CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question Answering

arXiv:2404.04302v1 Announce Type: new
Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Model (LLM) output by providing prior knowledge as context to input. This is beneficial for knowledge-intensive and expert reliant tasks, including legal question-answering, which require evidence to validate generated text outputs. We highlight that Case-Based Reasoning (CBR) presents key opportunities to structure retrieval as part of the RAG process in an LLM. We introduce CBR-RAG, where CBR cycle's initial retrieval stage, its indexing vocabulary, and similarity knowledge containers are …

abstract arxiv case context cs.ai cs.cl evidence expert generated highlight knowledge language language model large language large language model legal llm llms prior question question answering rag reasoning retrieval retrieval-augmented retrieval augmented generation tasks text type