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        <title>NyayGraph: A Knowledge Graph Enhanced Approach for Legal Statute Identification in Indian Law using Large Language Models</title>
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            <title>Proceedings of the Natural Legal Language Processing Workshop 2025</title>
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    <abstract>One of the first steps in the judicial processis finding the applicable statutes/laws basedon the facts of the current situation. Manu-ally searching through multiple legislation andlaws to find the relevant statutes can be time-consuming, making the Legal Statute Identi-fication (LSI) task important for reducing theworkload, helping improve the efficiency ofthe judicial system. To address this gap, wepresent a novel knowledge graph-enhanced ap-proach for Legal Statute Identification (LSI) inIndian legal documents using Large LanguageModels, incorporating structural relationshipsfrom the Indian Penal Code (IPC) the main leg-islation codifying criminal laws in India. Onthe IL-TUR benchmark, explicit KG inferencesignificantly enhances recall without sacrific-ing competitive precision. Augmenting LLMprompts with KG context, though, merely en-hances coverage at the expense of precision,underscoring the importance of good rerank-ing techniques. This research provides the firstcomplete IPC knowledge graph and shows thatorganized legal relations richly augment statuteretrieval, subject to being integrated into lan-guage models in a judicious way. Our code anddata are publicly available at Github. (https://github.com/SiddharthShukla48/NyayGraph)</abstract>
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%0 Conference Proceedings
%T NyayGraph: A Knowledge Graph Enhanced Approach for Legal Statute Identification in Indian Law using Large Language Models
%A Shukla, Siddharth
%A Tyagi, Tanuj
%A Bisht, Abhay Singh
%A Sharma, Ashish
%A Agarwal, Basant
%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 shukla-etal-2025-nyaygraph
%X One of the first steps in the judicial processis finding the applicable statutes/laws basedon the facts of the current situation. Manu-ally searching through multiple legislation andlaws to find the relevant statutes can be time-consuming, making the Legal Statute Identi-fication (LSI) task important for reducing theworkload, helping improve the efficiency ofthe judicial system. To address this gap, wepresent a novel knowledge graph-enhanced ap-proach for Legal Statute Identification (LSI) inIndian legal documents using Large LanguageModels, incorporating structural relationshipsfrom the Indian Penal Code (IPC) the main leg-islation codifying criminal laws in India. Onthe IL-TUR benchmark, explicit KG inferencesignificantly enhances recall without sacrific-ing competitive precision. Augmenting LLMprompts with KG context, though, merely en-hances coverage at the expense of precision,underscoring the importance of good rerank-ing techniques. This research provides the firstcomplete IPC knowledge graph and shows thatorganized legal relations richly augment statuteretrieval, subject to being integrated into lan-guage models in a judicious way. Our code anddata are publicly available at Github. (https://github.com/SiddharthShukla48/NyayGraph)
%U https://aclanthology.org/2025.nllp-1.11/
%P 147-156

Markdown (Informal)

[NyayGraph: A Knowledge Graph Enhanced Approach for Legal Statute Identification in Indian Law using Large Language Models](https://aclanthology.org/2025.nllp-1.11/) (Shukla et al., NLLP 2025)

• NyayGraph: A Knowledge Graph Enhanced Approach for Legal Statute Identification in Indian Law using Large Language Models (Shukla et al., NLLP 2025)

ACL

• Siddharth Shukla, Tanuj Tyagi, Abhay Singh Bisht, Ashish Sharma, and Basant Agarwal. 2025. NyayGraph: A Knowledge Graph Enhanced Approach for Legal Statute Identification in Indian Law using Large Language Models. In Proceedings of the Natural Legal Language Processing Workshop 2025, pages 147–156, Suzhou, China. Association for Computational Linguistics.