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

• URL: http://arxiv.org/abs/2508.13606v1
• Date: Tue, 19 Aug 2025 08:12:45 GMT
• Title: AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings
• Authors: Haoxuan Li, Wei Song, Aofan Liu, Peiwu Qin,
• Abstract summary: Document Visual Question Answering (Document VQA) faces significant challenges when processing long documents in low-resource environments.<n>This paper presents AdaDocVQA, a unified adaptive framework addressing these challenges through three core innovations.<n> Experiments on Japanese document VQA benchmarks demonstrate substantial improvements with 83.04% accuracy on Yes/No questions.
• Score: 8.22650587342049
• License: http://creativecommons.org/licenses/by/4.0/
• 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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