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 flexibility, efficiency, and accessibility of process modeling for both expert and non-expert users. 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.

翻译：在业务流程管理（BPM）领域，过程建模在将复杂过程动态转化为可理解的可视化表示方面发挥着关键作用，有助于理解、分析、改进和自动化组织流程。传统过程建模方法通常需要广泛的专业知识且耗时较长。本文探讨将大语言模型（LLMs）融入过程建模，以提升过程建模对专家和非专家用户的灵活性、效率和可及性。我们提出一个框架，利用LLMs从文本描述出发自动生成并迭代优化过程模型。该框架包含有效利用LLMs的创新提示策略，以及安全的模型生成协议和错误处理机制。此外，我们实例化了一个基于该框架的具体系统，为生成的模型提供稳健的质量保障，并支持以标准建模符号（如业务流程建模符号BPMN和Petri网）导出模型。初步结果表明，该框架能够简化过程建模任务，凸显了生成式AI在BPM领域的变革潜力。