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Sharif-MGTD at SemEval-2024 Task 8: A Transformer-Based Approach to Detect Machine Generated Text

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arxiv 2407.11774 v1 pith:PIK5GDIE submitted 2024-07-16 cs.CL cs.AI

Seyedeh Fatemeh Ebrahimi , Karim Akhavan Azari , Amirmasoud Iravani , Arian Qazvini , Pouya Sadeghi , Zeinab Sadat Taghavi , Hossein Sameti This is my paper · ORCID
classification cs.CLcs.AI
keywords languagetextmodelsneuralsemeval-2024systemtaskwhile
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Detecting Machine-Generated Text (MGT) has emerged as a significant area of study within Natural Language Processing. While language models generate text, they often leave discernible traces, which can be scrutinized using either traditional feature-based methods or more advanced neural language models. In this research, we explore the effectiveness of fine-tuning a RoBERTa-base transformer, a powerful neural architecture, to address MGT detection as a binary classification task. Focusing specifically on Subtask A (Monolingual-English) within the SemEval-2024 competition framework, our proposed system achieves an accuracy of 78.9% on the test dataset, positioning us at 57th among participants. Our study addresses this challenge while considering the limited hardware resources, resulting in a system that excels at identifying human-written texts but encounters challenges in accurately discerning MGTs.

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Cited by 2 Pith papers

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