DeepPavlov at SemEval-2024 Task 8: Leveraging Transfer Learning for Detecting Boundaries of Machine-Generated Texts
<|reference_start|>DeepPavlov at SemEval-2024 Task 8: Leveraging Transfer Learning for Detecting Boundaries of Machine-Generated Texts: The Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection shared task in the SemEval-2024 competition aims to tackle the problem of misusing collaborative human-AI writing. Although there are a lot of existing detectors of AI content, they are often designed to give a binary answer and thus may not be suitable for more nuanced problem of finding the boundaries between human-written and machine-generated texts, while hybrid human-AI writing becomes more and more popular. In this paper, we address the boundary detection problem. Particularly, we present a pipeline for augmenting data for supervised fine-tuning of DeBERTaV3. We receive new best MAE score, according to the leaderboard of the competition, with this pipeline.<|reference_end|>
@article{voznyuk2024deeppavlov,
    title={DeepPavlov at SemEval-2024 Task 8: Leveraging Transfer Learning for
  Detecting Boundaries of Machine-Generated Texts},
    author={Anastasia Voznyuk and Vasily Konovalov},
    journal={arXiv preprint arXiv:2405.10629},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.10629},
    primaryClass={cs.CL cs.AI}
}