@inproceedings{yong-etal-2025-state,
    title = "The State of Multilingual {LLM} Safety Research: From Measuring The Language Gap To Mitigating It",
    author = "Yong, Zheng Xin  and
      Ermis, Beyza  and
      Fadaee, Marzieh  and
      Bach, Stephen  and
      Kreutzer, Julia",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.800/",
    pages = "15856--15871",
    ISBN = "979-8-89176-332-6",
    abstract = "This paper presents a comprehensive analysis of the linguistic diversity of LLM safety research, highlighting the English-centric nature of the field. Through a systematic review of nearly 300 publications from 2020{--}2024 across major NLP conferences and workshops at ACL, we identify a significant and growing language gap in LLM safety research, with even high-resource non-English languages receiving minimal attention. We further observe that non-English languages are rarely studied as a standalone language and that English safety research exhibits poor language documentation practice. To motivate future research into multilingual safety, we make several recommendations based on our survey, and we then pose three concrete future directions on safety evaluation, training data generation, and crosslingual safety generalization. Based on our survey and proposed directions, the field can develop more robust, inclusive AI safety practices for diverse global populations."
}