SubmissionNumber#=%=#226 FinalPaperTitle#=%=#CLULab-UofA at SemEval-2024 Task 8: Detecting Machine-Generated Text Using Triplet-Loss-Trained Text Similarity and Text Classification ShortPaperTitle#=%=# NumberOfPages#=%=#7 CopyrightSigned#=%=#MohammadHossein Rezaei JobTitle#==# Organization#==#The University of Arizona | Tucson, Arizona 85721 Abstract#==#Detecting machine-generated text is a critical task in the era of large language models. In this paper, we present our systems for SemEval-2024 Task 8, which focuses on multi-class classification to discern between human-written and maching-generated texts by five state-of-the-art large language models. We propose three different systems: unsupervised text similarity, triplet-loss-trained text similarity, and text classification. We show that the triplet-loss trained text similarity system outperforms the other systems, achieving 80\% accuracy on the test set and surpassing the baseline model for this subtask. Additionally, our text classification system, which takes into account sentence paraphrases generated by the candidate models, also outperforms the unsupervised text similarity system, achieving 74\% accuracy. Author{1}{Firstname}#=%=#MohammadHossein Author{1}{Lastname}#=%=#Rezaei Author{1}{Username}#=%=#mhrezaei Author{1}{Email}#=%=#mhrezaei@arizona.edu Author{1}{Affiliation}#=%=#The University of Arizona Author{2}{Firstname}#=%=#Yeaeun Author{2}{Lastname}#=%=#Kwon Author{2}{Username}#=%=#yekwon Author{2}{Email}#=%=#yeaeunkwon@arizona.edu Author{2}{Affiliation}#=%=#The University of Arizona Author{3}{Firstname}#=%=#Reza Author{3}{Lastname}#=%=#Sanayei Author{3}{Username}#=%=#rsanayei Author{3}{Email}#=%=#rsanayei@arizona.edu Author{3}{Affiliation}#=%=#University of Arizona Author{4}{Firstname}#=%=#Abhyuday Author{4}{Lastname}#=%=#Singh Author{4}{Username}#=%=#abhyudaysingh Author{4}{Email}#=%=#abhyudaysingh@arizona.edu Author{4}{Affiliation}#=%=#University of Arizona Author{5}{Firstname}#=%=#Steven Author{5}{Lastname}#=%=#Bethard Author{5}{Username}#=%=#steven.bethard Author{5}{Email}#=%=#bethard@arizona.edu Author{5}{Affiliation}#=%=#University of Arizona ========== èéáğö