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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.00623 |
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| _version_ | 1866912562437685248 |
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| author | Zain, Ali Farooqui, Sareem Rafi, Muhammad |
| author_facet | Zain, Ali Farooqui, Sareem Rafi, Muhammad |
| contents | This paper presents presents three distinct systems developed for the M-DAIGT shared task on detecting AI generated content in news articles and academic abstracts. The systems includes: (1) A fine-tuned RoBERTa-base classifier, (2) A classical TF-IDF + Support Vector Machine (SVM) classifier , and (3) An Innovative ensemble model named Candace, leveraging probabilistic features extracted from multiple Llama-3.2 models processed by a customTransformer encoder.The RoBERTa-based system emerged as the most performant, achieving near-perfect results on both development and test sets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00623 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Multi-Strategy Approach for AI-Generated Text Detection Zain, Ali Farooqui, Sareem Rafi, Muhammad Computation and Language Artificial Intelligence This paper presents presents three distinct systems developed for the M-DAIGT shared task on detecting AI generated content in news articles and academic abstracts. The systems includes: (1) A fine-tuned RoBERTa-base classifier, (2) A classical TF-IDF + Support Vector Machine (SVM) classifier , and (3) An Innovative ensemble model named Candace, leveraging probabilistic features extracted from multiple Llama-3.2 models processed by a customTransformer encoder.The RoBERTa-based system emerged as the most performant, achieving near-perfect results on both development and test sets. |
| title | A Multi-Strategy Approach for AI-Generated Text Detection |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2509.00623 |