CHAOS: Chart Analysis with Outlier Samples

• URL: http://arxiv.org/abs/2505.17235v1
• Date: Thu, 22 May 2025 19:26:49 GMT
• Title: CHAOS: Chart Analysis with Outlier Samples
• Authors: Omar Moured, Yufan Chen, Ruiping Liu, Simon Reiß, Philip Torr, Jiaming Zhang, Rainer Stiefelhagen,
• Abstract summary: CHAOS is a benchmark to evaluate Multimodal Large Language Models (MLLMs) against chart perturbations.<n>The benchmark includes 13 state-of-the-art MLLMs divided into three groups according to the training scope and data.
• Score: 31.64244745491319
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Charts play a critical role in data analysis and visualization, yet real-world applications often present charts with challenging or noisy features. However, "outlier charts" pose a substantial challenge even for Multimodal Large Language Models (MLLMs), which can struggle to interpret perturbed charts. In this work, we introduce CHAOS (CHart Analysis with Outlier Samples), a robustness benchmark to systematically evaluate MLLMs against chart perturbations. CHAOS encompasses five types of textual and ten types of visual perturbations, each presented at three levels of severity (easy, mid, hard) inspired by the study result of human evaluation. The benchmark includes 13 state-of-the-art MLLMs divided into three groups (i.e., general-, document-, and chart-specific models) according to the training scope and data. Comprehensive analysis involves two downstream tasks (ChartQA and Chart-to-Text). Extensive experiments and case studies highlight critical insights into robustness of models across chart perturbations, aiming to guide future research in chart understanding domain. Data and code are publicly available at: http://huggingface.co/datasets/omoured/CHAOS.

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