Looking Through the Deep Glasses : How Large Language Models Enhance Explainability of Deep Learning Models

Title data

Spitzer, Philipp ; Celis, Sebastian ; Martin, Dominik ; Kühl, Niklas ; Satzger, Gerhard:
Looking Through the Deep Glasses : How Large Language Models Enhance Explainability of Deep Learning Models.
In: Maedche, Alexander ; Beigl, Michael ; Gerling, Kathrin ; Mayer, Sven (ed.): Proceedings of Mensch und Computer 2024. - Karlsruhe, Germany : Association for Computing Machinery , 2024 . - pp. 566-570
ISBN 979-8-4007-0998-2
DOI: https://doi.org/10.1145/3670653.3677488

Official URL:

Abstract in another language

As AI becomes more powerful, it also becomes more complex. Tra ditionally, eXplainable AI (XAI) is used to make these models more transparent and interpretable to decision-makers. However, re search shows that decision-makers can lack the ability to properly interpret XAI techniques. Large language models (LLMs) offer a solution to this challenge by providing natural language text in combination with XAI techniques to provide more understandable explanations. However, previous work has only explored this ap proach for inherently interpretable models–an understanding of how LLMs can assist decision-makers when using deep learning models is lacking. To fill this gap, we investigate how different aug mentation strategies of LLMs assist decision-makers in interacting with deep learning models. We evaluate the satisfaction and prefer ences of decision-makers through a user study. Overall, our results provide first insights into how LLMs support decision-makers in interacting with deep learning models and open future avenues to continue this endeavor.

Further data

Item Type:	Article in a book
Refereed:	Yes
Keywords:	Large Language Models; Artificial Intelligence; Explainable AI; Human-Computer Interaction
Institutions of the University:	Faculties > Faculty of Law, Business and Economics > Department of Business Administration Research Institutions Research Institutions > Affiliated Institutes Research Institutions > Affiliated Institutes > Branch Business and Information Systems Engineering of Fraunhofer FIT Research Institutions > Affiliated Institutes > FIM Research Center for Information Management
Result of work at the UBT:	Yes
DDC Subjects:	000 Computer Science, information, general works > 004 Computer science 300 Social sciences > 330 Economics
Date Deposited:	06 Sep 2024 08:02
Last Modified:	06 Sep 2024 08:02
URI:	https://eref.uni-bayreuth.de/id/eprint/90353