title: The State of Multilingual LLM Safety Research: From Measuring the
    Language Gap to Mitigating It
summary: 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.
paper_authors._id: 683daea0f3b72ddf735af295
paper_authors.name: Zheng-Xin Yong
paper_authors._id: 683daea0f3b72ddf735af296
paper_authors.name: Beyza Ermis
paper_authors._id: 683daea0f3b72ddf735af297
paper_authors.name: Marzieh Fadaee
paper_authors._id: 683daea0f3b72ddf735af298
paper_authors.name: Stephen H. Bach
paper_authors._id: 683daea0f3b72ddf735af299
paper_authors.name: Julia Kreutzer
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-06-02T14:01:05.682Z
paper_authors._id: 6544e43b12da508864c38f96
paper_authors.avatarUrl: /avatars/76f0cd55b4bf9c03d2686e146c6f795f.svg
paper_authors.fullname: Julia Kreutzer
paper_authors.type: user
paper_authors.user: JuliaKreutzerCohere
paper_title: The State of Multilingual LLM Safety Research: From Measuring the
    Language Gap to Mitigating It
paper_summary: 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.
paper_ai_summary: The analysis reveals a significant language gap in LLM safety research, focusing mainly on English, and suggests recommendations for multilingual safety evaluation and crosslingual generalization to enhance global AI safety.
paper_ai_keywords: linguistic diversity LLM safety English-centric NLP conferences ACL language gap multilingual safety safety evaluation training data generation crosslingual safety generalization