@inproceedings{kodali-etal-2025-bytesizedllm-dravidianlangtech,
    title = "byte{S}ized{LLM}@{D}ravidian{L}ang{T}ech 2025: Detecting {AI}-Generated Product Reviews in {D}ravidian Languages Using {XLM}-{R}o{BERT}a and Attention-{B}i{LSTM}",
    author = "Kodali, Rohith Gowtham  and
      Manukonda, Durga Prasad  and
      Pannakkaran, Maharajan",
    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-1.13/",
    pages = "74--79",
    ISBN = "979-8-89176-228-2",
    abstract = "This study presents a hybrid model integrating TamilXLM-RoBERTa and MalayalamXLM-RoBERTa with BiLSTM and attention mechanisms to classify AI-generated and human-written product reviews in Tamil and Malayalam. The model employs a transliteration-based fine-tuning strategy, effectively handling native, Romanized, and mixed-script text. Despite being trained on a relatively small portion of data, our approach demonstrates strong performance in distinguishing AI-generated content, achieving competitive macro F1 scores in the DravidianLangTech 2025 shared task. The proposed method showcases the effectiveness of multilingual transformers and hybrid architectures in tackling low-resource language challenges."
}