Authors: Yuming Yang, Jiang Zhong, Li Jin, Jingwang Huang, Jingpeng Gao, Qing Liu, Yang Bai, Jingyuan Zhang, Rui Jiang, Kaiwen Wei
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.
Source: http://arxiv.org/abs/2502.14864v1
