The State of Multilingual LLM Safety Research: From Measuring the
  Language Gap to Mitigating It
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.
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Zheng-Xin Yong
683daea0f3b72ddf735af296
Beyza Ermis
683daea0f3b72ddf735af297
Marzieh Fadaee
683daea0f3b72ddf735af298
Stephen H. Bach
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Julia Kreutzer
extracted_pending
2025-06-02T14:01:05.682Z
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/avatars/76f0cd55b4bf9c03d2686e146c6f795f.svg
Julia Kreutzer
user
JuliaKreutzerCohere
The State of Multilingual LLM Safety Research: From Measuring the
  Language Gap to Mitigating It
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.
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.
linguistic diversity LLM safety English-centric NLP conferences ACL language gap multilingual safety safety evaluation training data generation crosslingual safety generalization