@inproceedings{bhuvana-etal-2025-ssntrio,
    title = "{SSNT}rio@{D}ravidian{L}ang{T}ech 2025: Identification of {AI} Generated Content in {D}ravidian Languages using Transformers",
    author = "Bhuvana, J  and
      T T, Mirnalinee  and
      R, Rohan  and
      Seshan, Diya  and
      Koushik, Avaneesh",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Madasamy, Anand Kumar  and
      Thavareesan, Sajeetha  and
      Sherly, Elizabeth  and
      Rajiakodi, Saranya  and
      Palani, Balasubramanian  and
      Subramanian, Malliga  and
      Cn, Subalalitha  and
      Chinnappa, Dhivya",
    booktitle = "Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
    month = may,
    year = "2025",
    address = "Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.dravidianlangtech-1.59/",
    pages = "335--339",
    ISBN = "979-8-89176-228-2",
    abstract = "The increasing prevalence of AI-generated content has raised concerns about the authenticity and reliability of online reviews, particularly in resource-limited languages like Tamil and Malayalam. This paper presents an approach to the Shared Task on Detecting AI-generated Product Reviews in Dravidian Languages at NAACL2025, which focuses on distinguishing AI-generated reviews from human-written ones in Tamil and Malayalam. Several transformer-based models, including IndicBERT, RoBERTa, mBERT, and XLM-R, were evaluated, with language-specific BERT models for Tamil and Malayalam demonstrating the best performance. The chosen methodologies were evaluated using Macro Average F1 score. In the rank list released by the organizers, team SSNTrio, achieved ranks of 3rd and 29th for the Malayalam and Tamil datasets with Macro Average F1 Scores of 0.914 and 0.598 respectively."
}