@inproceedings{manukonda-kodali-2025-bytesizedllm-dravidianlangtech,
    title = "byte{S}ized{LLM}@{D}ravidian{L}ang{T}ech 2025: Fake News Detection in {D}ravidian Languages Using Transliteration-Aware {XLM}-{R}o{BERT}a and Transformer Encoder-Decoder",
    author = "Manukonda, Durga Prasad  and
      Kodali, Rohith Gowtham",
    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-2025-COMPUTEL/2025.dravidianlangtech-1.10/",
    pages = "56--61",
    ISBN = "979-8-89176-228-2",
    abstract = "This study addresses the challenge of fake news detection in code-mixed and transliterated text, focusing on a multilingual setting with significant linguistic variability. A novel approach is proposed, leveraging a fine-tuned multilingual transformer model trained using Masked Language Modeling on a dataset that includes original, fully transliterated, and partially transliterated text. The fine-tuned embeddings are integrated into a custom transformer classifier designed to capture complex dependencies in multilingual sequences. The system achieves state-of-the-art performance, demonstrating the effectiveness of combining transliteration-aware fine-tuning with robust transformer architectures to handle code-mixed and resource-scarce text, providing a scalable solution for multilingual natural language processing tasks."
}