@inproceedings{kodali-etal-2025-bytesizedllm,
    title = "byte{S}ized{LLM}@{NLU} of {D}evanagari Script Languages 2025: Hate Speech Detection and Target Identification Using Customized Attention {B}i{LSTM} and {XLM}-{R}o{BERT}a Base Embeddings",
    author = "Kodali, Rohith Gowtham  and
      Manukonda, Durga Prasad  and
      Iglesias, Daniel",
    editor = "Sarveswaran, Kengatharaiyer  and
      Vaidya, Ashwini  and
      Krishna Bal, Bal  and
      Shams, Sana  and
      Thapa, Surendrabikram",
    booktitle = "Proceedings of the First Workshop on Challenges in Processing South Asian Languages (CHiPSAL 2025)",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/2025.chipsal-1.25/",
    pages = "242--247",
    abstract = "This paper presents a novel approach to hate speech detection and target identification across Devanagari-script languages, with a focus on Hindi and Nepali. Leveraging an Attention BiLSTM-XLM-RoBERTa architecture, our model effectively captures language-specific features and sequential dependencies crucial for multilingual natural language understanding (NLU). In Task B (Hate Speech Detection), our model achieved a Macro F1 score of 0.7481, demonstrating its robustness in identifying hateful content across linguistic variations. For Task C (Target Identification), it reached a Macro F1 score of 0.6715, highlighting its ability to classify targets into {\textquotedblleft}individual,{\textquotedblright} {\textquotedblleft}organization,{\textquotedblright} and {\textquotedblleft}community{\textquotedblright} with high accuracy. Our work addresses the gap in Devanagari-scripted multilingual hate speech analysis and sets a benchmark for future research in low-resource language contexts."
}