MRAG-Suite: A Diagnostic Evaluation Platform for Visual Retrieval-Augmented Generation

Fuente: arXiv

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Detalles Bibliográficos
Autores principales:	Ji, Yuelyu, Lan, Wuwei, NG, Patrick
Formato:	Preprint
Publicado:	2025
Materias:	Computation and Language
Acceso en línea:	Acceder al recurso
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author	Ji, Yuelyu Lan, Wuwei NG, Patrick
author_facet	Ji, Yuelyu Lan, Wuwei NG, Patrick
contents	Multimodal Retrieval-Augmented Generation (Visual RAG) significantly advances question answering by integrating visual and textual evidence. Yet, current evaluations fail to systematically account for query difficulty and ambiguity. We propose MRAG-Suite, a diagnostic evaluation platform integrating diverse multimodal benchmarks (WebQA, Chart-RAG, Visual-RAG, MRAG-Bench). We introduce difficulty-based and ambiguity-aware filtering strategies, alongside MM-RAGChecker, a claim-level diagnostic tool. Our results demonstrate substantial accuracy reductions under difficult and ambiguous queries, highlighting prevalent hallucinations. MM-RAGChecker effectively diagnoses these issues, guiding future improvements in Visual RAG systems.
format	Preprint
id	arxiv_https___arxiv_org_abs_2509_24253
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	MRAG-Suite: A Diagnostic Evaluation Platform for Visual Retrieval-Augmented Generation Ji, Yuelyu Lan, Wuwei NG, Patrick Computation and Language Multimodal Retrieval-Augmented Generation (Visual RAG) significantly advances question answering by integrating visual and textual evidence. Yet, current evaluations fail to systematically account for query difficulty and ambiguity. We propose MRAG-Suite, a diagnostic evaluation platform integrating diverse multimodal benchmarks (WebQA, Chart-RAG, Visual-RAG, MRAG-Bench). We introduce difficulty-based and ambiguity-aware filtering strategies, alongside MM-RAGChecker, a claim-level diagnostic tool. Our results demonstrate substantial accuracy reductions under difficult and ambiguous queries, highlighting prevalent hallucinations. MM-RAGChecker effectively diagnoses these issues, guiding future improvements in Visual RAG systems.
title	MRAG-Suite: A Diagnostic Evaluation Platform for Visual Retrieval-Augmented Generation
topic	Computation and Language
url	https://arxiv.org/abs/2509.24253

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