# On the use of Large Language Models in Model-Driven Engineering

arXiv: https://arxiv.org/abs/2410.17370 · HTML: https://mlsanity.com/paper/2410.17370/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2024.

## Built on by (most cited first, 13 of 13)

- [Querying Large Automotive Software Models: Agentic vs. Direct LLM Approaches](https://mlsanity.com/paper/2506.13171/) (2025)
- [Accurate and Consistent Graph Model Generation from Text with Large Language Models](https://mlsanity.com/paper/2508.00255/) (2025)
- [Metamodel-Guided Model Generation with Layered Constraints](https://mlsanity.com/paper/2510.25890/) (2025)
- [Leveraging LLMs to support co-evolution between definitions and instances of textual DSLs](https://mlsanity.com/paper/2512.06836/) (2025)
- [SHERPA: A Model-Driven Framework for Large Language Model Execution](https://mlsanity.com/paper/2509.00272/) (2025)
- [Walking the Tightrope of LLMs for Software Development: A Practitioners' Perspective](https://mlsanity.com/paper/2511.06428/) (2025)
- [Leveraging LLMs to support co-evolution between definitions and instances of textual DSLs: A Systematic Evaluation](https://mlsanity.com/paper/2602.11904/) (2026)
- [Model-Driven Quantum Code Generation Using Large Language Models and Retrieval-Augmented Generation](https://mlsanity.com/paper/2508.21097/) (2025)
- [Impact and Implications of Generative AI for Enterprise Architects in Agile Environments: A Systematic Literature Review](https://mlsanity.com/paper/2510.22003/) (2025)
- [Automating Domain-Driven Design: Experience with a Prompting Framework](https://mlsanity.com/paper/2603.26244/) (2026)
- [Vibe-driven model-based engineering](https://mlsanity.com/paper/2604.10645/) (2026)
- [Projectional Decoding: Towards Semantic-Aware LLM Generation](https://mlsanity.com/paper/2605.30054/) (2026)
- [Models as Governed Interfaces for AI-Native MBSE: Read-Side Adequacy and Write-Side Admissibility](https://mlsanity.com/paper/2609.16252/) (2026)

## Abstract (arXiv)

Model-Driven Engineering (MDE) has seen significant advancements with the integration of Machine Learning (ML) and Deep Learning (DL) techniques. Building upon the groundwork of previous investigations, our study provides a concise overview of current Language Large Models (LLMs) applications in MDE, emphasizing their role in automating tasks like model repository classification and developing advanced recommender systems. The paper also outlines the technical considerations for seamlessly integrating LLMs in MDE, offering a practical guide for researchers and practitioners. Looking forward, the paper proposes a focused research agenda for the future interplay of LLMs and MDE, identifying key challenges and opportunities. This concise roadmap envisions the deployment of LLM techniques to enhance the management, exploration, and evolution of modeling ecosystems. By offering a compact exploration of LLMs in MDE, this paper contributes to the ongoing evolution of MDE practices, providing a forward-looking perspective on the transformative role of Language Large Models in software engineering and model-driven practices.