Scientific data visualization plays a crucial role in research by enabling
the direct display of complex information and assisting researchers in
identifying implicit patterns. Despite its importance, the use of Large
Language Models (LLMs) for scientific data visualization remains rather
unexplored. In this study, we introduce MatPlotAgent, an efficient
model-agnostic LLM agent framework designed to automate scientific data
visualization tasks. Leveraging the capabilities of both code LLMs and
multi-modal LLMs, MatPlotAgent consists of three core modules: query
understanding, code generation with iterative debugging, and a visual feedback
mechanism for error correction. To address the lack of benchmarks in this
field, we present MatPlotBench, a high-quality benchmark consisting of 100
human-verified test cases. Additionally, we introduce a scoring approach that
utilizes GPT-4V for automatic evaluation. Experimental results demonstrate that
MatPlotAgent can improve the performance of various LLMs, including both
commercial and open-source models. Furthermore, the proposed evaluation method
shows a strong correlation with human-annotated scores.

本文介绍了 MatPlotAgent，一种高效的模型无关的大型语言模型（LLM）代理框架，用于自动化科学数据可视化任务，并提出了一个使用 GPT-4V 进行自动评估的评分方法，实验结果表明 MatPlotAgent 可以提高各种 LLM 的性能。