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| Main Authors: | , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2502.16857 |
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| _version_ | 1866912243838353408 |
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| author | Trivedi, Avinash Sivanesan, Sangeetha |
| author_facet | Trivedi, Avinash Sivanesan, Sangeetha |
| contents | This paper presents an effective approach to detect AI-generated text, developed for the Defactify 4.0 shared task at the fourth workshop on multimodal fact checking and hate speech detection. The task consists of two subtasks: Task-A, classifying whether a text is AI generated or human written, and Task-B, classifying the specific large language model that generated the text. Our team (Sarang) achieved the 1st place in both tasks with F1 scores of 1.0 and 0.9531, respectively. The methodology involves adding noise to the dataset to improve model robustness and generalization. We used an ensemble of DeBERTa models to effectively capture complex patterns in the text. The result indicates the effectiveness of our noise-driven and ensemble-based approach, setting a new standard in AI-generated text detection and providing guidance for future developments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16857 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sarang at DEFACTIFY 4.0: Detecting AI-Generated Text Using Noised Data and an Ensemble of DeBERTa Models Trivedi, Avinash Sivanesan, Sangeetha Computation and Language Artificial Intelligence This paper presents an effective approach to detect AI-generated text, developed for the Defactify 4.0 shared task at the fourth workshop on multimodal fact checking and hate speech detection. The task consists of two subtasks: Task-A, classifying whether a text is AI generated or human written, and Task-B, classifying the specific large language model that generated the text. Our team (Sarang) achieved the 1st place in both tasks with F1 scores of 1.0 and 0.9531, respectively. The methodology involves adding noise to the dataset to improve model robustness and generalization. We used an ensemble of DeBERTa models to effectively capture complex patterns in the text. The result indicates the effectiveness of our noise-driven and ensemble-based approach, setting a new standard in AI-generated text detection and providing guidance for future developments. |
| title | Sarang at DEFACTIFY 4.0: Detecting AI-Generated Text Using Noised Data and an Ensemble of DeBERTa Models |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2502.16857 |