NepKANUN: A RAG-Based Nepali Legal Assistant

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.

Nepali Legal Expertise through Generative and Extractive Pre-trained Transformers (NepLEGiT)
LegalDrill: Diagnosis-Driven Synthesis for Legal Reasoning in Small Language Models
Evaluating Customized vs. Generalist Transformer-based Models for Legal Contract Classification
IndicTalk: A Large-Scale Persona-Based Multilingual Conversational Corpus for Indic Languages
On Measuring Semantic Preservation in Legal Ontology Learning

Source: arXiv cs.CL | 2026-09-16

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