  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
