All Languages Matter: On the Multilingual Safety of Large Language
  Models
Wenxuan Wang Zhaopeng Tu Chang Chen Youliang Yuan Jen-tse Huang Wenxiang Jiao Michael R. Lyu
Safety lies at the core of developing and deploying large language models
(LLMs). However, previous safety benchmarks only concern the safety in one
language, e.g. the majority language in the pretraining data such as English.
In this work, we build the first multilingual safety benchmark for LLMs,
XSafety, in response to the global deployment of LLMs in practice. XSafety
covers 14 kinds of commonly used safety issues across 10 languages that span
several language families. We utilize XSafety to empirically study the
multilingual safety for 4 widely-used LLMs, including both close-API and
open-source models. Experimental results show that all LLMs produce
significantly more unsafe responses for non-English queries than English ones,
indicating the necessity of developing safety alignment for non-English
languages. In addition, we propose several simple and effective prompting
methods to improve the multilingual safety of ChatGPT by evoking safety
knowledge and improving cross-lingual generalization of safety alignment. Our
prompting method can significantly reduce the ratio of unsafe responses from
19.1% to 9.7% for non-English queries. We release our data at
https://github.com/Jarviswang94/Multilingual_safety_benchmark.