Sharif-MGTD at SemEval-2024 Task 8: A Transformer-Based Approach to Detect Machine Generated Text
| Autor: | Ebrahimi, Seyedeh Fatemeh, Azari, Karim Akhavan, Iravani, Amirmasoud, Qazvini, Arian, Sadeghi, Pouya, Taghavi, Zeinab Sadat, Sameti, Hossein |
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| Rok vydání: | 2024 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 8 pages, 3 figures, 2 tables. Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024) |
| Databáze: | arXiv |
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