Hierarchical Vision-Language Reasoning for Multimodal Multiple-Choice Question Answering

TL;DR

This paper introduces a hierarchical reasoning framework for multimodal Japanese PDF document understanding, improving semantic parsing and robustness in complex, multilingual scenarios with a novel retrieval and verification strategy.

Contribution

It presents a new hierarchical reasoning approach combined with optimized retrieval and semantic verification for better multimodal document understanding in Japanese.

Findings

Significant improvement in deep semantic parsing of complex documents

Enhanced robustness in practical multimodal scenarios

Outperforms existing models on Japanese PDF understanding tasks

Abstract

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.