CHAOS: Chart Analysis with Outlier Samples
This work addresses the challenge of chart interpretation robustness for researchers and practitioners in data visualization and AI, but it is incremental as it builds on existing benchmarks and models.
The authors tackled the problem of Multimodal Large Language Models (MLLMs) struggling to interpret perturbed charts by introducing CHAOS, a robustness benchmark with textual and visual perturbations, which systematically evaluated 13 state-of-the-art MLLMs on tasks like ChartQA and Chart-to-Text.
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.