Summary of Cbr-rag: Case-based Reasoning For Retrieval Augmented Generation in Llms For Legal Question Answering, by Nirmalie Wiratunga et al.
CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question Answering
by Nirmalie Wiratunga, Ramitha Abeyratne, Lasal Jayawardena, Kyle Martin, Stewart Massie, Ikechukwu Nkisi-Orji, Ruvan Weerasinghe, Anne Liret, Bruno Fleisch
First submitted to arxiv on: 4 Apr 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 retrieval-augmented generation (RAG) approach enhances large language model (LLM) output by providing prior knowledge as context. This is particularly beneficial for tasks that require evidence, such as legal question-answering. By integrating case-based reasoning (CBR) into RAG, CBR-RAG provides a richer prompt for LLM queries. The evaluation demonstrates significant improvements in generated answer quality using different representations and methods of comparison on the task of legal question-answering. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make computers smarter is by giving them more context to help with tasks that need evidence, like answering legal questions. Right now, big language models can generate text, but they might not always get it right. By using a special technique called case-based reasoning, we can give these models more information to work with, making their answers better. |
Keywords
» Artificial intelligence » Large language model » Prompt » Question answering » Rag » Retrieval augmented generation
