@inproceedings{onajol-etal-2024-lora,
    title = "{L}o{RA} adapter weight tuning with multi-task learning for Faux-Hate detection",
    author = "Onajol, Abhinandan  and
      Gani, Varun  and
      Marakatti, Praneeta  and
      Malwankar, Bhakti  and
      Biradar, Shankar",
    editor = "Biradar, Shankar  and
      Reddy, Kasu Sai Kartheek  and
      Saumya, Sunil  and
      Akhtar, Md. Shad",
    booktitle = "Proceedings of the 21st International Conference on Natural Language Processing (ICON): Shared Task on Decoding Fake Narratives in Spreading Hateful Stories (Faux-Hate)",
    month = dec,
    year = "2024",
    address = "AU-KBC Research Centre, Chennai, India",
    publisher = "NLP Association of India (NLPAI)",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.icon-fauxhate.11/",
    pages = "56--60",
    abstract = "Detecting misinformation and harmful language in bilingual texts, particularly those com-bining Hindi and English, poses considerabledifficulties. The intricacies of mixed-languagecontent and limited available resources compli-cate this task even more. The proposed workfocuses on unraveling deceptive stories thatpropagate hate. We have developed an inno-vative attention-weight-tuned LoRA Adopter-based model for such Faux-Hate content de-tection. This work is conducted as a partof the ICON 2024 shared task on DecodingFake narratives in spreading Hateful stories.The LoRA-enhanced architecture secured 13thplace among the participating teams for TaskA."
}