Hierarchical Vision-Language Reasoning for Multimodal Multiple-Choice Question Answering
This addresses a domain-specific problem of improving multimodal question answering for Japanese PDF documents, representing an incremental advance over existing methods.
The paper tackles the problem of multimodal multiple-choice question answering on Japanese PDF documents with complex layouts, where existing models perform poorly due to English bias. The proposed framework combining hierarchical reasoning, optimized retrieval, and semantic verification significantly enhances deep semantic parsing and robustness.
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.