This paper evaluates the visualization literacy of modern Large Language Models (LLMs) and introduces a novel prompting technique called Charts-of-Thought. We tested three state-of-the-art LLMs (Claude-3.7-sonnet, GPT-4.5 preview, and Gemini-2.0-pro) on the Visualization Literacy Assessment Test (VLAT) using standard prompts and our structured approach. The Charts-of-Thought method guides LLMs through a systematic data extraction, verification, and analysis process before answering visualization questions. Our results show Claude-3.7-sonnet achieved a score of 50.17 using this method, far exceeding the human baseline of 28.82. This approach improved performance across all models, with score increases of 21.8% for GPT-4.5, 9.4% for Gemini-2.0, and 13.5% for Claude-3.7 compared to standard prompting. The performance gains were consistent across original and modified VLAT charts, with Claude correctly answering 100% of questions for several chart types that previously challenged LLMs. Our study reveals that modern multimodal LLMs can surpass human performance on visualization literacy tasks when given the proper analytical framework. These findings establish a new benchmark for LLM visualization literacy and demonstrate the importance of structured prompting strategies for complex visual interpretation tasks. Beyond improving LLM visualization literacy, Charts-of-Thought could also enhance the accessibility of visualizations, potentially benefiting individuals with visual impairments or lower visualization literacy.

翻译：本文评估了现代大语言模型的可视化素养，并引入了一种名为“思维图表”的新型提示技术。我们使用标准提示和我们的结构化方法，在可视化素养评估测试上测试了三种先进的大语言模型。思维图表方法引导大语言模型在回答可视化问题前，进行系统性的数据提取、验证和分析。我们的结果显示，Claude-3.7-sonnet 使用此方法获得了 50.17 分，远超人类基线 28.82 分。该方法提升了所有模型的性能，与标准提示相比，GPT-4.5 的分数提高了 21.8%，Gemini-2.0 提高了 9.4%，Claude-3.7 提高了 13.5%。性能提升在原版和修改版 VLAT 图表中均保持一致，Claude 在几种先前挑战大语言模型的图表类型上正确回答了 100% 的问题。我们的研究表明，当给予适当的分析框架时，现代多模态大语言模型可以在可视化素养任务上超越人类表现。这些发现为大语言模型的可视化素养设立了新的基准，并证明了结构化提示策略对于复杂视觉解释任务的重要性。除了提高大语言模型的可视化素养，思维图表还可以增强可视化的可访问性，可能使视觉障碍或可视化素养较低的个体受益。