| Title |
Adaptation of Enterprise Modeling Methods for Large Language Models |
| ID_Doc |
16130 |
| Authors |
Barn, BS; Barat, S; Sandkuhl, K |
| Title |
Adaptation of Enterprise Modeling Methods for Large Language Models |
| Year |
2024 |
| Published |
|
| DOI |
10.1007/978-3-031-48583-1_1 |
| Abstract |
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. |
| Author Keywords |
Enterprise Modeling; Large Language Model; Modeling Method; ChatGPT; Prompt meta-model |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH) |
| EID |
WOS:001285963600001 |
| WoS Category |
Business; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Management; Operations Research & Management Science |
| Research Area |
Business & Economics; Computer Science; Operations Research & Management Science |
| PDF |
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