SubmissionNumber#=%=#180
FinalPaperTitle#=%=#Team Innovative at SemEval-2024 Task 8: Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection
ShortPaperTitle#=%=#
NumberOfPages#=%=#5
CopyrightSigned#=%=#SURBHI SHARMA
JobTitle#==#
Organization#==#
Abstract#==#With the widespread adoption of large language models (LLMs), such as ChatGPT and GPT-4, in various domains, concerns regarding their potential misuse, including spreading misinformation and disrupting education, have escalated. The need to discern between human-generated and machine-generated text has become increasingly crucial. This paper addresses the challenge of automatic text classification with a focus on distinguishing between human-written and machine-generated text. Leveraging the robust capabilities of the RoBERTa model, we propose an approach for text classification, termed as RoBERTa hybrid, which involves fine-tuning the pre-trained Roberta model coupled with additional dense layers and softmax activation for authorship attribution. In this paper, we present an approach that leverages \newcite{fab} Stylometric features, hybrid features, and the output probabilities of a fine-tuned RoBERTa model. Our method achieves a test accuracy of 73\% and a validation accuracy of 89\%, demonstrating promising advancements in the field of machine-generated text detection. These results mark significant progress in the domain of machine-generated text detection, as evidenced by our 74th position on the leaderboard for Subtask-A of SemEval-2024 Task 8.
Author{1}{Firstname}#=%=#Surbhi
Author{1}{Lastname}#=%=#Sharma
Author{1}{Username}#=%=#kaluti
Author{1}{Email}#=%=#surbhisharma9099@gmail.com
Author{1}{Affiliation}#=%=#Purdue University
Author{2}{Firstname}#=%=#Irfan
Author{2}{Lastname}#=%=#Mansuri
Author{2}{Username}#=%=#iffy
Author{2}{Email}#=%=#iffyaiyan@gmail.com
Author{2}{Affiliation}#=%=#National Institute of Technology Jamshedpur

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