通过视觉语言模型的零样本提示理解数据可视化中的图形感知
Understanding Graphical Perception in Data Visualization through Zero-shot Prompting of Vision-Language Models
AI总结：
该研究评估视觉语言模型零样本提示在图形感知任务上的准确率，发现其在特定任务样式下表现与人类相似，且对样式变化敏感，为用VLM建模人类可视化感知能力奠定基础。
摘要：
视觉语言模型（Vision Language Models, VLMs）已在诸多需要同时关注图表图像及其配套文本描述的图表理解任务中取得成功。然而，VLM的性能表现如何映射到类人行为，目前尚未得到充分证实。若能证明VLM具备类人的图表理解能力，便可将其应用于更广泛的任务，例如为人类读者设计和评估可视化作品。本文通过评估VLM的零样本提示在已有人类性能基准的图形感知任务上的准确率，为此类应用奠定了基础。研究结果表明，VLM在特定任务与样式组合下的表现与人类相似，这意味着它们有潜力用于建模人类性能。此外，对输入刺激的调整实验显示，即便底层数据和数据映射完全相同，VLM的准确率也会对填充颜色、图表连续性等样式变化敏感。
英文摘要：
Vision Language Models (VLMs) have been successful at many chart comprehension tasks that require attending to both the images of charts and their accompanying textual descriptions. However, it is not well established how VLM performance profiles map to human-like behaviors. If VLMs can be shown to have human-like chart comprehension abilities, they can then be applied to a broader range of tasks, such as designing and evaluating visualizations for human readers. This paper lays the foundations for such applications by evaluating the accuracy of zero-shot prompting of VLMs on graphical perception tasks with established human performance profiles. Our findings reveal that VLMs perform similarly to humans under specific task and style combinations, suggesting that they have the potential to be used for modeling human performance. Additionally, variations to the input stimuli show that VLM accuracy is sensitive to stylistic changes such as fill color and chart contiguity, even when the underlying data and data mappings are the same.
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