LLM-based Iterative Approach to Metamodeling in Automotive
This addresses the problem of automating metamodeling for automotive engineers, but it appears incremental as it applies existing LLM technology to a specific domain.
The paper tackles automated domain-specific metamodel construction in the automotive domain using a Large Language Model (LLM), resulting in a prototype web service that successfully constructs Ecore metamodels from automotive requirements and visualizes them with PlantUML for expert feedback.
In this paper, we introduce an automated approach to domain-specific metamodel construction relying on Large Language Model (LLM). The main focus is adoption in automotive domain. As outcome, a prototype was implemented as web service using Python programming language, while OpenAI's GPT-4o was used as the underlying LLM. Based on the initial experiments, this approach successfully constructs Ecore metamodel based on set of automotive requirements and visualizes it making use of PlantUML notation, so human experts can provide feedback in order to refine the result. Finally, locally deployable solution is also considered, including the limitations and additional steps required.