@inproceedings{farhan-2025-hyderabadi,
    title = "Hyderabadi Pearls at Multilingual Counterspeech Generation : {HALT} : Hate Speech Alleviation using Large Language Models and Transformers",
    author = "Farhan, Md Shariq",
    editor = "Bonaldi, Helena  and
      Vallecillo-Rodr{\'i}guez, Mar{\'i}a Estrella  and
      Zubiaga, Irune  and
      Montejo-R{\'a}ez, Arturo  and
      Soroa, Aitor  and
      Mart{\'i}n-Valdivia, Mar{\'i}a Teresa  and
      Guerini, Marco  and
      Agerri, Rodrigo",
    booktitle = "Proceedings of the First Workshop on Multilingual Counterspeech Generation",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.mcg-1.8/",
    pages = "65--76",
    abstract = "This paper explores the potential of using fine- tuned Large Language Models (LLMs) for generating counter-narratives (CNs) to combat hate speech (HS). We focus on English and Basque, leveraging the ML{\_}MTCONAN{\_}KN dataset, which provides hate speech and counter-narrative pairs in multiple languages. Our study compares the performance of Mis- tral, Llama, and a Llama-based LLM fine- tuned on a Basque language dataset for CN generation. The generated CNs are evalu- ated using JudgeLM (a LLM to evaluate other LLMs in open-ended scenarios) along with traditional metrics such as ROUGE-L, BLEU, BERTScore, and other traditional metrics. The results demonstrate that fine-tuned LLMs can produce high-quality contextually relevant CNs for low-resource languages that are comparable to human-generated responses, offering a sig- nificant contribution to combating online hate speech across diverse linguistic settings."
}