Towards using Few-Shot Prompt Learning for Automating Model Completion

TL;DR

This paper introduces a novel few-shot prompt learning method leveraging large language models to automate model completion tasks in domain modeling, avoiding extensive training.

Contribution

It presents a simple, effective approach that uses few-shot prompts with large language models for domain diagram completion without fine-tuning.

Findings

Effective in static and dynamic domain diagram completion

Can be integrated into various modeling activities

Avoids need for large datasets and fine-tuning

Abstract

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.