Multimodal Large Language Models (MLLMs) have demonstrated remarkable multimodal understanding capabilities in Visual Question Answering (VQA) tasks by integrating visual and textual features. However, under the challenging ten-choice question evaluation paradigm, existing methods still exhibit significant limitations when processing PDF documents with complex layouts and lengthy content. Notably, current mainstream models suffer from a strong bias toward English training data, resulting in suboptimal performance for Japanese and other language scenarios. To address these challenges, this paper proposes a novel Japanese PDF document understanding framework that combines multimodal hierarchical reasoning mechanisms with Colqwen-optimized retrieval methods, while innovatively introducing a semantic verification strategy through sub-question decomposition. Experimental results demonstrate that our framework not only significantly enhances the model's deep semantic parsing capability for complex documents, but also exhibits superior robustness in practical application scenarios.
@article{arxiv.2508.16148,
title = {Hierarchical Vision-Language Reasoning for Multimodal Multiple-Choice Question Answering},
author = {Ao Zhou and Zebo Gu and Tenghao Sun and Jiawen Chen and Mingsheng Tu and Zifeng Cheng and Yafeng Yin and Zhiwei Jiang and Qing Gu},
journal= {arXiv preprint arXiv:2508.16148},
year = {2025}
}