PlotGen: Multi-Agent LLM-based Scientific Data Visualization via
  Multimodal Feedback
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.
67a5e076b94446dfc848533b
Kanika Goswami
67a5e076b94446dfc848533c
Puneet Mathur
67a5e076b94446dfc848533d
Ryan Rossi
67a5e076b94446dfc848533e
Franck Dernoncourt
claimed_verified
2025-02-07T10:30:03.421Z
62c5947524171688a9feb992
/avatars/5a151713b9eae8dc566f5957acee3475.svg
Franck Dernoncourt
user
Franck-Dernoncourt
PlotGen: Multi-Agent LLM-based Scientific Data Visualization via
  Multimodal Feedback
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.
PlotGen is a multi-agent framework that automates scientific visualization by coordinating LLM-based agents to generate accurate, reliable plots, reducing user debugging time and enhancing novice productivity.
PlotGen multi-agent framework large language models LLM-based agents Query Planning Agent Code Generation Agent Numeric Feedback Agent Lexical Feedback Agent Visual Feedback Agent multimodal LLMs self-reflection MatPlotBench