# AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Source: [AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:26:48.092Z Authors: Haoxuan Li, Wei Song, Aofan Liu, Peiwu Qin Published: 2025-08-19T08:12:45.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()