用检索增强生成解决尼泊尔法律问答数据少的问题
Retrieval Augmented Generation Framework for the Nepali Legal Domain Question Answering
- 用文档片段的BM25检索+大模型生成答案
- 最高准确率91%，答案真实度达85%
- 适合低资源语言法律AI研究者参考
英语等高资源语言的法律领域已广泛采用人工智能进行法律问答，但尼泊尔等低资源语言因法律文本数据稀缺，难以训练大型语言模型。本研究首次将基于检索增强生成（RAG）的模型应用于尼泊尔法律问答，使用从尼泊尔法律档案（Nepal Kanun Patrika）中提取的判例。通过在分块文档上使用BM25进行检索，该方法在顶级精确率上达到91%，使用多语言E5大模型时最高可达75%。生成答案的评估显示：74%的答案有据可依，自动化裁判模型判断其真实性达85%，人工评估真实性为84%，答案成功生成率为92%。结果表明，RAG流程能有效弥补低资源语言法律问答的差距，为尼泊尔法律领域的可靠AI系统奠定基础。
原文摘要 · Abstract (English)
Legal domains in high-resource languages like English have widely adopted artificial intelligence for legal question answering. However, data scarcity in low resource languages such as Nepali has limited the training of large language models on Nepali legal texts. This study presents the first application of a Retrieval Augmented Generation based model for Nepali legal question answering using case laws extracted from the Nepal Kanun Patrika digital archive. Using BM25 on chunked documents, the approach achieved a top precision at one of 91 percent, and up to 75 percent with the multilingual E5 large model. Evaluation of generated answers showed 74 percent groundedness, 85 percent truthfulness according to an automated judge model, and 84 percent human evaluated truthfulness when using BM25 document retrieval, with a 92 percent successful answer generation rate. These results demonstrate that the RAG pipeline can effectively address the gap in legal question answering for low resource languages and provide a foundation for reliable AI systems in the Nepali legal domain.
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