Benchmarking Multimodal RAG through a Chart-based Document Question-Answering Generation Framework

• URL: http://arxiv.org/abs/2502.14864v1
• Date: Thu, 20 Feb 2025 18:59:42 GMT
• Title: Benchmarking Multimodal RAG through a Chart-based Document Question-Answering Generation Framework
• Authors: Yuming Yang, Jiang Zhong, Li Jin, Jingwang Huang, Jingpeng Gao, Qing Liu, Yang Bai, Jingyuan Zhang, Rui Jiang, Kaiwen Wei,
• Abstract summary: Multimodal Retrieval-Augmented Generation (MRAG) enhances reasoning capabilities by integrating external knowledge.<n>Existing benchmarks primarily focus on simple image-text interactions, overlooking complex visual formats like charts that are prevalent in real-world applications.<n>We propose CHARt-based document question-answering GEneration (CHARGE), a framework that produces evaluation data through structured keypoint extraction, crossmodal verification, and keypoint-based generation.
• Score: 17.838177710655287
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal Retrieval-Augmented Generation (MRAG) enhances reasoning capabilities by integrating external knowledge. However, existing benchmarks primarily focus on simple image-text interactions, overlooking complex visual formats like charts that are prevalent in real-world applications. In this work, we introduce a novel task, Chart-based MRAG, to address this limitation. To semi-automatically generate high-quality evaluation samples, we propose CHARt-based document question-answering GEneration (CHARGE), a framework that produces evaluation data through structured keypoint extraction, crossmodal verification, and keypoint-based generation. By combining CHARGE with expert validation, we construct Chart-MRAG Bench, a comprehensive benchmark for chart-based MRAG evaluation, featuring 4,738 question-answering pairs across 8 domains from real-world documents. Our evaluation reveals three critical limitations in current approaches: (1) unified multimodal embedding retrieval methods struggles in chart-based scenarios, (2) even with ground-truth retrieval, state-of-the-art MLLMs achieve only 58.19% Correctness and 73.87% Coverage scores, and (3) MLLMs demonstrate consistent text-over-visual modality bias during Chart-based MRAG reasoning. The CHARGE and Chart-MRAG Bench are released at https://github.com/Nomothings/CHARGE.git.

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