标题： PlotEdit：通过多模态大语言模型代理实现PDF中可访问的图表编辑的自然语言驱动方法 显示英文标题

标题： PlotEdit: Natural Language-Driven Accessible Chart Editing in PDFs via Multimodal LLM Agents

摘要： 图表可视化虽然对于数据解释和交流至关重要，但通常只能以PDF中的图像形式访问，缺乏源数据表和样式信息。 为了实现对PDF或数字扫描中图表的有效编辑，我们提出了PlotEdit，这是一种新的多智能体框架，通过自省的大语言模型代理实现自然语言驱动的端到端图表图像编辑。 PlotEdit协调五个大语言模型代理：(1) Chart2Table用于数据表提取，(2) Chart2Vision用于风格属性识别，(3) Chart2Code用于检索渲染代码，(4) 指令分解代理用于将用户请求解析为可执行步骤，以及(5) 多模态编辑代理用于实现细微的图表组件修改——所有操作均通过多模态反馈协调，以保持视觉保真度。 PlotEdit在ChartCraft数据集上，在风格、布局、格式和以数据为中心的编辑方面优于现有基线，提高了视障用户的可访问性并提升了新手的生产力。 显示英文摘要

摘要： Chart visualizations, while essential for data interpretation and communication, are predominantly accessible only as images in PDFs, lacking source data tables and stylistic information. To enable effective editing of charts in PDFs or digital scans, we present PlotEdit, a novel multi-agent framework for natural language-driven end-to-end chart image editing via self-reflective LLM agents. PlotEdit orchestrates five LLM agents: (1) Chart2Table for data table extraction, (2) Chart2Vision for style attribute identification, (3) Chart2Code for retrieving rendering code, (4) Instruction Decomposition Agent for parsing user requests into executable steps, and (5) Multimodal Editing Agent for implementing nuanced chart component modifications - all coordinated through multimodal feedback to maintain visual fidelity. PlotEdit outperforms existing baselines on the ChartCraft dataset across style, layout, format, and data-centric edits, enhancing accessibility for visually challenged users and improving novice productivity.