# Are LLMs ready for Visualization?

arXiv: https://arxiv.org/abs/2403.06158 · HTML: https://mlsanity.com/paper/2403.06158/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2024.

## Built on by (most cited first, 16 of 16)

- [VisEval: A Benchmark for Data Visualization in the Era of Large Language Models](https://mlsanity.com/paper/2407.00981/) (2024)
- [Benchmarking Visual Language Models on Standardized Visualization Literacy Tests](https://mlsanity.com/paper/2503.16632/) (2025)
- [The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective](https://mlsanity.com/paper/2407.08583/) (2024)
- [InterChat: Enhancing Generative Visual Analytics using Multimodal Interactions](https://mlsanity.com/paper/2503.04110/) (2025)
- [AstroVisBench: A Code Benchmark for Scientific Computing and Visualization in Astronomy](https://mlsanity.com/paper/2505.20538/) (2025)
- [Can GPT-4 Models Detect Misleading Visualizations?](https://mlsanity.com/paper/2408.12617/) (2024)
- [SciVisAgentBench: A Benchmark for Evaluating Scientific Data Analysis and Visualization Agents](https://mlsanity.com/paper/2603.29139/) (2026)
- [Visualization Literacy of Multimodal Large Language Models: A Comparative Study](https://mlsanity.com/paper/2407.10996/) (2024)
- [DeepVIS: Bridging Natural Language and Data Visualization Through Step-wise Reasoning](https://mlsanity.com/paper/2508.01700/) (2025)
- [See or Recall: A Sanity Check for the Role of Vision in Solving Visualization Question Answer Tasks with Multimodal LLMs](https://mlsanity.com/paper/2504.09809/) (2025)
- [MapColorAI: Designing Contextually Relevant Choropleth Map Color Schemes Using a Large Language Model](https://mlsanity.com/paper/2503.15502/) (2025)
- [Evaluating LLMs for Visualization Generation and Understanding](https://mlsanity.com/paper/2507.22890/) (2025)
- [LangLasso: Interactive Cluster Descriptions through LLM Explanation](https://mlsanity.com/paper/2601.10458/) (2026)
- [Evaluating LLMs for Visualization Tasks](https://mlsanity.com/paper/2506.10996/) (2025)
- [Agentic Authoring of Interactive Multiview Visualizations in Genomics](https://mlsanity.com/paper/2606.00370/) (2026)
- [MV-Bench: Benchmarking Multimodal Large Language Models for Coordinated Multi-View Interface Construction](https://mlsanity.com/paper/2607.19910/) (2026)

## Abstract (arXiv)

Generative models have received a lot of attention in many areas of academia and the industry. Their capabilities span many areas, from the invention of images given a prompt to the generation of concrete code to solve a certain programming issue. These two paradigmatic cases fall within two distinct categories of requirements, ranging from "creativity" to "precision", as characterized by Bing Chat, which employs ChatGPT-4 as its backbone. Visualization practitioners and researchers have wondered to what end one of such systems could accomplish our work in a more efficient way. Several works in the literature have utilized them for the creation of visualizations. And some tools such as Lida, incorporate them as part of their pipeline. Nevertheless, to the authors' knowledge, no systematic approach for testing their capabilities has been published, which includes both extensive and in-depth evaluation. Our goal is to fill that gap with a systematic approach that analyzes three elements: whether Large Language Models are capable of correctly generating a large variety of charts, what libraries they can deal with effectively, and how far we can go to configure individual charts. To achieve this objective, we initially selected a diverse set of charts, which are commonly utilized in data visualization. We then developed a set of generic prompts that could be used to generate them, and analyzed the performance of different LLMs and libraries. The results include both the set of prompts and the data sources, as well as an analysis of the performance with different configurations.