Retrieval Augmented Generation Framework Developed for Nepali Legal Question Answering
A new study presents the first Retrieval Augmented Generation (RAG) system designed for legal question answering in Nepali, trained on case laws from the Nepal Kanun Patrika digital archive. The system achieved up to 91 percent precision in document retrieval and 85 percent automated truthfulness scores, addressing a gap caused by data scarcity in low-resource languages. The work offers a potential foundation for accessible AI-driven legal assistance in underserved linguistic communities.
Researchers have developed the first RAG-based legal question-answering system for the Nepali language, targeting a domain where AI tools have been largely absent due to limited training data. The system retrieves relevant legal documents using BM25 and multilingual E5 large models applied to chunked case law texts from the Nepal Kanun Patrika archive. BM25 retrieval achieved a top precision-at-one of 91 percent, while the multilingual E5 large model reached up to 75 percent. Generated answers were evaluated at 74 percent groundedness, 85 percent truthfulness by an automated judge, and 84 percent truthfulness by human evaluators, with a 92 percent successful answer generation rate. The study argues these results demonstrate that RAG pipelines can meaningfully close the gap in legal AI for low-resource languages, providing a replicable framework for similar underserved legal systems.
What's missing
The study does not detail the size or diversity of the case law dataset used, potential biases in the Nepal Kanun Patrika archive's coverage, how the system performs on out-of-domain or ambiguous legal queries, or whether the automated judge model was validated against broader human evaluation benchmarks. The generalizability of these results to other low-resource languages or legal jurisdictions is also not addressed.
What different sources said
- arXiv cs.AICenter
Retrieval Augmented Generation Framework for the Nepali Legal Domain Question Answering
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