Retrieval Augmented Generation Framework Developed for Nepali Legal Question Answering
Researchers have developed the first Retrieval Augmented Generation (RAG) based system for answering legal questions in Nepali, addressing a gap in AI applications for low-resource languages. The system uses case laws from Nepal's digital legal archive and achieved 91% precision at rank one with BM25 retrieval and 75% with multilingual models. The framework demonstrates that RAG pipelines can effectively support legal AI systems in languages with limited training data.
A new study presents the first application of Retrieval Augmented Generation technology for legal question answering in Nepali, a low-resource language that has been underserved by AI legal tools compared to high-resource languages like English. The researchers leveraged case laws from the Nepal Kanun Patrika digital archive and tested two retrieval approaches: BM25 on chunked documents and the multilingual E5 large model. The BM25 approach achieved a top precision at one of 91%, with the E5 model reaching 75% precision. Evaluation metrics showed 74% groundedness, 85% automated truthfulness, and 84% human-evaluated truthfulness for generated answers using BM25 retrieval, with a 92% successful answer generation rate. The results suggest that RAG pipelines can effectively bridge the gap in legal AI systems for languages with scarce training data, providing a foundation for reliable AI-assisted legal services in the Nepali legal domain.
What's missing
The study does not discuss potential limitations of the evaluation methodology, such as the size of the human evaluation sample, inter-annotator agreement metrics, or how the automated judge model was trained and validated. Additionally, the paper does not address computational costs, scalability considerations, or how the system performs on edge cases and ambiguous legal queries.
What different sources said
- arXiv cs.AICenter
Retrieval Augmented Generation Framework for the Nepali Legal Domain Question Answering
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