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Adaptation of enterprise modeling methods for large language models
Middlesex University, London, United Kingdom.
Tata Consultancy Services Research, Pune, India.
Jönköping University, School of Engineering, JTH, Department of Computer Science and Informatics. The University of Rostock, Rostock, Germany.ORCID iD: 0000-0002-7431-8412
2024 (English)In: The practice of enterprise modeling: 16th IFIP Working Conference, PoEM 2023, Vienna, Austria, November 28 – December 1, 2023, Proceedings, Cham: Springer, 2024, p. 3-18Conference paper, Published paper (Refereed)
Abstract [en]

Large language models (LLM) are considered by many researchers as promising technology for automating routine tasks. Results from applying LLM in engineering disciplines such as Enterprise Modeling also indicate potential for the support of modeling activities. LLMs are fine-tuned for specific tasks using chat based interaction through the use of prompts. This paper aims at a detailed investigation of the potential of LLMs in Enterprise Modeling (EM) by taking the perspective of EM method adaptation of selected parts of the modeling process within the context of using prompts to interrogate the LLM. The research question addressed is: What adaptations in EM methods have to be made to exploit the potential of prompt based interaction with LLMs? The main contributions are (1) a meta-model for prompt engineering that integrates the concepts of the modeling domain under consideration with the notation of the modeling language applied and the input and output of prompts, (2) an investigation into the general potential of LLM in EM methods and its application in the 4EM method, and (3) implications for enterprise modeling methods.

Place, publisher, year, edition, pages
Cham: Springer, 2024. p. 3-18
Series
Lecture Notes in Business Information Processing, ISSN 1865-1348, E-ISSN 1865-1356 ; 497
Keywords [en]
ChatGPT, Enterprise Modeling, Large Language Model, Modeling Method, Prompt meta-model, Computational linguistics, Engineering disciplines, Enterprise models, Language model, Meta model, Metamodeling, Model method, Specific tasks, Modeling languages
National Category
Information Systems
Identifiers
URN: urn:nbn:se:hj:diva-63353DOI: 10.1007/978-3-031-48583-1_1Scopus ID: 2-s2.0-85178574971ISBN: 9783031485824 (print)ISBN: 9783031485831 (electronic)OAI: oai:DiVA.org:hj-63353DiVA, id: diva2:1828091
Conference
16th IFIP Working Conference, PoEM 2023, Vienna, Austria, November 28 – December 1, 2023
Available from: 2024-01-16 Created: 2024-01-16 Last updated: 2024-01-16Bibliographically approved

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Sandkuhl, Kurt

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