CBR-RAG: Case-based Reasoning For Retrieval Augmented Generation In Llms For Legal Question Answering

Wiratunga Nirmalie, Abeyratne Ramitha, Jayawardena Lasal, Martin Kyle, Massie Stewart, Nkisi-orji Ikechukwu, Weerasinghe Ruvan, Liret Anne, Fleisch Bruno. Arxiv 2024

[Paper]    
Applications Prompting RAG Reinforcement Learning

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 used to enhance LLM queries with contextually relevant cases. This integration augments the original LLM query, providing a richer prompt. We present an evaluation of CBR-RAG, and examine different representations (i.e. general and domain-specific embeddings) and methods of comparison (i.e. inter, intra and hybrid similarity) on the task of legal question-answering. Our results indicate that the context provided by CBR’s case reuse enforces similarity between relevant components of the questions and the evidence base leading to significant improvements in the quality of generated answers.

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