@inproceedings{thevakumar-thevakumar-2025-rathan,
    title = "{RATHAN}@{D}ravidian{L}ang{T}ech 2025: Annaparavai - Separate the Authentic Human Reviews from {AI}-generated one",
    author = "Thevakumar, Jubeerathan  and
      Thevakumar, Luheerathan",
    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/ingest-emnlp/2025.dravidianlangtech-1.66/",
    doi = "10.18653/v1/2025.dravidianlangtech-1.66",
    pages = "371--375",
    ISBN = "979-8-89176-228-2",
    abstract = "Detecting AI-generated reviews is crucial for maintaining the authenticity of online feedback in low-resource languages like Tamil and Malayalam. We propose a transfer learning-based approach using embeddings from XLM-RoBERTa, IndicBERT, mT5, and Sentence-BERT, validated with five-fold cross-validation via XGBoost. These embeddings are used to train deep neural networks (DNNs), refined through a weighted ensemble model. Our method achieves 90{\%} F1-score for Malayalam and 73{\%} for Tamil, demonstrating the effectiveness of transfer learning and ensembling for review detection. The source code is publicly available to support further research and improve online review systems in multilingual settings."
}