# Towards using Few-Shot Prompt Learning for Automating Model Completion

arXiv: https://arxiv.org/abs/2212.03404 · HTML: https://mlsanity.com/paper/2212.03404/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 7 Dec 2022.

## Builds on (most cited first, 3 of 3)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Recommending Metamodel Concepts during Modeling Activities with Pre-Trained Language Models](https://mlsanity.com/paper/2104.01642/) (2021)

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

- [Prompting or Fine-tuning? A Comparative Study of Large Language Models for Taxonomy Construction](https://mlsanity.com/paper/2309.01715/) (2023)
- [PathOCL: Path-Based Prompt Augmentation for OCL Generation with GPT-4](https://mlsanity.com/paper/2405.12450/) (2024)
- [On the Utility of Domain Modeling Assistance with Large Language Models](https://mlsanity.com/paper/2410.12577/) (2024)
- [GPT-4 and Safety Case Generation: An Exploratory Analysis](https://mlsanity.com/paper/2312.05696/) (2023)
- [An Analysis of LLM Fine-Tuning and Few-Shot Learning for Flaky Test Detection and Classification](https://mlsanity.com/paper/2502.02715/) (2025)
- [SHERPA: A Model-Driven Framework for Large Language Model Execution](https://mlsanity.com/paper/2509.00272/) (2025)
- [Software Model Evolution with Large Language Models: Experiments on Simulated, Public, and Industrial Datasets](https://mlsanity.com/paper/2406.17651/) (2024)
- [Leveraging LLMs to support co-evolution between definitions and instances of textual DSLs: A Systematic Evaluation](https://mlsanity.com/paper/2602.11904/) (2026)
- [Unify and Triumph: Polyglot, Diverse, and Self-Consistent Generation of Unit Tests with LLMs](https://mlsanity.com/paper/2503.16144/) (2025)
- [Generative AI-assisted Participatory Modeling in Socio-Environmental Planning under Deep Uncertainty](https://mlsanity.com/paper/2603.17021/) (2026)
- [Vibe-driven model-based engineering](https://mlsanity.com/paper/2604.10645/) (2026)
- [Leveraging LLMs for Grammar Adaptation: A Study on Metamodel-Grammar Co-Evolution](https://mlsanity.com/paper/2605.21465/) (2026)
- [Relationally Guided Use Case Modeling with LLMs](https://mlsanity.com/paper/2609.18291/) (2026)

## Abstract (arXiv)

We propose a simple yet a novel approach to improve completion in domain modeling activities. Our approach exploits the power of large language models by using few-shot prompt learning without the need to train or fine-tune those models with large datasets that are scarce in this field. We implemented our approach and tested it on the completion of static and dynamic domain diagrams. Our initial evaluation shows that such an approach is effective and can be integrated in different ways during the modeling activities.