Enhancing Document VQA Models via Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2508.18984v2
• Date: Thu, 28 Aug 2025 10:31:44 GMT
• Title: Enhancing Document VQA Models via Retrieval-Augmented Generation
• Authors: Eric López, Artemis Llabrés, Ernest Valveny,
• Abstract summary: Document VQA must cope with documents that span dozens of pages, yet leading systems still rely on very large vision-language models.<n>Retrieval-Augmented Generation (RAG) offers an attractive alternative, first retrieving a concise set of relevant segments before generating answers.
• Score: 1.6769365072542683
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Document Visual Question Answering (Document VQA) must cope with documents that span dozens of pages, yet leading systems still concatenate every page or rely on very large vision-language models, both of which are memory-hungry. Retrieval-Augmented Generation (RAG) offers an attractive alternative, first retrieving a concise set of relevant segments before generating answers from this selected evidence. In this paper, we systematically evaluate the impact of incorporating RAG into Document VQA through different retrieval variants - text-based retrieval using OCR tokens and purely visual retrieval without OCR - across multiple models and benchmarks. Evaluated on the multi-page datasets MP-DocVQA, DUDE, and InfographicVQA, the text-centric variant improves the "concatenate-all-pages" baseline by up to +22.5 ANLS, while the visual variant achieves +5.0 ANLS improvement without requiring any text extraction. An ablation confirms that retrieval and reranking components drive most of the gain, whereas the layout-guided chunking strategy - proposed in several recent works to leverage page structure - fails to help on these datasets. Our experiments demonstrate that careful evidence selection consistently boosts accuracy across multiple model sizes and multi-page benchmarks, underscoring its practical value for real-world Document VQA.

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