# Visual-RAG: Benchmarking Text-to-Image Retrieval Augmented Generation for Visual Knowledge Intensive Queries Source: [Visual-RAG: Benchmarking Text-to-Image Retrieval Augmented Generation for Visual Knowledge Intensive Queries]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:25:52.609Z Authors: Yin Wu, Quanyu Long, Jing Li, Jianfei Yu, Wenya Wang Published: 2025-02-23T16:23:50.000Z ## Abstract Retrieval-augmented generation (RAG) is a paradigm that augments large language models (LLMs) with external knowledge to tackle knowledge-intensive question answering. While several benchmarks evaluate Multimodal LLMs (MLLMs) under Multimodal RAG settings, they predominantly retrieve from textual corpora and do not explicitly assess how models exploit visual evidence during generation. Consequently, there still lacks benchmark that isolates and measures the contribution of retrieved images in RAG. We introduce Visual-RAG, a question-answering benchmark that targets visually grounded, knowledge-intensive questions. Unlike prior work, Visual-RAG requires text-to-image retrieval and the integration of retrieved clue images to extract visual evidence for answer generation. With Visual-RAG, we evaluate 5 open-source and 3 proprietary MLLMs, showcasing that images provide strong evidence in augmented generation. However, even state-of-the-art models struggle to efficiently extract and utilize visual knowledge. Our results highlight the need for improved visual retrieval, grounding, and attribution in multimodal RAG systems. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()