In recent years, large language models (LLMs) have been extensively utilized for behavioral modeling, for example, to automatically generate sequence diagrams. However, no overview of this work has been published yet. Such an overview will help identify future research directions and inform practitioners and educators about the effectiveness of LLMs in assisting behavioral modeling. This study aims to provide an overview of the existing research on the use of LLMs for behavioral modeling, particularly focusing on use case and sequence diagrams. Through a term-based search, we filtered and identified 14 relevant primary studies. Our analysis of the selected primary studies reveals that LLMs have demonstrated promising results in automatically generating use case and sequence diagrams. In addition, we found that most of the current literature lacks expert-based evaluations and has mainly used GPT-based models. Therefore, future work should evaluate a broader range of LLMs for behavioral modeling and involve domain experts to evaluate the output of LLMs.

翻译：近年来，大语言模型（LLMs）已被广泛用于行为建模，例如自动生成序列图。然而，目前尚未有相关工作的综述发表。此类综述将有助于明确未来的研究方向，并为从业者和教育工作者了解LLMs在辅助行为建模方面的有效性提供参考。本研究旨在概述现有关于使用LLMs进行行为建模的研究，尤其关注用例图和序列图。通过基于术语的检索，我们筛选并确定了14项相关的初步研究。对所选初步研究的分析表明，LLMs在自动生成用例图和序列图方面已展现出有前景的结果。此外，我们发现当前大多数文献缺乏基于专家的评估，且主要使用了基于GPT的模型。因此，未来的工作应评估更广泛的LLMs在行为建模中的应用，并让领域专家参与评估LLMs的输出。