🤖 AI Summary
Document understanding (DU) in long-context and complex-layout scenarios remains hindered by the scarcity of fine-grained annotations—particularly for low-resource languages like Arabic, which heavily rely on costly manual labeling. To address this, we propose the first fully automated multi-agent interaction framework that synergistically integrates structured prompting, cross-lingual question generation, and layout-aware mechanisms to efficiently synthesize high-quality single-page and multi-page QA pairs in English and Arabic. The resulting benchmark, AraEngLongBench, exhibits rich linguistic diversity, intricate document layouts, and substantial long-context challenges, imposing rigorous evaluation pressure on both mainstream open-source and proprietary vision-language models. Our approach breaks from traditional manual annotation paradigms, significantly improving scalability, fidelity, and efficiency in long-document DU data synthesis. It establishes a viable, extensible technical pathway for advancing DU in low-resource languages.
📝 Abstract
Document Understanding (DU) in long-contextual scenarios with complex layouts remains a significant challenge in vision-language research. Although Large Vision-Language Models (LVLMs) excel at short-context DU tasks, their performance declines in long-context settings. A key limitation is the scarcity of fine-grained training data, particularly for low-resource languages such as Arabic. Existing state-of-the-art techniques rely heavily on human annotation, which is costly and inefficient. We propose a fully automated, multi-agent interactive framework to generate long-context questions efficiently. Our approach efficiently generates high-quality single- and multi-page questions for extensive English and Arabic documents, covering hundreds of pages across diverse domains. This facilitates the development of LVLMs with enhanced long-context understanding ability. Experimental results in this work have shown that our generated English and Arabic questions ( extbf{AraEngLongBench}) are quite challenging to major open- and close-source LVLMs. The code and data proposed in this work can be found in https://github.com/wangk0b/Multi_Agentic_QA_Long_Doc.git. Sample Question and Answer (QA) pairs and structured system prompts can be found in the Appendix.