Leveraging Large Language Models for Enhanced Process Model Comprehension

Title data

Kourani, Humam ; Berti, Alessandro ; Hennrich, Jasmin ; Kratsch, Wolfgang ; Weidlich, Robin ; Li, Chiao-Yun ; Arslan, Ahmad ; van der Aalst, Wil M. P. ; Schuster, Daniel:
Leveraging Large Language Models for Enhanced Process Model Comprehension.
In: Decision Support Systems. Vol. 200 (2026) . - 114563.
ISSN 1873-5797
DOI: https://doi.org/10.1016/j.dss.2025.114563

Official URL:

Abstract in another language

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.

Further data

Item Type:	Article in a journal
Refereed:	Yes
Keywords:	Process model comprehension; Business process management; Large language models; Generative AI
Institutions of the University:	Faculties > Faculty of Law, Business and Economics > Department of Business Administration Research Institutions Research Institutions > Affiliated Institutes Research Institutions > Affiliated Institutes > Branch Business and Information Systems Engineering of Fraunhofer FIT Research Institutions > Affiliated Institutes > FIM Research Center for Information Management
Result of work at the UBT:	Yes
DDC Subjects:	000 Computer Science, information, general works > 004 Computer science 300 Social sciences > 330 Economics
Date Deposited:	20 Mar 2026 07:34
Last Modified:	20 Mar 2026 07:34
URI:	https://eref.uni-bayreuth.de/id/eprint/96644