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This research presents a fine-tuned large language model integrated with a Retrieval-Augmented Generation (RAG) framework to address legal information barriers in Nepal. By leveraging customized datasets and evaluating performance via BERTScore, the study demonstrates how merging generation and retrieval can effectively democratize access to complex legal knowledge. The work highlights a practical application of machine learning techniques tailored for low-resource language environments.
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NepKANUN: A RAG-Based Nepali Legal Assistant

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2609.15999v1 Announce Type: new Abstract: Accessing legal information in Nepal is difficult due to complex terminology, limited resources, and misinformation. We introduce an AI-powered legal assistant that is tailored for Nepali legal texts and is built on a fine-tuned large language model. The technology provides precise, streamlined answers to natural language legal inquiries when integrated into a Retrieval-Augmented Generation (RAG) framework. It was trained using a custom dataset of high-quality question-answer pairs, and according to BERTScore, it obtained strong F1 scores of 0.82 (simple), 0.77 (moderate), and 0.71 (complex). Its usability is further confirmed by expert reviews. Our method shows how merging generation and retrieval can effectively democratize access to legal knowledge in Nepal by focusing on customized legal data and incorporating RAG.

Abstract:Accessing legal information in Nepal is difficult due to complex terminology, limited resources, and misinformation. We introduce an AI-powered legal assistant that is tailored for Nepali legal texts and is built on a fine-tuned large language model. The technology provides precise, streamlined answers to natural language legal inquiries when integrated into a Retrieval-Augmented Generation (RAG) framework. It was trained using a custom dataset of high-quality question-answer pairs, and according to BERTScore, it obtained strong F1 scores of 0.82 (simple), 0.77 (moderate), and 0.71 (complex). Its usability is further confirmed by expert reviews. Our method shows how merging generation and retrieval can effectively democratize access to legal knowledge in Nepal by focusing on customized legal data and incorporating RAG.

Computation and Language (cs.CL)

arXiv:2609.15999 [cs.CL]

(or arXiv:2609.15999v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.15999 Focus to learn more arXiv-issued DOI via DataCite

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