  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.
