使用大型视觉语言模型评估科学出版物图表对可视化指南的合规性
Evaluating Compliance with Visualization Guidelines in Diagrams for Scientific Publications Using Large Vision Language Models
AI总结：
本文评估开源VLM与提示策略识别科学图表是否符合可视化指南的能力，发现Qwen2.5VL及总结式提示整体最佳，可可靠检测多类图表问题。
摘要：
图表被广泛用于在出版物中可视化数据。数据可视化研究领域负责定义创建和使用这些图表的原则与指南，而研究人员往往并不了解或未遵守这些原则和指南，从而因提供不准确或不完整的信息导致错误信息。在本研究中，大型视觉语言模型（VLM）被用于分析图表，以识别与所选数据可视化原则和指南相关的潜在问题。为确定VLM对这些任务的适用性，研究使用一组源自所选数据可视化指南的问题，对五个开源VLM和五种提示策略进行了比较。结果表明，所采用的VLM能够很好地准确分析图表类型（F1分数82.49%）、3D效果（F1分数98.55%）、坐标轴标签（F1分数76.74%）、线条（RMSE 1.16）、颜色（RMSE 1.60）和图例（F1分数96.64%，RMSE 0.70），但它们无法可靠地提供有关图像质量（F1分数0.74%）和刻度线/标签（F1分数46.13%）的反馈。在所采用的VLM中，Qwen2.5VL表现最佳，而总结式提示策略在大多数实验问题上表现最佳。研究表明，VLM可用于自动识别图表中的若干潜在问题，例如缺失坐标轴标签、缺失图例以及不必要的3D效果。本研究提出的方法还可以扩展到数据可视化的其他方面。
英文摘要：
Diagrams are widely used to visualize data in publications. The research field of data visualization deals with defining principles and guidelines for the creation and use of these diagrams, which are often not known or adhered to by researchers, leading to misinformation caused by providing inaccurate or incomplete information. In this work, large Vision Language Models (VLMs) are used to analyze diagrams in order to identify potential problems in regards to selected data visualization principles and guidelines. To determine the suitability of VLMs for these tasks, five open source VLMs and five prompting strategies are compared using a set of questions derived from selected data visualization guidelines. The results show that the employed VLMs work well to accurately analyze diagram types (F1-score 82.49 %), 3D effects (F1-score 98.55 %), axes labels (F1-score 76.74 %), lines (RMSE 1.16), colors (RMSE 1.60) and legends (F1-score 96.64 %, RMSE 0.70), while they cannot reliably provide feedback about the image quality (F1-score 0.74 %) and tick marks/labels (F1-score 46.13 %). Among the employed VLMs, Qwen2.5VL performs best, and the summarizing prompting strategy performs best for most of the experimental questions. It is shown that VLMs can be used to automatically identify a number of potential issues in diagrams, such as missing axes labels, missing legends, and unnecessary 3D effects. The approach laid out in this work can be extended for further aspects of data visualization.
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。
