Sharif-MGTD at SemEval-2024 Task 8: A Transformer-Based Approach to Detect Machine Generated Text
<|reference_start|>Sharif-MGTD at SemEval-2024 Task 8: A Transformer-Based Approach to Detect Machine Generated Text: 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.<|reference_end|>
@article{ebrahimi2024sharif-mgtd,
    title={Sharif-MGTD at SemEval-2024 Task 8: A Transformer-Based Approach to
  Detect Machine Generated Text},
    author={Seyedeh Fatemeh Ebrahimi, Karim Akhavan Azari, Amirmasoud Iravani,
  Arian Qazvini, Pouya Sadeghi, Zeinab Sadat Taghavi, Hossein Sameti},
    journal={arXiv preprint arXiv:2407.11774},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11774},
    primaryClass={cs.CL cs.AI}
}