PlotEdit: Natural Language-Driven Accessible Chart Editing in PDFs via Multimodal LLM Agents
This improves accessibility for visually challenged users and novice productivity by making chart editing more intuitive and efficient.
The paper tackles the problem of editing chart images in PDFs, which lack source data and style information, by introducing PlotEdit, a multi-agent framework that uses natural language to enable end-to-end chart editing, outperforming baselines on the ChartCraft dataset across various edit types.
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.