PetKaz at SemEval-2024 Task 8: Can Linguistics Capture the Specifics of LLM-generated Text?
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arXiv
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| Format: | Preprint |
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2024
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| author | Petukhova, Kseniia Kazakov, Roman Kochmar, Ekaterina |
| author_facet | Petukhova, Kseniia Kazakov, Roman Kochmar, Ekaterina |
| contents | In this paper, we present our submission to the SemEval-2024 Task 8 "Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection", focusing on the detection of machine-generated texts (MGTs) in English. Specifically, our approach relies on combining embeddings from the RoBERTa-base with diversity features and uses a resampled training set. We score 12th from 124 in the ranking for Subtask A (monolingual track), and our results show that our approach is generalizable across unseen models and domains, achieving an accuracy of 0.91. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_05483 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PetKaz at SemEval-2024 Task 8: Can Linguistics Capture the Specifics of LLM-generated Text? Petukhova, Kseniia Kazakov, Roman Kochmar, Ekaterina Computation and Language Artificial Intelligence I.2.7 In this paper, we present our submission to the SemEval-2024 Task 8 "Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection", focusing on the detection of machine-generated texts (MGTs) in English. Specifically, our approach relies on combining embeddings from the RoBERTa-base with diversity features and uses a resampled training set. We score 12th from 124 in the ranking for Subtask A (monolingual track), and our results show that our approach is generalizable across unseen models and domains, achieving an accuracy of 0.91. |
| title | PetKaz at SemEval-2024 Task 8: Can Linguistics Capture the Specifics of LLM-generated Text? |
| topic | Computation and Language Artificial Intelligence I.2.7 |
| url | https://arxiv.org/abs/2404.05483 |