AILQA: Evaluating AI-Driven Legal Question Answering Systems for the Indian Legal System
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Computer Science > Computation and Language
arXiv:2607.18825 (cs)
[Submitted on 21 Jul 2026]
Title:AILQA: Evaluating AI-Driven Legal Question Answering Systems for the Indian Legal System
Authors:Shubham Kumar Nigam, Shubham Kumar Mishra, Noel Shallum, Kripabandhu Ghosh, Arnab Bhattacharya
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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.
| Comments: | Accepted in AI and Law Journal |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR) |
| Cite as: | arXiv:2607.18825 [cs.CL] |
| (or arXiv:2607.18825v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.18825
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Shubham Kumar Nigam [view email][v1] Tue, 21 Jul 2026 08:01:17 UTC (664 KB)
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