Using Transformers to Generate Wellbeing Questions

Bachelor thesis (2022)

Authors

M. Trasberg Electrical Engineering, Mathematics and Computer Science

Contributors

W.L.A. van der Maden Design Aesthetics - IDE (supervisor 1)
Ujwal Gadiraju Web Information Systems - EEMCS (supervisor 1)
G.M. Allen Web Information Systems - EEMCS (supervisor 1)
J.D. Lomas Design Aesthetics - IDE (supervisor 1)
Z. Erkin Cyber Security - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Markus Trasberg
GPT-3 Wellbeing Transformer
More Info
expand_more

Published Date

22-06-2022

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

With the surge of mental health issues during COVID-19, more emphasis has turned towards assessing wellbeing. At the same time, recent advances in AI have shown huge potential in a variety of fields. However, few solutions are available at the intersection of those two fields. This research explores how the use of transformer models like GPT-3 could have a positive impact in the domain of wellbeing and proposes a solution to automatize the survey question creation process. After comparing several GPT-3 question creation methods, it was found that through clever prompt engineering and added context, it can be possible to generate syntactically and contextually correct questions about any specific wellbeing context. In addition, the paper discusses potential ways to assess such questions and offers a demo for a question generation web application.