@inproceedings{zhang-etal-2025-dcbu,
    title = "{DCBU} at {G}en{AI} Detection Task 1: Enhancing Machine-Generated Text Detection with Semantic and Probabilistic Features",
    author = "Zhang, Zhaowen  and
      Chen, Songhao  and
      Liu, Bingquan",
    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-1.12/",
    pages = "150--154",
    abstract = "This paper presents our approach to the MGT Detection Task 1, which focuses on detecting AI-generated content. The objective of this task is to classify texts as either machine-generated or human-written. We participated in Subtask A, which concentrates on English-only texts. We utilized the RoBERTa model for semantic feature extraction and the LLaMA3 model for probabilistic feature analysis. By integrating these features, we aimed to enhance the system{'}s classification accuracy. Our approach achieved strong results, with an F1 score of 0.7713 on Subtask A, ranking ninth among 36 teams. These results demonstrate the effectiveness of our feature integration strategy."
}