SubmissionNumber#=%=#239
FinalPaperTitle#=%=#TueCICL at SemEval-2024 Task 8: Resource-efficient approaches for machine-generated text detection
ShortPaperTitle#=%=#
NumberOfPages#=%=#5
CopyrightSigned#=%=#Daniel Stuhlinger
JobTitle#==#
Organization#==#University of Tübingen
Abstract#==#Recent developments in the field of NLP have brought large language models (LLMs) to the forefront of both public and research attention. As the use of language generation technologies becomes more widespread, the problem arises of determining whether a given text is machine generated or not. Task 8 at SemEval 2024 consists of a shared task with this exact objective. Our approach aims at developing models and strategies that strike a good balance between performance and model size. We show that it is possible to compete with large transformer-based solutions with smaller systems.
Author{1}{Firstname}#=%=#Daniel
Author{1}{Lastname}#=%=#Stuhlinger
Author{1}{Username}#=%=#danielstuhlinger
Author{1}{Email}#=%=#daniel.stuhlinger@student.uni-tuebingen.de
Author{1}{Affiliation}#=%=#Universität Tübingen
Author{2}{Firstname}#=%=#Aron
Author{2}{Lastname}#=%=#Winkler
Author{2}{Email}#=%=#aron.winkler@student.uni-tuebingen.de
Author{2}{Affiliation}#=%=#Universität Tübingen

==========
èéáğö