# ChatVis: Automating Scientific Visualization with a Large Language Model

arXiv: https://arxiv.org/abs/2410.11863 · HTML: https://mlsanity.com/paper/2410.11863/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## Builds on (most cited first, 8 of 8)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [A Survey on Large Language Models for Code Generation](https://mlsanity.com/paper/2406.00515/) (2024)
- [CodeGemma: Open Code Models Based on Gemma](https://mlsanity.com/paper/2406.11409/) (2024)
- [HPC-GPT: Integrating Large Language Model for High-Performance Computing](https://mlsanity.com/paper/2311.12833/) (2023)
- [LM4HPC: Towards Effective Language Model Application in High-Performance Computing](https://mlsanity.com/paper/2306.14979/) (2023)

## Built on by (most cited first, 11 of 11)

- [NLI4VolVis: Natural Language Interaction for Volume Visualization via LLM Multi-Agents and Editable 3D Gaussian Splatting](https://mlsanity.com/paper/2507.12621/) (2025)
- [ParaView-MCP: An Autonomous Visualization Agent with Direct Tool Use](https://mlsanity.com/paper/2505.07064/) (2025)
- [VizGenie: Toward Self-Refining, Domain-Aware Workflows for Next-Generation Scientific Visualization](https://mlsanity.com/paper/2507.21124/) (2025)
- [ChatVis: Large Language Model Agent for Generating Scientific Visualizations](https://mlsanity.com/paper/2507.23096/) (2025)
- [An Evaluation-Centric Paradigm for Scientific Visualization Agents](https://mlsanity.com/paper/2509.15160/) (2025)
- [IntuiTF: MLLM-Guided Transfer Function Optimization for Direct Volume Rendering](https://mlsanity.com/paper/2506.18407/) (2025)
- [TopoPilot: Reliable Conversational Workflow Automation for Topological Data Analysis and Visualization](https://mlsanity.com/paper/2603.25063/) (2026)
- [MOSAIC: Multi-agent Orchestration for Task-Intelligent Scientific Coding](https://mlsanity.com/paper/2510.08804/) (2025)
- [The Truth, the Whole Truth, and Nothing but the Truth: Automatic Visualization Evaluation from Reconstruction Quality](https://mlsanity.com/paper/2603.16873/) (2026)
- [Toward Reliable Scientific Visualization Pipeline Construction with Structure-Aware Retrieval-Augmented LLMs](https://mlsanity.com/paper/2603.16057/) (2026)
- [MedVA: An End-to-End Neuro-Symbolic Agentic System for Medical Volume Visualization](https://mlsanity.com/paper/2609.14874/) (2026)

## Abstract (arXiv)

We develop an iterative assistant we call ChatVis that can synthetically generate Python scripts for data analysis and visualization using a large language model (LLM). The assistant allows a user to specify the operations in natural language, attempting to generate a Python script for the desired operations, prompting the LLM to revise the script as needed until it executes correctly. The iterations include an error detection and correction mechanism that extracts error messages from the execution of the script and subsequently prompts LLM to correct the error. Our method demonstrates correct execution on five canonical visualization scenarios, comparing results with ground truth. We also compared our results with scripts generated by several other LLMs without any assistance. In every instance, ChatVis successfully generated the correct script, whereas the unassisted LLMs failed to do so. The code is available on GitHub: https://github.com/tanwimallick/ChatVis/.