Summary of Sharif-mgtd at Semeval-2024 Task 8: a Transformer-based Approach to Detect Machine Generated Text, by Seyedeh Fatemeh Ebrahimi et al.
Sharif-MGTD at SemEval-2024 Task 8: A Transformer-Based Approach to Detect Machine Generated Text
by Seyedeh Fatemeh Ebrahimi, Karim Akhavan Azari, Amirmasoud Iravani, Arian Qazvini, Pouya Sadeghi, Zeinab Sadat Taghavi, Hossein Sameti
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning-based approach is proposed to detect Machine-Generated Text (MGT) with improved performance on the SemEval-2024 competition framework. The study fine-tunes a RoBERTa-base transformer, a powerful neural architecture, for binary classification of MGT detection. The model achieves an accuracy of 78.9% on the test dataset, ranking 57th among participants. The system excels at identifying human-written texts but struggles to accurately discern MGTs due to limited hardware resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to tell apart human-written text from machine-generated text. They used a powerful computer model called RoBERTa-base to help with this task. The model was trained and tested on a specific competition framework, and it did very well, getting 78.9% of the test results correct. This is a significant achievement, as it can be challenging for computers to accurately identify machine-generated text. However, there are still some limitations to the system, such as its difficulty in identifying certain types of machine-generated text. |
Keywords
» Artificial intelligence » Classification » Machine learning » Transformer
