@inproceedings{abiola-etal-2025-cic-nlp,
    title = "{CIC}-{NLP} at {G}en{AI} Detection Task 1: Leveraging {D}istil{BERT} for Detecting Machine-Generated Text in {E}nglish",
    author = "Abiola, Tolulope Olalekan  and
      Bizuneh, Tewodros Achamaleh  and
      Abiola, Oluwatobi Joseph  and
      Oladepo, Temitope Olasunkanmi  and
      Ojo, Olumide Ebenezer  and
      Sidorov, Grigori  and
      Kolesnikova, Olga",
    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/jlcl-multiple-ingestion/2025.genaidetect-1.29/",
    pages = "271--277",
    abstract = "As machine-generated texts (MGT) become increasingly similar to human writing, these dis- tinctions are harder to identify. In this paper, we as the CIC-NLP team present our submission to the Gen-AI Content Detection Workshop at COLING 2025 for Task 1 Subtask A, which involves distinguishing between text generated by LLMs and text authored by humans, with an emphasis on detecting English-only MGT. We applied the DistilBERT model to this binary classification task using the dataset provided by the organizers. Fine-tuning the model effectively differentiated between the classes, resulting in a micro-average F1-score of 0.70 on the evaluation test set. We provide a detailed explanation of the fine-tuning parameters and steps involved in our analysis."
}