Re-ranking the Context for Multimodal Retrieval Augmented Generation

TL;DR

This paper improves multi-modal retrieval-augmented generation by using an advanced relevancy measure to select more relevant context entries, thereby enhancing response accuracy and reducing hallucinations.

Contribution

It introduces a relevancy score-based retrieval method that adaptively selects context entries, improving multi-modal RAG performance over traditional embedding-based methods.

Findings

Enhanced relevance in context selection using the proposed measure.

Significant improvement in response accuracy on COCO dataset.

Reduction in irrelevant context entries leading to better generation quality.

Abstract

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external knowledge to generate a response within a context with improved accuracy and reduced hallucinations. However, multi-modal RAG systems face unique challenges: (i) the retrieval process may select irrelevant entries to user query (e.g., images, documents), and (ii) vision-language models or multi-modal language models like GPT-4o may hallucinate when processing these entries to generate RAG output. In this paper, we aim to address the first challenge, i.e, improving the selection of relevant context from the knowledge-base in retrieval phase of the multi-modal RAG. Specifically, we leverage the relevancy score (RS) measure designed in our previous work for evaluating the RAG performance to select more relevant entries in retrieval process. The retrieval based on embeddings, say CLIP-based embedding, and cosine similarity usually perform poorly particularly for multi-modal data. We show that by using a more advanced relevancy measure, one can enhance the retrieval process by selecting more relevant pieces from the knowledge-base and eliminate the irrelevant pieces from the context by adaptively selecting up-to-$k$ entries instead of fixed number of entries. Our evaluation using COCO dataset demonstrates significant enhancement in selecting relevant context and accuracy of the generated response.