Improving BPMN XOR Gateway Labels through Dynamic Prompt Engineering

Business Process Modeling plays a critical role in improving operational efficiency, consistency, and transparency within organizations. Despite the significant focus on syntactic and semantic aspects, pragmatic quality, the clarity and comprehensibility of BP models, remains underexplored. This paper addresses this gap by proposing a Large Language Models approach to enhance the naming of XOR split outgoing edges in BPMN models. The proposed method automates the generation of clear, meaningful, and contextually appropriate edge labels. These labels are evaluated using the all-MiniLM-L6-v2 model, a pre-trained sentence transformer that maps sentences into a dense vector space, enabling semantic search and clustering. Our results show that this LLM-based approach significantly improves the pragmatic quality of BPMN models, enhancing both model clarity and decision-making efficiency.