@inproceedings{mehak-etal-2025-techexperts,
    title = "{T}ech{E}xperts({IPN}) at {G}en{AI} Detection Task 1: Detecting {AI}-Generated Text in {E}nglish and Multilingual Contexts",
    author = "Mehak, Gull  and
      Qasim, Amna  and
      Meque, Abdul Gafar Manuel  and
      Hussain, Nisar  and
      Sidorov, Grigori  and
      Gelbukh, Alexander",
    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-1.14/",
    pages = "161--165",
    abstract = "The ever-increasing spread of AI-generated text, driven by the considerable progress in large language models, entails a real problem for all digital platforms: how to ensure con tent authenticity. The team TechExperts(IPN) presents a method for detecting AI-generated content in English and multilingual contexts, using the google/gemma-2b model fine-tuned for COLING 2025 shared task 1 for English and multilingual. Training results show peak F1 scores of 97.63{\%} for English and 97.87{\%} for multilingual detection, highlighting the model{'}s effectiveness in supporting content integrity across platforms."
}