MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization
CoRR(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要