@inproceedings{b-etal-2025-overview,
    title = "Overview of the Shared Task on Detecting {AI} Generated Product Reviews in {D}ravidian Languages: {D}ravidian{L}ang{T}ech@{NAACL} 2025",
    author = "B, Premjith  and
      Kumaresh, Nandhini  and
      Chakravarthi, Bharathi Raja  and
      Durairaj, Thenmozhi  and
      Palani, Balasubramanian  and
      Thavareesan, Sajeetha  and
      Kumaresan, Prasanna Kumar",
    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/moar-dois/2025.dravidianlangtech-1.21/",
    doi = "10.18653/v1/2025.dravidianlangtech-1.21",
    pages = "123--132",
    ISBN = "979-8-89176-228-2",
    abstract = "The detection of AI-generated product reviews is critical due to the increased use of large language models (LLMs) and their capability to generate convincing sentences. The AI-generated reviews can affect the consumers and businesses as they influence the trust and decision-making. This paper presents the overview of the shared task on Detecting AI-generated product reviews in Dravidian Languages'' organized as part of DravidianLangTech@NAACL 2025. This task involves two subtasks{---}one in Malayalam and another in Tamil, both of which are binary classifications where a review is to be classified as human-generated or AI-generated. The dataset was curated by collecting comments from YouTube videos. Various machine learning and deep learning-based models ranging from SVM to transformer-based architectures were employed by the participants."
}