@inproceedings{park-etal-2025-llm,
    title = "{LLM}-{C}3{MOD}: A Human-{LLM} Collaborative System for Cross-Cultural Hate Speech Moderation",
    author = "Park, Junyeong  and
      Jeong, Seogyeong  and
      Song, Seyoung  and
      Lee, Yohan  and
      Oh, Alice",
    editor = "Prabhakaran, Vinodkumar  and
      Dev, Sunipa  and
      Benotti, Luciana  and
      Hershcovich, Daniel  and
      Cao, Yong  and
      Zhou, Li  and
      Cabello, Laura  and
      Adebara, Ife",
    booktitle = "Proceedings of the 3rd Workshop on Cross-Cultural Considerations in NLP (C3NLP 2025)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.c3nlp-1.7/",
    pages = "71--88",
    ISBN = "979-8-89176-237-4",
    abstract = "Content moderation platforms concentrate resources on English content despite serving predominantly non-English speaking users.Also, given the scarcity of native moderators for low-resource languages, non-native moderators must bridge this gap in moderation tasks such as hate speech moderation.Through a user study, we identify that non-native moderators struggle with understanding culturally-specific knowledge, sentiment, and internet culture in the hate speech.To assist non-native moderators, we present LLM-C3MOD, a human-LLM collaborative pipeline with three steps: (1) RAG-enhanced cultural context annotations; (2) initial LLM-based moderation; and (3) targeted human moderation for cases lacking LLM consensus.Evaluated on Korean hate speech dataset with Indonesian and German participants, our system achieves 78{\%} accuracy (surpassing GPT-4o{'}s 71{\%} baseline) while reducing human workload by 83.6{\%}.In addition, cultural context annotations improved non-native moderator accuracy from 22{\%} to 61{\%}, with humans notably excelling at nuanced tasks where LLMs struggle.Our findings demonstrate that non-native moderators, when properly supported by LLMs, can effectively contribute to cross-cultural hate speech moderation."
}