In the realm of Business Process Management (BPM), process modeling plays a crucial role in translating complex process dynamics into comprehensible visual representations, facilitating the understanding, analysis, improvement, and automation of organizational processes. Traditional process modeling methods often require extensive expertise and can be time-consuming. This paper explores the integration of Large Language Models (LLMs) into process modeling to enhance the accessibility of process modeling, offering a more intuitive entry point for non-experts while augmenting the efficiency of experts. We propose a framework that leverages LLMs for the automated generation and iterative refinement of process models starting from textual descriptions. Our framework involves innovative prompting strategies for effective LLM utilization, along with a secure model generation protocol and an error-handling mechanism. Moreover, we instantiate a concrete system extending our framework. This system provides robust quality guarantees on the models generated and supports exporting them in standard modeling notations, such as the Business Process Modeling Notation (BPMN) and Petri nets. Preliminary results demonstrate the framework's ability to streamline process modeling tasks, underscoring the transformative potential of generative AI in the BPM field.
Critical Systems Development Using Modeling Languages. (CSDUML-04): Current Developments and Future Challenges (Report on the Third International Workshop)
Apricot - An Object-Oriented Modeling Language for Hybrid Systems
Shanghai Key Laboratory of Trustworthy Computing Software Engineering Institute, East China Normal University（上海可信计算重点实验室软件工程学院，华东师范大学）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）
