mlsanityData to 7 Oct 2026

Towards using Few-Shot Prompt Learning for Automating Model Completion

arXiv 2212.03404 ↗First posted 7 Dec 2022cs.SE cs.CLcited by 13

Not on the map. Why?

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.

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

Builds on

3 of 16 references.

References · 3

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  3. 2021Recommending Metamodel Concepts during Modeling Activities with Pre-Trained Language Modelscited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 3; 2026: 5.

13 papers.

Sort:

  1. 2023Prompting or Fine-tuning? A Comparative Study of Large Language Models for Taxonomy Constructioncited by 18
  2. 2024PathOCL: Path-Based Prompt Augmentation for OCL Generation with GPT-4cited by 4
  3. 2024On the Utility of Domain Modeling Assistance with Large Language Modelscited by 3
  4. 2023GPT-4 and Safety Case Generation: An Exploratory Analysiscited by 2
  5. 2025An Analysis of LLM Fine-Tuning and Few-Shot Learning for Flaky Test Detection and Classificationcited by 2
  6. 2024Software Model Evolution with Large Language Models: Experiments on Simulated, Public, and Industrial Datasetscited by 1
  7. 2025SHERPA: A Model-Driven Framework for Large Language Model Executioncited by 1
  8. 2026Leveraging LLMs to support co-evolution between definitions and instances of textual DSLs: A Systematic Evaluationcited by 1
  9. 2025Unify and Triumph: Polyglot, Diverse, and Self-Consistent Generation of Unit Tests with LLMscited by 0
  10. 2026Generative AI-assisted Participatory Modeling in Socio-Environmental Planning under Deep Uncertaintycited by 0
  11. 2026Vibe-driven model-based engineeringcited by 0
  12. 2026Leveraging LLMs for Grammar Adaptation: A Study on Metamodel-Grammar Co-Evolutioncited by 0
1 more
  1. 2026Relationally Guided Use Case Modeling with LLMscited by 0