针对低资源场景下的长文档视觉问答，提出自适应框架提升准确率。
AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings
- 分段+检索混合架构，智能切分长文档
- 自动生成高质量问答对，提升数据稀缺时表现
- 动态推理配置与早停机制，适合资源受限场景
长文档视觉问答在低资源环境下面临上下文限制和训练数据不足的挑战。本文提出AdaDocVQA，一个统一的自适应框架，包含三项核心创新：混合文本检索架构实现高效文档分割，智能数据增强流水线通过多级验证自动生成高质量推理型问答对，以及支持动态配置生成与早停机制的自适应集成推理。在日文文档VQA基准JDocQA上，对是非题达到83.04%准确率，事实类题52.66%，数值类题44.12%；在LAVA数据集上达59%准确率。消融实验证明各组件均有效，该框架在日文文档VQA上取得新最优结果，并为其他低资源语言和专业领域提供可扩展基础。代码已开源。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。