Leveraging large language models for enhanced process model comprehension
本文提出一个框架，利用大型语言模型（LLMs）将业务流程模型转化为可查询格式，通过自动评估、定性分析和用户研究证明其能有效提升复杂流程模型的理解，并识别死锁等质量问题。
In Business Process Management (BPM), effectively comprehending process models is crucial yet poses significant challenges, particularly as organizations scale and processes become more complex. This paper introduces a novel framework utilizing the advanced capabilities of Large Language Models (LLMs) to enhance the comprehension of complex process models. We present different methods for abstracting business process models into a format accessible to LLMs, and we implement advanced prompting strategies specifically designed to optimize LLM performance within our framework. Additionally, we present a tool, AIPA, that implements our proposed framework and allows for conversational process querying. We evaluate our framework and tool through: i) an automatic evaluation comparing different LLMs, model abstractions, and prompting strategies; ii) a qualitative analysis assessing the ability to identify critical quality issues in process models; and iii) a user study designed to assess AIPA’s effectiveness comprehensively. Results demonstrate our framework’s ability to improve the comprehension and understanding of process models, pioneering new pathways for integrating AI technologies into the BPM field. • Cutting-edge LLMs can effectively interpret abstractions of business process models. • Prompting techniques enhance the LLMs’ comprehension of process models. • Recent open-source LLMs demonstrate comparable effectiveness to commercial solutions. • Advanced LLMs can identify critical quality issues in process models, such as deadlocks and soundness violations. • A case study shows promising results with suggestions for more concise responses.