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    <abstract>Large language models (LLMs) are moving into legal workflows, yet we lack a jurisdiction-grounded way to gauge their basic competence in thereof. We use India’s public legal examinations as a transparent proxy. Our multi-year benchmark assembles objective screens from top national and state exams and evaluates open and frontier LLMs under real world exam conditions. To probe beyond MCQs, we also include a lawyer-graded, paired-blinded study of long-form answers from the Supreme Court’s Advocate-on-Record exam. This is, to our knowledge, the first exam-grounded, India-specific yardstick for LLM court-readiness released with datasets and protocols. Our work shows that while frontier systems consistently clear historical cutoffs and often match or exceed recent top-scorer bands on objective exams, none surpasses the human topper on long-form reasoning. Grader notes converge on three reliability failure modes—procedural/format compliance, authority/citation discipline, and forum-appropriate voice/structure. These findings delineate where LLMs can assist (checks, cross-statute consistency, statute and precedent lookups) and where human leadership remains essential: forum-specific drafting and filing, procedural and relief strategy, reconciling authorities and exceptions, and ethical, accountable judgment.</abstract>
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%0 Conference Proceedings
%T Are LLMs Court-Ready? Evaluating Frontier Models on Indian Legal Reasoning
%A Juvekar, Kush
%A Bhattacharya, Arghya
%A Khadloya, Sai
%A Saxena, Utkarsh
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goanță, Cătălina
%Y Preoțiuc-Pietro, Daniel
%Y Spanakis, Gerasimos
%S Proceedings of the Natural Legal Language Processing Workshop 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-338-8
%F juvekar-etal-2025-llms
%X Large language models (LLMs) are moving into legal workflows, yet we lack a jurisdiction-grounded way to gauge their basic competence in thereof. We use India’s public legal examinations as a transparent proxy. Our multi-year benchmark assembles objective screens from top national and state exams and evaluates open and frontier LLMs under real world exam conditions. To probe beyond MCQs, we also include a lawyer-graded, paired-blinded study of long-form answers from the Supreme Court’s Advocate-on-Record exam. This is, to our knowledge, the first exam-grounded, India-specific yardstick for LLM court-readiness released with datasets and protocols. Our work shows that while frontier systems consistently clear historical cutoffs and often match or exceed recent top-scorer bands on objective exams, none surpasses the human topper on long-form reasoning. Grader notes converge on three reliability failure modes—procedural/format compliance, authority/citation discipline, and forum-appropriate voice/structure. These findings delineate where LLMs can assist (checks, cross-statute consistency, statute and precedent lookups) and where human leadership remains essential: forum-specific drafting and filing, procedural and relief strategy, reconciling authorities and exceptions, and ethical, accountable judgment.
%U https://aclanthology.org/2025.nllp-1.26/
%P 359-369

Markdown (Informal)

[Are LLMs Court-Ready? Evaluating Frontier Models on Indian Legal Reasoning](https://aclanthology.org/2025.nllp-1.26/) (Juvekar et al., NLLP 2025)

• Are LLMs Court-Ready? Evaluating Frontier Models on Indian Legal Reasoning (Juvekar et al., NLLP 2025)

ACL

• Kush Juvekar, Arghya Bhattacharya, Sai Khadloya, and Utkarsh Saxena. 2025. Are LLMs Court-Ready? Evaluating Frontier Models on Indian Legal Reasoning. In Proceedings of the Natural Legal Language Processing Workshop 2025, pages 359–369, Suzhou, China. Association for Computational Linguistics.