A General Retrieval-Augmented Generation Framework for Multimodal Case-Based Reasoning Applications
This addresses multimodal case-based reasoning for AI applications, but it is incremental as it extends existing RAG methods to multimodal data.
The paper tackles the problem of applying retrieval-augmented generation to multimodal case-based reasoning by proposing MCBR-RAG, a framework that converts non-text components into text representations for retrieval and context enrichment, showing improved generation quality in experiments on Math-24 and Backgammon applications.
Case-based reasoning (CBR) is an experience-based approach to problem solving, where a repository of solved cases is adapted to solve new cases. Recent research shows that Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) can support the Retrieve and Reuse stages of the CBR pipeline by retrieving similar cases and using them as additional context to an LLM query. Most studies have focused on text-only applications, however, in many real-world problems the components of a case are multimodal. In this paper we present MCBR-RAG, a general RAG framework for multimodal CBR applications. The MCBR-RAG framework converts non-text case components into text-based representations, allowing it to: 1) learn application-specific latent representations that can be indexed for retrieval, and 2) enrich the query provided to the LLM by incorporating all case components for better context. We demonstrate MCBR-RAG's effectiveness through experiments conducted on a simplified Math-24 application and a more complex Backgammon application. Our empirical results show that MCBR-RAG improves generation quality compared to a baseline LLM with no contextual information provided.