# AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization

arXiv: https://arxiv.org/abs/2102.01330 · HTML: https://mlsanity.com/paper/2102.01330/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2021.

## Built on by (most cited first, 30 of 32)

- [From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models](https://mlsanity.com/paper/2403.12027/) (2024)
- [DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization](https://mlsanity.com/paper/2204.06504/) (2022)
- [Foundation Models Meet Visualizations: Challenges and Opportunities](https://mlsanity.com/paper/2310.05771/) (2023)
- [LLM4Vis: Explainable Visualization Recommendation using ChatGPT](https://mlsanity.com/paper/2310.07652/) (2023)
- [Generative AI for Visualization: State of the Art and Future Directions](https://mlsanity.com/paper/2404.18144/) (2024)
- [LightVA: Lightweight Visual Analytics with LLM Agent-Based Task Planning and Execution](https://mlsanity.com/paper/2411.05651/) (2024)
- [Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration](https://mlsanity.com/paper/2309.15723/) (2023)
- [InterChat: Enhancing Generative Visual Analytics using Multimodal Interactions](https://mlsanity.com/paper/2503.04110/) (2025)
- [EncQA: Benchmarking Vision-Language Models on Visual Encodings for Charts](https://mlsanity.com/paper/2508.04650/) (2025)
- [ParaView-MCP: An Autonomous Visualization Agent with Direct Tool Use](https://mlsanity.com/paper/2505.07064/) (2025)
- [AnaMeta: A Table Understanding Dataset of Field Metadata Knowledge Shared by Multi-dimensional Data Analysis Tasks](https://mlsanity.com/paper/2209.00946/) (2022)
- [Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytelling](https://mlsanity.com/paper/2304.08366/) (2023)
- [Explainable XR: Understanding User Behaviors of XR Environments using LLM-assisted Analytics Framework](https://mlsanity.com/paper/2501.13778/) (2025)
- [RebuttalAgent: Strategic Persuasion in Academic Rebuttal via Theory of Mind](https://mlsanity.com/paper/2601.15715/) (2026)
- [Vital Insight: Assisting Experts' Context-Driven Sensemaking of Multi-modal Personal Tracking Data Using Visualization and Human-In-The-Loop LLM](https://mlsanity.com/paper/2410.14879/) (2024)
- [Evaluating LLMs for Visualization Generation and Understanding](https://mlsanity.com/paper/2507.22890/) (2025)
- [Agnostic Visual Recommendation Systems: Open Challenges and Future Directions](https://mlsanity.com/paper/2302.00569/) (2023)
- [DeepVATS: Deep Visual Analytics for Time Series](https://mlsanity.com/paper/2302.03858/) (2023)
- [TopoPilot: Reliable Conversational Workflow Automation for Topological Data Analysis and Visualization](https://mlsanity.com/paper/2603.25063/) (2026)
- [Adversarial Attacks on Machine Learning-Aided Visualizations](https://mlsanity.com/paper/2409.02485/) (2024)
- [LLM-Assisted Visual Analytics: Opportunities and Challenges](https://mlsanity.com/paper/2409.02691/) (2024)
- [Towards Automated Cross-domain Exploratory Data Analysis through Large Language Models](https://mlsanity.com/paper/2412.07214/) (2024)
- [Attention is All They Need: Exploring the Media Archaeology of the Computer Vision Research Paper](https://mlsanity.com/paper/2209.11200/) (2022)
- [MDSF: Context-Aware Multi-Dimensional Data Storytelling Framework based on Large language Model](https://mlsanity.com/paper/2501.01014/) (2025)
- [FactFlow: Automatic Fact Sheet Generation and Customization from Tabular Dataset via AI Chain Design & Implementation](https://mlsanity.com/paper/2502.17909/) (2025)
- [Seeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization](https://mlsanity.com/paper/2503.00228/) (2025)
- [Reflection on Data Storytelling Tools in the Generative AI Era from the Human-AI Collaboration Perspective](https://mlsanity.com/paper/2503.02631/) (2025)
- [Story Ribbons: Reimagining Storyline Visualizations with Large Language Models](https://mlsanity.com/paper/2508.06772/) (2025)
- [A Generative AI System for Biomedical Data Discovery with Grammar-Based Visualizations](https://mlsanity.com/paper/2509.16454/) (2025)
- [YAC: Bridging Natural Language and Interactive Visual Exploration with Generative AI for Biomedical Data Discovery](https://mlsanity.com/paper/2509.19182/) (2025)

## Abstract (arXiv)

Visualizations themselves have become a data format. Akin to other data formats such as text and images, visualizations are increasingly created, stored, shared, and (re-)used with artificial intelligence (AI) techniques. In this survey, we probe the underlying vision of formalizing visualizations as an emerging data format and review the recent advance in applying AI techniques to visualization data (AI4VIS). We define visualization data as the digital representations of visualizations in computers and focus on data visualization (e.g., charts and infographics). We build our survey upon a corpus spanning ten different fields in computer science with an eye toward identifying important common interests. Our resulting taxonomy is organized around WHAT is visualization data and its representation, WHY and HOW to apply AI to visualization data. We highlight a set of common tasks that researchers apply to the visualization data and present a detailed discussion of AI approaches developed to accomplish those tasks. Drawing upon our literature review, we discuss several important research questions surrounding the management and exploitation of visualization data, as well as the role of AI in support of those processes. We make the list of surveyed papers and related material available online at ai4vis.github.io.