PlotGen: Multi-Agent LLM-based Scientific Data Visualization via Multimodal Feedback (2502.00988v1)

Abstract: 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. 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.

• The paper introduces a multi-agent framework that employs LLMs and multimodal feedback to iteratively refine scientific data visualizations.
• It integrates specialized agents—Query Planning, Code Generation, and Numeric, Lexical, and Visual Feedback—to automate each visualization step.
• Experimental results on the MatPlotBench dataset demonstrate a 4-6% improvement over state-of-the-art methods, reducing common plotting errors.

PlotGen: Multi-Agent LLM-Based Scientific Data Visualization via Multimodal Feedback

In the increasingly complex field of scientific data visualization, the transition from raw data to comprehensible visuals is crucial for effective analysis and communication of information. The paper "PlotGen: Multi-Agent LLM-based Scientific Data Visualization via Multimodal Feedback" introduces a novel framework, PlotGen, which aims to streamline this transition by leveraging LLMs in a multi-agent setup. This approach is designed to enhance the automation of accurate scientific visualizations, addressing the challenges faced by users, particularly novices, who struggle with existing tools and techniques.

PlotGen employs a multi-agent strategy consisting of several distinct LLM-based agents that collaboratively handle different stages of data visualization. These agents include a Query Planning Agent, a Code Generation Agent, and three distinct feedback agents: Numeric, Lexical, and Visual Feedback Agents. Each agent leverages multimodal feedback to refine and enhance the visualization process iteratively through self-reflection. This prevents common errors associated with LLM-driven code generation, such as inaccuracies in data plotting, textual labeling, and visual elements, which often require iterative debugging and user intervention.

Key Methodologies and Components

1. Query Planning Agent: This agent decomposes complex visualization requests into executable steps, preparing precise instructions for creating visualizations according to user requirements.
2. Code Generation Agent: Utilizing LLMs like GPT-3.5 and GPT-4, this agent translates the structured plans into Python code, automating the creation of the initial draft of visualizations. This phase addresses syntax and transformation tasks but requires iterative refinement to achieve a high-fidelity output.
3. Multimodal Feedback Agents:
   • Numeric Feedback Agent: Ensures that the data trends in the visualization align with the user-provided datasets and prevents errors in plot type.
   • Lexical Feedback Agent: Focuses on the accuracy of labels and text within the visualizations, ensuring they meet user specifications.
   • Visual Feedback Agent: Enhances aesthetic and layout qualities to match user-defined parameters.

Each of these agents synergistically improves the code generation process by providing targeted feedback, leading to more accurate final visualizations.

Experimental Validation

PlotGen's performance was robustly evaluated using the MatPlotBench dataset, demonstrating a 4-6% improvement over existing state-of-the-art techniques. Notably, it performed well across different LLM configurations, highlighting its adaptability. The results underscore its effectiveness in significantly reducing errors and enhancing accuracy in LLM-generated visualizations.

Implications and Future Directions

The development and validation of PlotGen underscore its potential utility in democratizing access to sophisticated visualization techniques for users with limited technical expertise. By reducing the barrier to effective data visualization, PlotGen not only enhances novice productivity but also boosts trust in LLM-generated outputs due to decreased likelihood of plot errors.

The theoretical implications extend to the field of LLM research, particularly in understanding how multimodal feedback can enhance iterative learning and correction in automated processes. The practical applications of PlotGen may evolve to include real-time interactive visualization, potentially integrating with virtual reality and other immersive technologies, broadening the scope and impact of scientific communication tools.

In conclusion, PlotGen represents a compelling integration of LLM capabilities into a multi-agent framework that automates scientific visualization, promising improved accuracy and accessibility in data-driven communication. As AI technologies advance, frameworks like PlotGen will likely play an increasingly central role in bridging the gap between raw data and actionable insights in scientific research.