@inproceedings{galat-2024-advancing,
    title = "Advancing {LLM} detection in the {ALTA} 2024 Shared Task: Techniques and Analysis",
    author = "Galat, Dima",
    editor = "Baldwin, Tim  and
      Rodr{\'i}guez M{\'e}ndez, Sergio Jos{\'e}  and
      Kuo, Nicholas",
    booktitle = "Proceedings of the 22nd Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2024",
    address = "Canberra, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.alta-1.18/",
    pages = "203--206",
    abstract = "The recent proliferation of AI-generated content has prompted significant interest in developing reliable detection methods. This study explores techniques for identifying AIgenerated text through sentence-level evaluation within hybrid articles. Our findings indicate that ChatGPT-3.5 Turbo exhibits distinct, repetitive probability patterns that enable consistent in-domain detection. Empirical tests show that minor textual modifications, such as rewording, have minimal impact on detection accuracy. These results provide valuable insights for advancing AI detection methodologies, offering a pathway toward robust solutions to address the complexities of synthetic text identification."
}