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Legal Question-Answering in the Indian Context: Efficacy, Challenges, and Potential of Modern AI Models

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arxiv 2309.14735 v2 pith:NOCKPCO4 submitted 2023-09-26 cs.CL cs.AI

Shubham Kumar Nigam , Shubham Kumar Mishra , Ayush Kumar Mishra , Noel Shallum , Arnab Bhattacharya This is my paper · ORCID
classification cs.CLcs.AI
keywords legalindianailqachallengespotentialadeptnessambitanswering
verification ladder T0 review T1 audit T2 compute T3 formal
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Legal QA platforms bear the promise to metamorphose the manner in which legal experts engage with jurisprudential documents. In this exposition, we embark on a comparative exploration of contemporary AI frameworks, gauging their adeptness in catering to the unique demands of the Indian legal milieu, with a keen emphasis on Indian Legal Question Answering (AILQA). Our discourse zeroes in on an array of retrieval and QA mechanisms, positioning the OpenAI GPT model as a reference point. The findings underscore the proficiency of prevailing AILQA paradigms in decoding natural language prompts and churning out precise responses. The ambit of this study is tethered to the Indian criminal legal landscape, distinguished by its intricate nature and associated logistical constraints. To ensure a holistic evaluation, we juxtapose empirical metrics with insights garnered from seasoned legal practitioners, thereby painting a comprehensive picture of AI's potential and challenges within the realm of Indian legal QA.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LegalSeg: Unlocking the Structure of Indian Legal Judgments Through Rhetorical Role Classification

    cs.CL 2025-02 reject novelty 6.0 of 10

    LegalSeg provides 7,120 Indian judgments annotated with seven rhetorical roles and benchmarks several models, reporting that context-aware sequence models work best.

  2. NyayaAnumana & INLegalLlama: The Largest Indian Legal Judgment Prediction Dataset and Specialized Language Model for Enhanced Decision Analysis

    cs.CL 2024-12 reject novelty 6.0 of 10

    A new large corpus of Indian court cases and a legal LLaMA model report very high judgment-prediction accuracy, but the evaluation leaks the outcome from the input text.

  3. Rhetorical-Role-Aware Retrieval-Augmented Generation for Legal Question Answering over Indian Supreme Court Judgments

    cs.IR 2026-08 reject novelty 5.0 of 10

    A rhetorical-role-aware RAG system for Indian Supreme Court judgments is described, but its effectiveness claim is unsupported by baselines, ablations, or human evaluation.

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