"PlotGen: Multi-Agent LLM-based Scientific Data Visualization via Multimodal Feedback"
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https://arxiv.org/abs/2502.00988
The creation of scientific data visualizations is challenging for novice users due to the complexity of tools and visualization techniques. Existing LLMs struggle with accuracy in generating visualization code and require iterative debugging.
This paper introduces PlotGen, a multi-agent framework that utilizes multimodal feedback to iteratively refine and enhance the accuracy of scientific visualizations generated by LLMs.
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📌 PlotGen effectively decomposes the complex visualization task into modular agents. Each agent specializes in query planning, code generation, or multimodal feedback. This division of labor enables targeted error correction and enhances overall accuracy.
📌 Multimodal feedback is a key innovation. PlotGen uses visual, lexical, and numerical feedback agents. These agents mimic human sensory input to iteratively refine plots generated by LLMs, addressing inherent code generation inaccuracies.
📌 PlotGen improves accessibility for novice users. By automating debugging through self-reflection and multimodal feedback, it lowers the technical barrier to creating accurate scientific visualizations, enhancing user productivity.
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Methods Explored in this Paper 🔧:
→ PlotGen employs a multi-agent framework to automate scientific data visualization.
→ It starts with a Query Planning Agent. This agent breaks down complex user requests into a sequence of executable steps using chain-of-thought prompting.
→ A Code Generation Agent then converts these steps into executable Python code for plotting. This agent also includes a self-debugging mechanism to handle code execution errors.
→ PlotGen incorporates three feedback agents to refine the visualizations. These are Numeric, Lexical, and Visual Feedback Agents.
→ The Numeric Feedback Agent ensures data accuracy and correct plot type by comparing de-rendered data from the plot with the original data.
→ The Lexical Feedback Agent verifies the accuracy of textual elements like titles and labels by comparing them to user requirements and data.
→ The Visual Feedback Agent assesses visual aspects such as color schemes and layout to ensure alignment with user specifications.
→ These feedback agents provide iterative feedback to the Code Generation Agent for self-reflection and refinement of the generated plots.
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Key Insights 💡:
→ Multimodal feedback is crucial for enhancing the accuracy of LLM generated scientific visualizations.
→ PlotGen leverages visual, lexical, and numerical feedback to rectify errors in data, text, and visual aesthetics, through self-reflection.
→ This multi-agent approach significantly improves the quality and trustworthiness of LLM generated plots for scientific data visualization.
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Results 📊:
→ PlotGen outperforms baseline methods such as Direct Decoding, Zero-Shot Chain-of-thought, and MatPlotAgent on the MatPlotBench dataset.
→ PlotGen achieves a 4-6% performance improvement over these strong baselines across various LLM configurations.
→ User evaluations indicate increased user trust in PlotGen generated visualizations and a reduction in debugging time for novice users.