Leveraging Large Language Models For Enhanced Process Model Comprehension

Kourani Humam, Berti Alessandro, Henrich Jasmin, Kratsch Wolfgang, Weidlich Robin, Li Chiao-yun, Arslan Ahmad, Schuster Daniel, Van Der Aalst Wil M. P.. Arxiv 2024

[Paper]    
Interpretability And Explainability Prompting RAG Reinforcement Learning Tools Uncategorized

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 interpretability 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 by i) an automatic evaluation comparing different LLMs, model abstractions, and prompting strategies and ii) a user study designed to assess AIPA’s effectiveness comprehensively. Results demonstrate our framework’s ability to improve the accessibility and interpretability of process models, pioneering new pathways for integrating AI technologies into the BPM field.

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