用多智能体LLM自动生成精准科学图表，减少新手调试时间
PlotGen: Multi-Agent LLM-based Scientific Data Visualization via Multimodal Feedback
- 分步拆解用户需求，生成可执行的绘图代码
- 通过数值、文本和视觉反馈迭代优化图表准确度
- 在MatPlotBench上比基线提升4-6%，适合科研新手
科学数据可视化是将原始数据转化为可理解视觉表达的关键，有助于模式识别、预测和呈现数据洞察。然而，新手常因工具选择复杂和可视化技巧难掌握而面临困难。大型语言模型（LLMs）虽在代码生成方面展现潜力，但准确性不足且需反复调试。本文提出PlotGen，一种基于多智能体的新型框架，用于自动化生成精确的科学可视化。PlotGen协调多个基于LLM的智能体：查询规划智能体将复杂请求分解为可执行步骤，代码生成智能体将伪代码转换为可运行的Python代码，以及三个检索反馈智能体——数值反馈智能体、词汇反馈智能体和视觉反馈智能体——利用多模态LLM通过自我反思迭代优化生成图表的数据准确性、文本标签和视觉正确性。大量实验表明，PlotGen在MatPlotBench数据集上比强基线提升4-6%，显著增强用户对LLM生成图表的信任，并因减少绘图错误调试时间而提高新手生产力。
原文摘要 · Abstract (English)
Scientific data visualization is pivotal for transforming raw data into comprehensible visual representations, enabling pattern recognition, forecasting, and the presentation of data-driven insights. However, novice users often face difficulties due to the complexity of selecting appropriate tools and mastering visualization techniques. Large Language Models (LLMs) have recently demonstrated potential in assisting code generation, though they struggle with accuracy and require iterative debugging. In this paper, we propose PlotGen, a novel multi-agent framework aimed at automating the creation of precise scientific visualizations. PlotGen orchestrates multiple LLM-based agents, including a Query Planning Agent that breaks down complex user requests into executable steps, a Code Generation Agent that converts pseudocode into executable Python code, and three retrieval feedback agents - a Numeric Feedback Agent, a Lexical Feedback Agent, and a Visual Feedback Agent - that leverage multimodal LLMs to iteratively refine the data accuracy, textual labels, and visual correctness of generated plots via self-reflection. Extensive experiments show that PlotGen outperforms strong baselines, achieving a 4-6 percent improvement on the MatPlotBench dataset, leading to enhanced user trust in LLM-generated visualizations and improved novice productivity due to a reduction in debugging time needed for plot errors.
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