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PetKaz at SemEval-2024 Task 8: Can Linguistics Capture the Specifics of LLM-generated Text?

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arxiv 2404.05483 v1 pith:ORMPTQWI submitted 2024-04-08 cs.CL cs.AI

classification cs.CLcs.AI
keywords approachdetectionmachine-generatedsemeval-2024tasktextaccuracyachieving
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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.

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