Misleading chart visualizations, which intentionally manipulate data representations to support specific claims, can distort perceptions and lead to incorrect conclusions. Despite decades of research, misleading visualizations remain a widespread and pressing issue. Recent advances in multimodal large language models (MLLMs) have demonstrated strong chart comprehension capabilities, yet no existing work has systematically evaluated their ability to detect and interpret misleading charts. This paper introduces the Misleading Chart Question Answering (Misleading ChartQA) Benchmark, a large-scale multimodal dataset designed to assess MLLMs in identifying and reasoning about misleading charts. It contains over 3,000 curated examples, covering 21 types of misleaders and 10 chart types. Each example includes standardized chart code, CSV data, and multiple-choice questions with labeled explanations, validated through multi-round MLLM checks and exhausted expert human review. We benchmark 16 state-of-the-art MLLMs on our dataset, revealing their limitations in identifying visually deceptive practices. We also propose a novel pipeline that detects and localizes misleaders, enhancing MLLMs' accuracy in misleading chart interpretation. Our work establishes a foundation for advancing MLLM-driven misleading chart comprehension. We publicly release the sample dataset to support further research in this critical area.

翻译：误导性图表可视化通过有意操纵数据呈现方式来支持特定主张，可能扭曲认知并导致错误结论。尽管历经数十年研究，误导性可视化仍是普遍存在的紧迫问题。近期多模态大语言模型（MLLMs）的发展已展现出强大的图表理解能力，但现有研究尚未系统评估其检测与解读误导性图表的能力。本文提出误导性图表问答（Misleading ChartQA）基准测试，这是一个为评估MLLMs识别和推理误导性图表而设计的大规模多模态数据集。该数据集包含3,000余个精选示例，涵盖21种误导类型和10种图表类型。每个示例均包含标准化图表代码、CSV数据及带有标注解释的多选题，并经过多轮MLLM校验和详尽的专家人工审核验证。我们在该数据集上对16个前沿MLLMs进行基准测试，揭示了其在识别视觉欺骗性操作方面的局限性。同时，我们提出一种能检测并定位误导元素的新型流程，显著提升了MLLMs在误导性图表解读中的准确性。本研究为推进MLLM驱动的误导性图表理解奠定了理论基础。我们公开释放部分样本数据集，以支持这一关键领域的深入研究。