@inproceedings{ngueajio-etal-2025-think,
    title = "Think Like a Person Before Responding: A Multi-Faceted Evaluation of Persona-Guided {LLM}s for Countering Hate Speech.",
    author = "Ngueajio, Mikel  and
      Plaza-del-Arco, Flor Miriam  and
      Chung, Yi-Ling  and
      Rawat, Danda  and
      Cercas Curry, Amanda",
    editor = "Calabrese, Agostina  and
      de Kock, Christine  and
      Nozza, Debora  and
      Plaza-del-Arco, Flor Miriam  and
      Talat, Zeerak  and
      Vargas, Francielle",
    booktitle = "Proceedings of the The 9th Workshop on Online Abuse and Harms (WOAH)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.woah-1.10/",
    pages = "104--123",
    ISBN = "979-8-89176-105-6",
    abstract = "Automated counter-narratives (CN) offer a promising strategy for mitigating online hate speech, yet concerns about their affective tone, accessibility, and ethical risks remain. We propose a framework for evaluating Large Language Model (LLM)-generated CNs across four dimensions: persona framing, verbosity and readability, affective tone, and ethical robustness. Using GPT-4o-Mini, Cohere{'}s CommandR-7B, and Meta{'}s LLaMA 3.1-70B, we assess three prompting strategies on the MT-Conan and HatEval datasets.Our findings reveal that LLM-generated CNs are often verbose and adapted for people with college-level literacy, limiting their accessibility. While emotionally guided prompts yield more empathetic and readable responses, there remain concerns surrounding safety and effectiveness."
}