Bartolomeo, Sara DiSeveri, GiorgioSchetinger, VictorDunne, CodyHoellt, ThomasAigner, WolfgangWang, Bei2023-06-102023-06-102023978-3-03868-219-6https://doi.org/10.2312/evs.20231047https://diglib.eg.org:443/handle/10.2312/evs20231047Large 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, one of the most recent cutting-edge LLMs made available to the public. These algorithms are used to create readable 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-using the Sugiyama algorithm as a sample case-while providing a balanced assessment of the challenges and opportunities they present. A free copy of this paper with all supplemental materials to reproduce our results is available on osf.io .Attribution 4.0 International LicenseCCS Concepts: Human-centered computing -> Graph drawings; Computing methodologies -> Artificial intelligenceHuman centered computingGraph drawingsComputing methodologiesArtificial intelligence10.2312/evs.2023104779-835 pages