面向尼泊尔法律领域问答的检索增强生成框架
Retrieval Augmented Generation Framework for the Nepali Legal Domain Question Answering
AI总结：
提出首个基于检索增强生成的尼泊尔法律问答模型，利用BM25和E5模型检索案例法，实现91%的top-1精度和74%的生成答案可信度。
摘要：
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。然而，尼泊尔语等低资源语言的数据稀缺限制了大型语言模型在尼泊尔法律文本上的训练。本研究首次应用基于检索增强生成的模型，利用从Nepal Kanun Patrika数字档案中提取的案例法进行尼泊尔法律问答。使用BM25对分块文档进行检索，该方法实现了91%的top-1精度，使用多语言E5大模型时达到75%。对生成答案的评估显示，使用BM25文档检索时，可信度为74%，根据自动评判模型评估的真实性为85%，人工评估的真实性为84%，成功答案生成率为92%。这些结果表明，RAG管道可以有效解决低资源语言法律问答的差距，并为尼泊尔法律领域的可靠AI系统奠定基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
