@inproceedings{jayaraman-etal-2025-analysisarchitects,
    title = "{A}nalysis{A}rchitects@{D}ravidian{L}ang{T}ech 2025: {BERT} Based Approach For Detecting {AI} Generated Product Reviews In {D}ravidian Languages",
    author = "Jayaraman, Abirami  and
      Shanmugam, Aruna Devi  and
      Sasikumar, Dharunika  and
      B, Bharathi",
    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/fix-sig-urls/2025.dravidianlangtech-1.104/",
    pages = "609--613",
    ISBN = "979-8-89176-228-2",
    abstract = "The shared task on Detecting AI-generated Product Reviews in Dravidian Languages is aimed at addressing the growing concern of AI-generated product reviews, specifically in Malayalam and Tamil. As AI tools become more advanced, the ability to distinguish between human-written and AI-generated content has become increasingly crucial, especially in the domain of online reviews where authenticity is essential for consumer decision-making. In our approach, we used the ALBERT, IndicBERT, and Support Vector Machine (SVM) models to classify the reviews. The results of our experiments demonstrate the effectiveness of our methods in detecting AI-generated content."
}