评测大模型生成与理解可视化图表的能力及局限。
Evaluating LLMs for Visualization Generation and Understanding
- 用简单提示生成柱状图、饼图等基础图表代码。
- 能回答关于图表的简单问题，但对复杂图表易出错。
- 适合关注大模型在数据可视化中应用的研究者参考。
信息可视化常用于从复杂数据中获取洞察。近年来，大型语言模型（LLMs）在多项任务中表现优异。本文评估了多种主流大模型基于简单提示生成可视化代码的能力，并分析其理解常见图表并回答相关问题的表现。结果显示，大模型可成功生成柱状图、饼图等简单图表的代码，也能回答部分基础问题。然而，它们在生成小提琴图等复杂图表时存在困难，且在识别邻近边界关系、判断形状长度等细节上出现错误。研究揭示了当前大模型在可视化任务中的能力边界，为优化大模型与信息可视化系统提供参考。
原文摘要 · Abstract (English)
Information Visualization has been utilized to gain insights from complex data. In recent times, Large Language models (LLMs) have performed very well in many tasks. In this paper, we showcase the capabilities of different popular LLMs to generate code for visualization based on simple prompts. We also analyze the power of LLMs to understand some common visualizations by answering questions. Our study shows that LLMs could generate code for some simpler visualizations such as bar and pie charts. Moreover, they could answer simple questions about visualizations. However, LLMs also have several limitations. For example, some of them had difficulty generating complex visualizations, such as violin plot. LLMs also made errors in answering some questions about visualizations, for example, identifying relationships between close boundaries and determining lengths of shapes. We believe that our insights can be used to improve both LLMs and Information Visualization systems.
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