Third-party cyber evaluations involving OpenAI models
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AI Research
arXiv CS.AI·Jun 9·5 min read
Retrieval Augmented Generation Framework for the Nepali Legal Domain Question Answering
TL;DR
Data scarcity in low-resource languages like Nepali hampers the development of AI for legal question answering. A Retrieval Augmented Generation model was developed using case laws from the Nepal Kanun Patrika digital archive.
✦ Why It Matters
Engineers can leverage this model to develop AI solutions for other low-resource languages in specialized fields.
Key Takeaways
- Explore RAG models for low-resource language applications.
- Utilize BM25 for document retrieval in legal contexts.
- Assess AI-generated answers for truthfulness and groundedness.
- Leverage case law archives to train language models.
How It Works
The RAG framework combines retrieval and generation techniques, first retrieving relevant legal documents using BM25, then generating answers based on the retrieved content. This two-step process enhances the accuracy and relevance of the responses, particularly in the context of Nepali legal texts.