@inproceedings{nguyen-etal-2023-stacking,
    title = "Stacking the Odds: Transformer-Based Ensemble for {AI}-Generated Text Detection",
    author = "Nguyen, Duke  and
      Naing, Khaing Myat Noe  and
      Joshi, Aditya",
    editor = "Muresan, Smaranda  and
      Chen, Vivian  and
      Casey, Kennington  and
      David, Vandyke  and
      Nina, Dethlefs  and
      Koji, Inoue  and
      Erik, Ekstedt  and
      Stefan, Ultes",
    booktitle = "Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association",
    month = nov,
    year = "2023",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2023.alta-1.22/",
    pages = "173--178",
    abstract = "This paper reports our submission under the team name {\textquoteleft}SynthDetectives' to the ALTA 2023 Shared Task. We use a stacking ensemble of Transformers for the task of AI-generated text detection. Our approach is novel in terms of its choice of models in that we use accessible and lightweight models in the ensemble. We show that ensembling the models results in an improved accuracy in comparison with using them individually. Our approach achieves an accuracy score of 0.9555 on the official test data provided by the shared task organisers."
}