PlotGen: Multi-Agent LLM-based Scientific Data Visualization via Multimodal Feedback
- URL: http://arxiv.org/abs/2502.00988v1
- Date: Mon, 03 Feb 2025 02:00:29 GMT
- Title: PlotGen: Multi-Agent LLM-based Scientific Data Visualization via Multimodal Feedback
- Authors: Kanika Goswami, Puneet Mathur, Ryan Rossi, Franck Dernoncourt,
- Abstract summary: We propose PlotGen, a novel multi-agent framework aimed at the creation of precise scientific visualizations.
PlotGen orchestrates multiple.
retrieval agents, including a Query Planning Agent that breaks.
down complex user requests into executable code, and three.
retrieval feedback agents.
Experiments show that PlotGen outperforms strong baselines, achieving a 4-6 percent improvement on the MatBench dataset.
- Score: 47.79080056618323
- License:
- 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. 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.
Related papers
- nvAgent: Automated Data Visualization from Natural Language via Collaborative Agent Workflow [9.676697360425196]
Natural Language to Visualization (NL2Vis) seeks to convert natural-language descriptions into visual representations of given tables.
We propose a collaborative agent workflow, termed nvAgent, for NL2Vis.
Comprehensive evaluations on the new VisEval benchmark demonstrate that nvAgent consistently surpasses state-of-the-art baselines.
arXiv Detail & Related papers (2025-02-07T16:03:08Z) - Iris: Breaking GUI Complexity with Adaptive Focus and Self-Refining [67.87810796668981]
Information-Sensitive Cropping (ISC) and Self-Refining Dual Learning (SRDL)
Iris achieves state-of-the-art performance across multiple benchmarks with only 850K GUI annotations.
These improvements translate to significant gains in both web and OS agent downstream tasks.
arXiv Detail & Related papers (2024-12-13T18:40:10Z) - Web-Scale Visual Entity Recognition: An LLM-Driven Data Approach [56.55633052479446]
Web-scale visual entity recognition presents significant challenges due to the lack of clean, large-scale training data.
We propose a novel methodology to curate such a dataset, leveraging a multimodal large language model (LLM) for label verification, metadata generation, and rationale explanation.
Experiments demonstrate that models trained on this automatically curated data achieve state-of-the-art performance on web-scale visual entity recognition tasks.
arXiv Detail & Related papers (2024-10-31T06:55:24Z) - RGD: Multi-LLM Based Agent Debugger via Refinement and Generation Guidance [0.6062751776009752]
Large Language Models (LLMs) have shown incredible potential in code generation tasks.
LLMs can generate code based on task descriptions, but accuracy remains limited.
We introduce a novel architecture of LLM-based agents for code generation and automatic debug: Refinement and Guidance debugger (RGD)
RGD decomposes the code generation task into multiple steps, ensuring a clearer workflow and enabling iterative code refinement based on self-reflection and feedback.
arXiv Detail & Related papers (2024-10-02T05:07:02Z) - Data Formulator 2: Iteratively Creating Rich Visualizations with AI [65.48447317310442]
We present Data Formulator 2, an LLM-powered visualization system to address these challenges.
With Data Formulator 2, users describe their visualization intent with blended UI and natural language inputs, and data transformation are delegated to AI.
To support iteration, Data Formulator 2 lets users navigate their iteration history and reuse previous designs towards new ones so that they don't need to start from scratch every time.
arXiv Detail & Related papers (2024-08-28T20:12:17Z) - AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoning [93.96463520716759]
Large language model (LLM) agents have demonstrated impressive capabilities in utilizing external tools and knowledge to boost accuracy and hallucinations.
Here, we introduce AvaTaR, a novel and automated framework that optimize an LLM agent to effectively leverage provided tools, improving performance on a given task.
arXiv Detail & Related papers (2024-06-17T04:20:02Z) - MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization [86.61052121715689]
MatPlotAgent is a model-agnostic framework designed to automate scientific data visualization tasks.
MatPlotBench is a high-quality benchmark consisting of 100 human-verified test cases.
arXiv Detail & Related papers (2024-02-18T04:28:28Z) - PiVe: Prompting with Iterative Verification Improving Graph-based Generative Capability of LLMs [28.33598529903845]
We show how a small language model could be trained to act as a verifier module for the output of an large language model.
We also show how the verifier module could apply iterative corrections offline for a more cost-effective solution to the text-to-graph generation task.
arXiv Detail & Related papers (2023-05-21T08:11:24Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.