当视觉不是问题：在误导性数据可视化上评估视觉-语言模型
When Visuals Aren't the Problem: Evaluating Vision-Language Models on Misleading Data Visualizations
AI总结：
本文评估视觉-语言模型在识别误导性数据可视化中的能力，发现模型在检测可视化设计错误上更可靠，但常误判非误导性可视化。
摘要：
可视化有助于传达数据洞察，但误导性数据表示会扭曲其解读并传播错误信息。尽管最近的视觉-语言模型（VLMs）在许多图表理解任务上表现良好，但其检测误导性可视化的能力，特别是当欺骗源于标题中的细微推理错误时，仍不明确。本文通过结合真实世界可视化与人工编纂的误导性标题，构建了一个基准测试，以触发特定的推理和可视化错误类型，从而在不同错误类别和误导性模式之间进行受控分析。评估多种商业和开源VLM后发现，模型在检测可视化设计错误上比推理错误更可靠，且常将非误导性可视化误判为误导性。本文填补了粗略检测误导内容与归因特定推理或可视化错误之间的空白。
英文摘要：
Visualizations help communicate data insights, but deceptive data representations can distort their interpretation and propagate misinformation. While recent Vision Language Models (VLMs) perform well on many chart understanding tasks, their ability to detect misleading visualizations, especially when deception arises from subtle reasoning errors in captions, remains poorly understood. Here, we evaluate VLMs on misleading visualization-caption pairs grounded in a fine-grained taxonomy of reasoning errors (e.g., Cherry-picking, Causal inference) and visualization design errors (e.g., Truncated axis, Dual axis, inappropriate encodings). To this end, we develop a benchmark that combines real-world visualization with human-authored, curated misleading captions designed to elicit specific reasoning and visualization error types, enabling controlled analysis across error categories and modalities of misleadingness. Evaluating many commercial and open-source VLMs, we find that models detect visual design errors substantially more reliably than reasoning-based misinformation, and frequently misclassify non-misleading visualizations as deceptive. Overall, our work fills a gap between coarse detection of misleading content and the attribution of the specific reasoning or visualization errors that give rise to it.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
