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.

翻译：以ChatGPT或GPT-4为代表的大规模预训练语言模型（LLMs）所提供的颠覆性技术已在多个应用领域受到广泛关注，相关讨论通常侧重于高层次机遇与挑战。本文首次探讨了将LLMs应用于科学仿真的问题。我们聚焦于四项建模与仿真任务，每项任务均评估了LLMs的预期优势与局限，并为建模人员提供了涉及各步骤的实践指导。第一项任务致力于解释概念模型的结构，以促进参与者对建模过程的参与。第二项任务专注于总结仿真输出结果，使得模型用户能够识别出首选方案。第三项任务试图通过文本传达仿真可视化的见解，从而拓展仿真平台的可访问性。最后一项任务探讨了解释仿真错误并提供解决指导的可能性。