ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE2024). Volume 6 : 36th International Conference on Design Theory and Methodology (DTM) / Matthew Keeler [and three others].

Format:

Book

Author/Creator:

Keeler, Matthew, author.

Language:

English

Subjects (All):

Engineering design--Congresses.

Engineering design.

Physical Description:

1 online resource (12 pages)

Other Title:

ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference

Place of Publication:

Washington, DC, USA : American Society of Mechanical Engineers, 2024.

Summary:

Well-studied techniques that enhance diversity in early design concept generation require effective metrics for evaluating human-perceived similarity between ideas. Recent work suggests collecting triplet comparisons between designs directly from human raters and using those triplets to form an embedding where similarity is expressed as a Euclidean distance. While effective at modeling human-perceived similarity judgments, these methods are expensive and require a large number of triplets to be hand-labeled. However, what if there were a way to use AI to replicate the human similarity judgments captured in triplet embedding methods? In this paper, we explore the potential for pretrained Large Language Models (LLMs) to be used in this context. Using a dataset of crowdsourced text descriptions written about engineering design sketches, we generate LLM embeddings and compare them to an embedding created from human-provided triplets of those same sketches. From these embeddings, we can use Euclidean distances to describe areas where human perception and LLM perception disagree regarding design similarity. We then implement this same procedure but with descriptions written from a template that attempts to isolate a particular modality of a design (i.e., functions, behaviors, structures). By comparing the templated description embeddings to both the triplet-generated and pre-template LLM embeddings, we identify ways of describing designs such that LLM and human similarity perception better agree. We use these results to better understand how humans and LLMs interpret similarity in engineering designs.

Notes:

Description based on publisher supplied metadata and other sources.

Includes bibliographical references and index.