基于GPT的模型与仿真相遇：如何跨仿真任务高效使用大规模预训练语言模型
GPT-Based Models Meet Simulation: How to Efficiently Use Large-Scale Pre-Trained Language Models Across Simulation Tasks
AI总结：
本文首次探讨大规模预训练语言模型在科学仿真中的应用，聚焦四项建模与仿真任务，评估LLMs的收益与局限并为建模人员提供实用指导。
摘要：
ChatGPT或GPT-4等大规模预训练语言模型（LLMs）提供的颠覆性技术在多个应用领域引起了广泛关注，通常侧重于高层面的机遇与担忧。本文是首次对将LLMs用于科学仿真进行的考察。我们聚焦于四项建模与仿真任务，每次评估LLMs的预期收益与局限性，同时为建模人员提供涉及相关步骤的实用指导。第一项任务致力于解释概念模型的结构，以促进参与者参与建模过程。第二项任务侧重于总结仿真输出，以便模型用户能够识别首选场景。第三项任务旨在通过文本传达仿真可视化的洞见，从而扩大仿真平台的可及性。最后，最后一项任务引出解释仿真错误并提供解决指导的可能性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
