PetKaz at SemEval-2024 Task 8: Can Linguistics Capture the Specifics of LLM-generated Text?
<|reference_start|>PetKaz at SemEval-2024 Task 8: Can Linguistics Capture the Specifics of LLM-generated Text?: 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.<|reference_end|>
@article{petukhova2024petkaz,
    title={PetKaz at SemEval-2024 Task 8: Can Linguistics Capture the Specifics of
  LLM-generated Text?},
    author={Kseniia Petukhova, Roman Kazakov, Ekaterina Kochmar},
    journal={arXiv preprint arXiv:2404.05483},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.05483},
    primaryClass={cs.CL cs.AI}
}