mlsanityData to 14 Oct 2026

LLM-based Iterative Approach to Metamodeling in Automotive

arXiv 2503.05449 ↗First posted 7 Mar 2025cs.SE cs.AIcited by 4

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 5 of its 19 references that are in the map. First posted on arXiv 7 Mar 2025.

Builds on

5 of 19 references.

References · 5

  1. 2024Synergy of Large Language Model and Model Driven Engineering for Automated Development of Centralized Vehicular Systemscited by 7
  2. 2024Adopting RAG for LLM-Aided Future Vehicle Designcited by 6
  3. 2025Multi-modal Summarization in Model-Based Engineering: Automotive Software Development Case Studycited by 6
  4. 2024A Model Is Not Built By A Single Prompt: LLM-Based Domain Modeling With Question Decompositioncited by 3
  5. 2024On the Utility of Domain Modeling Assistance with Large Language Modelscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.