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arXiv · 2508.13606

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings

Haoxuan Li·Wei Song·Aofan Liu·Peiwu Qin
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Abstract

Document Visual Question Answering (Document VQA) faces significant challenges when processing long documents in low-resource environments due to context limitations and insufficient training data. This paper presents AdaDocVQA, a unified adaptive framework addressing these challenges through three core innovations: a hybrid text retrieval architecture for effective document segmentation, an intelligent data augmentation pipeline that automatically generates high-quality reasoning question-answer pairs with multi-level verification, and adaptive ensemble inference with dynamic configuration generation and early stopping mechanisms. Experiments on Japanese document VQA benchmarks demonstrate substantial improvements with 83.04\% accuracy on Yes/No questions, 52.66\% on factual questions, and 44.12\% on numerical questions in JDocQA, and 59\% accuracy on LAVA dataset. Ablation studies confirm meaningful contributions from each component, and our framework establishes new state-of-the-art results for Japanese document VQA while providing a scalable foundation for other low-resource languages and specialized domains. Our code available at: https://github.com/Haoxuanli-Thu/AdaDocVQA.

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Haoxuan Li, Wei Song, Aofan Liu, Peiwu Qin. 2025-08-19. AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings. https://arxiv.org/abs/2508.13606

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