Summary of Krag Framework For Enhancing Llms in the Legal Domain, by Nguyen Ha Thanh et al.
KRAG Framework for Enhancing LLMs in the Legal Domain
by Nguyen Ha Thanh, Ken Satoh
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework called Knowledge Representation Augmented Generation (KRAG) is introduced to enhance the capabilities of Large Language Models (LLMs) within domain-specific applications. KRAG includes critical knowledge entities and relationships that are typically absent in standard data sets, which LLMs do not inherently learn. In legal applications, an implementation model called Soft PROLEG uses inference graphs to aid LLMs in delivering structured legal reasoning, argumentation, and explanations tailored to user inquiries. The integration of KRAG either as a standalone framework or with retrieval augmented generation (RAG) improves the ability of language models to navigate and solve challenges posed by legal texts and terminologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make computers understand special kinds of information is developed. This method, called Knowledge Representation Augmented Generation, helps big computer programs learn about things they don’t usually know. In this case, it’s used for understanding laws and making arguments like a lawyer would. The new system gets better results than before when dealing with complicated legal texts. |
Keywords
» Artificial intelligence » Inference » Rag » Retrieval augmented generation
