# Ask and You Shall Receive (a Graph Drawing): Testing ChatGPT's Potential to Apply Graph Layout Algorithms

arXiv: https://arxiv.org/abs/2303.08819 · HTML: https://mlsanity.com/paper/2303.08819/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 3 Mar 2023.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [An Explanation of In-context Learning as Implicit Bayesian Inference](https://mlsanity.com/paper/2111.02080/) (2021)
- [A Deep Generative Model for Graph Layout](https://mlsanity.com/paper/1904.12225/) (2019)
- [Aesthetic Discrimination of Graph Layouts](https://mlsanity.com/paper/1809.01017/) (2018)
- [Symmetry Detection and Classification in Drawings of Graphs](https://mlsanity.com/paper/1907.01004/) (2019)

## Built on by (most cited first, 6 of 6)

- [A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets](https://mlsanity.com/paper/2305.18486/) (2023)
- [Testing the Depth of ChatGPT's Comprehension via Cross-Modal Tasks Based on ASCII-Art: GPT3.5's Abilities in Regard to Recognizing and Generating ASCII-Art Are Not Totally Lacking](https://mlsanity.com/paper/2307.16806/) (2023)
- [Charts-of-Thought: Enhancing LLM Visualization Literacy Through Structured Data Extraction](https://mlsanity.com/paper/2508.04842/) (2025)
- [Large Language Models Understand Layout](https://mlsanity.com/paper/2407.05750/) (2024)
- [Microstructures and Accuracy of Graph Recall by Large Language Models](https://mlsanity.com/paper/2402.11821/) (2024)
- [Beauty in the Eye of AI: Aligning LLMs and Vision Models with Human Aesthetics in Network Visualization](https://mlsanity.com/paper/2604.03417/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have recently taken the world by storm. They can generate coherent text, hold meaningful conversations, and be taught concepts and basic sets of instructions - such as the steps of an algorithm. In this context, we are interested in exploring the application of LLMs to graph drawing algorithms by performing experiments on ChatGPT. These algorithms are used to improve the readability of graph visualizations. The probabilistic nature of LLMs presents challenges to implementing algorithms correctly, but we believe that LLMs' ability to learn from vast amounts of data and apply complex operations may lead to interesting graph drawing results. For example, we could enable users with limited coding backgrounds to use simple natural language to create effective graph visualizations. Natural language specification would make data visualization more accessible and user-friendly for a wider range of users. Exploring LLMs' capabilities for graph drawing can also help us better understand how to formulate complex algorithms for LLMs; a type of knowledge that could transfer to other areas of computer science. Overall, our goal is to shed light on the exciting possibilities of using LLMs for graph drawing while providing a balanced assessment of the challenges and opportunities they present. A free copy of this paper with all supplemental materials required to reproduce our results is available on https://osf.io/n5rxd/?view_only=f09cbc2621f44074810b7d843f1e12f9