Loud and Clear: Making Visual Evidence Count in Multimodal Document RAG
Multimodal document retrieval-augmented generation (RAG) has focused largely on retrieval, yet its answers depend on how well a vision-language model reads the retrieved pages.
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Key points
- We find that evidence in retrieved pages remains underused even when the correct pages are retrieved.
- On SciEGQA oracle pages, five prior methods give the generator 0.7K to 2.7K visual tokens per query on average under a common image processor, yet raising the page-only budget from 1K to 32K improves accuracy by 15.22 points.
- We study what the generator needs from retrieved pages through two requirements: Loud, making full use of visual token capacity, and Clear, refining the page so that its evidence becomes prominent.
- We implement both as Loud and Clear (LoC), which combines higher-resolution pages with OCR from query-relevant regions without ground-truth annotations or additional training.
Sources (1)
- [1]Loud and Clear: Making Visual Evidence Count in Multimodal Document RAGarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 06:08 AM
Multimodal document retrieval-augmented generation (RAG) has focused largely on retrieval, yet its answers depend on how well a vision-language model reads the retrieved pages.
We find that evidence in retrieved pages remains underused even when the correct pages are retrieved.
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