@inproceedings{kiss-berend-2025-szegedai,
    title = "{S}zeged{AI} at {G}en{AI} Detection Task 1: Beyond Binary - Soft-Voting Multi-Class Classification for Binary Machine-Generated Text Detection Across Diverse Language Models",
    author = "Kiss, Mihaly  and
      Berend, G{\'a}bor",
    editor = "Alam, Firoj  and
      Nakov, Preslav  and
      Habash, Nizar  and
      Gurevych, Iryna  and
      Chowdhury, Shammur  and
      Shelmanov, Artem  and
      Wang, Yuxia  and
      Artemova, Ekaterina  and
      Kutlu, Mucahid  and
      Mikros, George",
    booktitle = "Proceedings of the 1stWorkshop on GenAI Content Detection (GenAIDetect)",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "International Conference on Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.genaidetect-1.15/",
    pages = "166--172",
    abstract = "This paper describes the participation of the SzegedAI team in Subtask A of Task 1 at the COLING 2025 Workshop on Detecting AI-Generated Content. Our solutions investigate the effectiveness of combining multi-class approaches with ensemble methods for detecting machine-generated text. This approach groups models into multiple classes based on properties such as model size or generative capabilities. Additionally, we employ a length-based method, utilizing specialized expert models designed for specific text length ranges. During inference, we condense multi-class predictions into a binary outcome, categorizing any label other than human as AI-generated. The effectiveness of both standard and snapshot ensemble techniques is evaluated. Although not all multi-class configurations outperformed the binary setup, our findings indicate that the combination of multi-class training and ensemble methods can enhance performance over single-method or binary approaches."
}