Inhaltszusammenfassung

Computer assisted process model development from textual descriptions is still an open research question. Advantages of such a technology lie in shorter development times and possibly a more concise interpretation of the narrative input. A solution to this problem necessarily relies on methods from formal modeling and linguistics. In the latter field, the new GPT-3 model is recognized as a breakthrough that outperforms previous technologies whose limitations hindered success of earlier resear...Computer assisted process model development from textual descriptions is still an open research question. Advantages of such a technology lie in shorter development times and possibly a more concise interpretation of the narrative input. A solution to this problem necessarily relies on methods from formal modeling and linguistics. In the latter field, the new GPT-3 model is recognized as a breakthrough that outperforms previous technologies whose limitations hindered success of earlier research in this context. But are GPT-3's capabilities to summarize text, detect cause-and-effect, or to classify terms sufficient to succeed? The presented research describes the results of systematic experiments to use GPT-3 to interpret a textual process description and transform it into a formal representation. The different settings demonstrate how to exploit the capabilities of large language models and how to avoid pitfalls. Although the observations made are promising, further work is needed. The outcome of this paper identifies the direction in which this future research should proceed.» weiterlesen» einklappen

Autoren

Klassifikation

DFG Fachgebiet:
Informatik

DDC Sachgruppe:
Informatik