SubmissionNumber#=%=#26 FinalPaperTitle#=%=#NCL-UoR at SemEval-2024 Task 8: Fine-tuning Large Language Models for Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection ShortPaperTitle#=%=# NumberOfPages#=%=#7 CopyrightSigned#=%=#Feng Xiong JobTitle#==# Organization#==#School of Computing, Newcastle University, Newcastle upon Tyne, UK Abstract#==#SemEval-2024 Task 8 introduces the challenge of identifying machine-generated texts from diverse Large Language Models (LLMs) in various languages and domains. The task comprises three subtasks: binary classification in monolingual and multilingual (Subtask A), multi-class classification (Subtask B), and mixed text detection (Subtask C). This paper focuses on Subtask A \& B. To tackle this task, this paper proposes two methods: 1) using traditional machine learning (ML) with natural language preprocessing (NLP) for feature extraction, and 2) fine-tuning LLMs for text classification. For fine-tuning, we use the train datasets provided by the task organizers. The results show that transformer models like LoRA-RoBERTa and XLM-RoBERTa outperform traditional ML models, particularly in multilingual subtasks. However, traditional ML models performed better than transformer models for the monolingual task, demonstrating the importance of considering the specific characteristics of each subtask when selecting an appropriate approach. Author{1}{Firstname}#=%=#Feng Author{1}{Lastname}#=%=#Xiong Author{1}{Username}#=%=#fengxiong Author{1}{Email}#=%=#xf199912@163.com Author{1}{Affiliation}#=%=#Newcastle University Author{2}{Firstname}#=%=#Thanet Author{2}{Lastname}#=%=#Markchom Author{2}{Username}#=%=#thanet.mar Author{2}{Email}#=%=#thanet.mar@gmail.com Author{2}{Affiliation}#=%=#University of Reading Author{3}{Firstname}#=%=#Ziwei Author{3}{Lastname}#=%=#Zheng Author{3}{Email}#=%=#z.zheng21@newcastle.ac.uk Author{3}{Affiliation}#=%=#Newcastle University Author{4}{Firstname}#=%=#Subin Author{4}{Lastname}#=%=#Jung Author{4}{Email}#=%=#s.jung4@newcastle.ac.uk Author{4}{Affiliation}#=%=#Newcastle University Author{5}{Firstname}#=%=#Varun Author{5}{Lastname}#=%=#Ojha Author{5}{Email}#=%=#varun.ojha@newcastle.ac.uk Author{5}{Affiliation}#=%=#Newcastle University Author{6}{Firstname}#=%=#Huizhi Author{6}{Lastname}#=%=#Liang Author{6}{Username}#=%=#okliang Author{6}{Email}#=%=#oklianghuizi@gmail.com Author{6}{Affiliation}#=%=#University of Newcastle ========== èéáğö