Purifying Multimodal Retrieval: Fragment-Level Evidence Selection for RAG

TL;DR

This paper introduces FES-RAG, a fine-grained evidence selection framework for multimodal retrieval-augmented generation, which improves factual accuracy and reduces noise by selecting relevant fragments instead of entire documents.

Contribution

It proposes a novel fragment-level evidence selection method guided by Fragment Information Gain, enhancing retrieval precision and model performance in multimodal large language models.

Findings

FES-RAG outperforms state-of-the-art methods on M2RAG benchmark.

Achieves up to 27% relative improvement in CIDEr score.

Reduces context length while improving factual accuracy and coherence.

Abstract

Multimodal Retrieval-Augmented Generation (MRAG) is widely adopted for Multimodal Large Language Models (MLLMs) with external evidence to reduce hallucinations. Despite its success, most existing MRAG frameworks treat retrieved evidence as indivisible documents, implicitly assuming that all content within a document is equally informative. In practice, however, sometimes only a small fraction of a document is relevant to a given query, while the remaining content introduces substantial noise that may lead to performance degradation. We address this fundamental limitation by reframing MRAG as a fine-grained evidence selection problem. We propose Fragment-level Evidence Selection for RAG (FES-RAG), a framework that selects atomic multimodal fragments rather than entire documents as grounding evidence. FES-RAG decomposes retrieved multimodal documents into sentence-level textual fragments and region-level visual fragments, enabling precise identification of evidence that directly supports generation. To guide fragment selection, we introduce Fragment Information Gain (FIG), a principled metric that measures the marginal contribution of each fragment to the MLLM's generation confidence. Based on FIG, we distill fragment-level utility judgments from a high-capacity MLLM into a lightweight selector, achieving accurate evidence selection with low inference overhead. Experiments on the M2RAG benchmark show that FES-RAG consistently outperforms state-of-the-art document-level MRAG methods, achieving up to 27 percent relative improvement in CIDEr. By selecting fewer yet more informative fragments, our approach substantially reduces context length while improving factual accuracy and generation coherence.