In this study, we address the growing issue of misleading charts, a prevalent problem that undermines the integrity of information dissemination. Misleading charts can distort the viewer's perception of data, leading to misinterpretations and decisions based on false information. The development of effective automatic detection methods for misleading charts is an urgent field of research. The recent advancement of multimodal Large Language Models (LLMs) has introduced a promising direction for addressing this challenge. We explored the capabilities of these models in analyzing complex charts and assessing the impact of different prompting strategies on the models' analyses. We utilized a dataset of misleading charts collected from the internet by prior research and crafted nine distinct prompts, ranging from simple to complex, to test the ability of four different multimodal LLMs in detecting over 21 different chart issues. Through three experiments--from initial exploration to detailed analysis--we progressively gained insights into how to effectively prompt LLMs to identify misleading charts and developed strategies to address the scalability challenges encountered as we expanded our detection range from the initial five issues to 21 issues in the final experiment. Our findings reveal that multimodal LLMs possess a strong capability for chart comprehension and critical thinking in data interpretation. There is significant potential in employing multimodal LLMs to counter misleading information by supporting critical thinking and enhancing visualization literacy. This study demonstrates the applicability of LLMs in addressing the pressing concern of misleading charts.

翻译：本研究针对日益严重的误导性图表问题展开探讨，该问题已成为损害信息传播完整性的普遍现象。误导性图表可能扭曲观察者对数据的认知，导致基于错误信息的误判与决策。开发有效的误导性图表自动检测方法是当前亟待推进的研究领域。近期多模态大型语言模型（LLMs）的发展为应对这一挑战提供了新方向。我们探究了这些模型在分析复杂图表方面的能力，并评估了不同提示策略对模型分析效果的影响。我们采用先前研究从互联网收集的误导性图表数据集，设计了从简单到复杂的九种提示方案，测试了四种不同多模态LLMs在检测超过21类图表问题中的表现。通过从初步探索到精细分析的三个系列实验，我们逐步掌握了如何有效提示LLMs识别误导性图表的方法，并制定了应对检测范围从最初五类问题扩展到最终实验21类问题时所遇可扩展性挑战的策略。研究结果表明，多模态LLMs在图表理解和数据解读的批判性思维方面展现出强大能力。通过支持批判性思维和提升可视化素养，运用多模态LLMs应对误导性信息具有显著潜力。本研究论证了LLMs在解决误导性图表这一紧迫问题中的适用性。