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Enhancing Document VQA Models via Retrieval-Augmented Generation
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AI
DOI：10.1007/978-3-032-09368-4_6.png)
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Abstract
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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.
Keywords:
Document Visual Question Answering
Multi-Page Scenario
Multi-Modal Scenario
Retrieval-Augmented Generation
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