) system tailored to the Indian legal context. AILQA leverages a variety of embedding and generative models, including recent Large Language Models (LLMs), to address the unique challenges posed by the intricate and diverse nature of Indian legal texts and to enhance the accuracy and reliability of responses to legal questions. We conducted rigorous evaluations using both lexical and semantic metrics, enriched by expert legal feedback, to ensure relevance and accuracy. Our findings underscore the effectiveness of the Retrieval-Augmented Generation (RAG) paradigm in improving answer quality, particularly in complex legal domains. Additionally, we assessed performance on standardized tests such as the All India Bar Examination (AIBE), thereby providing a robust benchmark for practical applications. Under the study's evaluation protocol, some AI-generated responses received higher ratings than the available reference answers, particularly when they contained accurate and relevant supporting details. This finding is specific to the evaluated dataset and rating criteria and should not be interpreted as evidence that the models generally outperform qualified legal professionals. We also discuss the challenges encountered, such as the need for precise context and the risks of model hallucination, and propose directions for future research to further refine AI capabilities in the legal field. This study aims to pave the way for enhanced legal decision-support systems, making them more accessible and effective for legal professionals and the public alike.

Problem

• Legal Question Answering
• Indian Legal System
• AI Evaluation
• Large Language Models
• Retrieval-Augmented Generation

Innovation

• Retrieval-Augmented Generation
• Legal Question Answering
• Large Language Models
• Indian Legal System
• Expert Evaluation