arXiv:2601.21787v2 Announce Type: replace-cross Abstract: The creation of Business Process Model and Notation (BPMN) models is a complex and time-consuming task requiring both domain knowledge and proficiency in modeling conventions. Recent advances in large language models (LLMs) have significantly expanded the possibilities for generating BPMN models directly from natural language, building upon earlier text-to-process methods with enhanced capabilities in handling complex descriptions. However, there is a lack of systematic evaluations of LLM-generated process models. Current efforts either
The rapid advancement of large language models makes their application to complex tasks like business process modeling a natural next step, following earlier text-to-process methods.
Evaluating LLMs' competence in generating business process models is crucial for understanding their potential to automate and streamline enterprise operations, which can significantly reduce human effort and errors.
This research shifts the focus from simply generating process models to systematically assessing the quality and utility of LLM-generated models, providing a foundation for their practical deployment.
- · AI developers
- · Business process automation software vendors
- · Enterprises adopting AI tools
- · Consulting services specializing in AI integration
- · Traditional BPM consultants
- · Manual business process designers
Increased efficiency and accuracy in business process documentation and design.
Reduced operational costs and faster time-to-market for new business initiatives.
Re-skilling requirements for a workforce increasingly augmented by AI, leading to new job roles in AI oversight and validation.
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Read at arXiv cs.AI