Leveraging large language models in games with a purpose for enhanced knowledge elicitation

Bachelor thesis (2024)

Authors

T. Hu Electrical Engineering, Mathematics and Computer Science

Contributors

S. Biswas Web Information Systems - EEMCS (mentor)
Ujwal Gadiraju Web Information Systems - EEMCS (mentor)
R. Marroquim Computer Graphics and Visualisation - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
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Published Date

28-06-2024

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

The process of knowledge elicitation is crucial to the field of artificial intelligence because of the lack of data on commonsense knowledge. This paper explores the potential of using large language models (LLM) to enhance knowledge elicitation in games with a purpose (GWAP). By analyzing the capability of LLMs to play games and generate game content, this research shows how LLMs can increase accessibility, efficiency and engagement in GWAPs for knowledge elicitation. The findings show that LLMs can play games with great performance and show much promise in generating game content that facilitates knowledge elicitation. Through a comprehensive literature survey, this research highlights potential ways of enhancing knowledge elicitation in GWAPs using LLMs and offers recommendations for future research in this field.

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