  The disruptive technology provided by large-scale pre-trained language models
(LLMs) such as ChatGPT or GPT-4 has received significant attention in several
application domains, often with an emphasis on high-level opportunities and
concerns. This paper is the first examination regarding the use of LLMs for
scientific simulations. We focus on four modeling and simulation tasks, each
time assessing the expected benefits and limitations of LLMs while providing
practical guidance for modelers regarding the steps involved. The first task is
devoted to explaining the structure of a conceptual model to promote the
engagement of participants in the modeling process. The second task focuses on
summarizing simulation outputs, so that model users can identify a preferred
scenario. The third task seeks to broaden accessibility to simulation platforms
by conveying the insights of simulation visualizations via text. Finally, the
last task evokes the possibility of explaining simulation errors and providing
guidance to resolve them.
