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Generative Pre-Trained Transformer for Design Concept Generation: An Exploration

Published online by Cambridge University Press:  26 May 2022

Q. Zhu  and
J. Luo
Q. Zhu*
Affiliation:
Singapore University of Technology and Design, Singapore
J. Luo
Affiliation:
Singapore University of Technology and Design, Singapore
*
[email protected]

Abstract

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Novel concepts are essential for design innovation and can be generated with the aid of data stimuli and computers. However, current generative design algorithms focus on diagrammatic or spatial concepts that are either too abstract to understand or too detailed for early phase design exploration. This paper explores the uses of generative pre-trained transformers (GPT) for natural language design concept generation. Our experiments involve the use of GPT-2 and GPT-3 for different creative reasonings in design tasks. Both show reasonably good performance for verbal design concept generation.

Keywords

early design phaseidea generationgenerative designnatural language generationgenerative pre-trained transformer
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 1825 - 1834
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2022.

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