测试大模型生成可视化代码与理解图表的能力。
Evaluating LLMs for Visualization Tasks
- 用简单提示生成可视化代码，测试大模型编程能力。
- 能正确回答部分关于常见图表的问题，但准确率有限。
- 揭示大模型在可视化任务中的潜力与局限，适合研究者参考。
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简单问题的表现。研究发现，大模型能够生成部分可视化代码，并对图表内容作出合理回答。然而，模型仍存在显著局限性。我们的研究为改进大模型及信息可视化系统提供了重要洞见。
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 simple questions. Our study shows that LLMs could generate code for some visualizations as well as answer questions about them. However, LLMs also have several limitations. We believe that our insights can be used to improve both LLMs and Information Visualization systems.
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