@inproceedings{indurthi-varma-2025-tesla,
    title = "Tesla at {G}en{AI} Detection Task 2: Fast and Scalable Method for Detection of Academic Essay Authenticity",
    author = "Indurthi, Vijayasaradhi  and
      Varma, Vasudeva",
    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-1.36/",
    pages = "317--322",
    abstract = "This paper describes a simple yet effective method to identify if academic essays have been written by students or generated through the language models in English language. We extract a set of style, language complexity, bias and subjectivity, and emotion-based features that can be used to distinguish human-written essays from machine-generated essays. Our methods rank 6th on the leaderboard, achieving an impressive F1-score of 0.986."
}