Sharif-MGTD at SemEval-2024 Task 8: A Transformer-Based Approach to Detect Machine Generated Text
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910833521459200 |
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| author | Ebrahimi, Seyedeh Fatemeh Azari, Karim Akhavan Iravani, Amirmasoud Qazvini, Arian Sadeghi, Pouya Taghavi, Zeinab Sadat Sameti, Hossein |
| author_facet | Ebrahimi, Seyedeh Fatemeh Azari, Karim Akhavan Iravani, Amirmasoud Qazvini, Arian Sadeghi, Pouya Taghavi, Zeinab Sadat Sameti, Hossein |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_11774 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sharif-MGTD at SemEval-2024 Task 8: A Transformer-Based Approach to Detect Machine Generated Text Ebrahimi, Seyedeh Fatemeh Azari, Karim Akhavan Iravani, Amirmasoud Qazvini, Arian Sadeghi, Pouya Taghavi, Zeinab Sadat Sameti, Hossein Computation and Language Artificial Intelligence 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. |
| title | Sharif-MGTD at SemEval-2024 Task 8: A Transformer-Based Approach to Detect Machine Generated Text |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2407.11774 |