Chart-MRAG: Benchmarking Multimodal Retrieval Augmented Generation on Chart-based Documents

ymyang, Jiang Zhong, Li Jin, Xiao Sun, Jingwang Huang, gaojinpeng, Qing Liu, Yang Bai, Jingyuan Zhang, Rui Jiang, Qin Lei, 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, $\textbf{Chart-based MRAG}$, to address this limitation.
To generate high-quality evaluation samples, we propose $\textbf{CHARGE} $($\textbf{CHAR}$t-based document question-answering $\textbf{GE}$neration), a semi-automatic framework for generating evaluation samples through multi-modal keypoint extraction, knowledge graph construction, and qa pair synthesis.
By combining CHARGE with expert validation, we construct $\textbf{Chart-MRAG Bench}$, a comprehensive benchmark for chart-based MRAG evaluation, featuring 4,738 question-answering pairs across 8 domains from real-world documents.
Our experiments reveal 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 Multimodal Large Language Models (MLLMs) achieve only 71.15\% Correctness and 80.74\% Coverage scores, and (3) Widely-used MLLMs demonstrate consistent text-over-visual modality bias.
These findings highlight great challenges in processing information-dense visual formats.