@inproceedings{gharib-elgendy-2025-ra,
    title = "{RA} at {G}en{AI} Detection Task 2: Fine-tuned Language Models For Detection of Academic Authenticity, Results and Thoughts",
    author = "Gharib, Rana  and
      Elgendy, Ahmed",
    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-1.35/",
    pages = "312--316",
    abstract = "This paper assesses the performance of {\textquotedblleft}RA{\textquotedblright} in the Academic Essay Authenticity Challenge, which saw nearly 30 teams participating in each subtask. We employed cutting-edge transformer-based models to achieve our results. Our models consistently exceeded both the mean and median scores across the tasks. Notably, we achieved an F1-score of 0.969 in classifying AI-generated essays in English and an F1-score of 0.957 for classifying AI-generated essays in Arabic. Additionally, this paper offers insights into the current state of AI-generated models and argues that the benchmarking methods currently in use do not accurately reflect real-world scenarios."
}