# On the Utility of Domain Modeling Assistance with Large Language Models

arXiv: https://arxiv.org/abs/2410.12577 · HTML: https://mlsanity.com/paper/2410.12577/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 57 references that are in the map.
- First posted on arXiv 16 Oct 2024.

## Builds on (most cited first, 13 of 13)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Instruction Tuning for Large Language Models: A Survey](https://mlsanity.com/paper/2308.10792/) (2023)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [Design Guidelines for Prompt Engineering Text-to-Image Generative Models](https://mlsanity.com/paper/2109.06977/) (2021)
- [Towards Human-Bot Collaborative Software Architecting with ChatGPT](https://mlsanity.com/paper/2302.14600/) (2023)
- [Investigating ChatGPT's Potential to Assist in Requirements Elicitation Processes](https://mlsanity.com/paper/2307.07381/) (2023)
- [Towards using Few-Shot Prompt Learning for Automating Model Completion](https://mlsanity.com/paper/2212.03404/) (2022)
- [Don't Disturb Me: Challenges of Interacting with SoftwareBots on Open Source Software Projects](https://mlsanity.com/paper/2103.13950/) (2021)
- [How LLMs Aid in UML Modeling: An Exploratory Study with Novice Analysts](https://mlsanity.com/paper/2404.17739/) (2024)
- [Recommending Metamodel Concepts during Modeling Activities with Pre-Trained Language Models](https://mlsanity.com/paper/2104.01642/) (2021)
- [MAR: A structure-based search engine for models](https://mlsanity.com/paper/2008.11858/) (2020)

## Built on by (most cited first, 3 of 3)

- [LLM-based Iterative Approach to Metamodeling in Automotive](https://mlsanity.com/paper/2503.05449/) (2025)
- [Automating Domain-Driven Design: Experience with a Prompting Framework](https://mlsanity.com/paper/2603.26244/) (2026)
- [Trust the AI, Doubt Yourself: The Effect of Urgency on Self-Confidence in Human-AI Interaction](https://mlsanity.com/paper/2604.07535/) (2026)

## Abstract (arXiv)

Model-driven engineering (MDE) simplifies software development through abstraction, yet challenges such as time constraints, incomplete domain understanding, and adherence to syntactic constraints hinder the design process. This paper presents a study to evaluate the usefulness of a novel approach utilizing large language models (LLMs) and few-shot prompt learning to assist in domain modeling. The aim of this approach is to overcome the need for extensive training of AI-based completion models on scarce domain-specific datasets and to offer versatile support for various modeling activities, providing valuable recommendations to software modelers. To support this approach, we developed MAGDA, a user-friendly tool, through which we conduct a user study and assess the real-world applicability of our approach in the context of domain modeling, offering valuable insights into its usability and effectiveness.