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Generative large language models in engineering design: opportunities and challenges

Published online by Cambridge University Press:  16 May 2024

Filippo Chiarello ,
Simone Barandoni ,
Marija Majda Škec  and
Gualtiero Fantoni
Filippo Chiarello*
Affiliation:
University of Pisa, Italy
Simone Barandoni
Affiliation:
University of Pisa, Italy
Marija Majda Škec
Affiliation:
University of Zagreb Faculty of Mechanical Engineering and Naval Architecture, Croatia
Gualtiero Fantoni
Affiliation:
University of Pisa, Italy
*
filippo.chiarello@unipi.it

Abstract

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Despite the rapid advancement of generative Large Language Models (LLMs), there is still limited understanding of their potential impacts on engineering design (ED). This study fills this gap by collecting the tasks LLMs can perform within ED, using a Natural Language Processing analysis of 15,355 ED research papers. The results lead to a framework of LLM tasks in design, classifying them for different functions of LLMs and ED phases. Our findings illuminate the opportunities and risks of using LLMs for design, offering a foundation for future research and application in this domain.

Keywords

artificial intelligence (AI)design processgenerative AIlarge language model (LLM)natural language processing (NLP)

Information

Type
Artificial Intelligence and Data-Driven Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 1959 - 1968
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), 2024.

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