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Automatic identification of role-specific information in product development: a critical review on large language models

Published online by Cambridge University Press:  16 May 2024

Dominik Ehring ,
Ismail Menekse ,
Janosch Luttmer  and
Arun Nagarajah
Dominik Ehring*
Affiliation:
University of Duisburg-Essen, Germany
Ismail Menekse
Affiliation:
University of Duisburg-Essen, Germany
Janosch Luttmer
Affiliation:
University of Duisburg-Essen, Germany
Arun Nagarajah
Affiliation:
University of Duisburg-Essen, Germany
*
dominik.ehring@uni-due.de

Abstract

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In the era of digitization and the growing flood of information, the automatic, role-specific identification of information is crucial. This research paper aims to investigate whether the adaptation of LLM is suitable for classifying information obtained from standards for corresponding role profiles. This research reveals that with systematic fine-tuning, prediction accuracy can be increased by almost 100%. The validation was carried out using a two-digit number of standards for three predefined roles and demonstrates the significant potential of LM for labelling content with regard to roles.

Keywords

artificial intelligence (AI)large language model (LLM)fine-tuningrole-specific informationknowledge management
Type
Artificial Intelligence and Data-Driven Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 2009 - 2018
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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