Seeing Through the MiRAGE: Evaluating Multimodal Retrieval Augmented Generation
This addresses the need for better evaluation tools for multimodal RAG systems, which is crucial as audiovisual media becomes more prevalent online, though it is incremental as it builds on existing RAG evaluation concepts.
The paper tackles the problem of evaluating retrieval-augmented generation (RAG) systems that use multimodal sources, as existing evaluations are text-centric and inadequate for verifying information from audiovisual media. It introduces MiRAGE, a claim-centric evaluation framework with metrics like InfoF1 and CiteF1, showing strong alignment with human quality judgments and highlighting limitations of text-centric metrics.
We introduce MiRAGE, an evaluation framework for retrieval-augmented generation (RAG) from multimodal sources. As audiovisual media becomes a prevalent source of information online, it is essential for RAG systems to integrate information from these sources into generation. However, existing evaluations for RAG are text-centric, limiting their applicability to multimodal, reasoning intensive settings because they don't verify information against sources. MiRAGE is a claim-centric approach to multimodal RAG evaluation, consisting of InfoF1, evaluating factuality and information coverage, and CiteF1, measuring citation support and completeness. We show that MiRAGE, when applied by humans, strongly aligns with extrinsic quality judgments. We additionally introduce automatic variants of MiRAGE and three prominent TextRAG metrics -- ACLE, ARGUE, and RAGAS -- demonstrating the limitations of text-centric work and laying the groundwork for automatic evaluation. We release open-source implementations and outline how to assess multimodal RAG.