  Process models are frequently used in software engineering to describe
business requirements, guide software testing and control system improvement.
However, traditional process modeling methods often require the participation
of numerous experts, which is expensive and time-consuming. Therefore, the
exploration of a more efficient and cost-effective automated modeling method
has emerged as a focal point in current research. This article explores a
framework for automatically generating process models with multi-agent
orchestration (MAO), aiming to enhance the efficiency of process modeling and
offer valuable insights for domain experts. Our framework MAO leverages large
language models as the cornerstone for multi-agent, employing an innovative
prompt strategy to ensure efficient collaboration among multi-agent.
Specifically, 1) generation. The first phase of MAO is to generate a slightly
rough process model from the text description; 2) refinement. The agents would
continuously refine the initial process model through multiple rounds of
dialogue; 3) reviewing. Large language models are prone to hallucination
phenomena among multi-turn dialogues, so the agents need to review and repair
semantic hallucinations in process models; 4) testing. The representation of
process models is diverse. Consequently, the agents utilize external tools to
test whether the generated process model contains format errors, namely format
hallucinations, and then adjust the process model to conform to the output
paradigm. The experiments demonstrate that the process models generated by our
framework outperform existing methods and surpass manual modeling by 89%, 61%,
52%, and 75% on four different datasets, respectively.
