  Large language models (LLMs) and small language models (SLMs) are being
adopted at remarkable speed, although their safety still remains a serious
concern. With the advent of multilingual S/LLMs, the question now becomes a
matter of scale: can we expand multilingual safety evaluations of these models
with the same velocity at which they are deployed? To this end we introduce
RTP-LX, a human-transcreated and human-annotated corpus of toxic prompts and
outputs in 28 languages. RTP-LX follows participatory design practices, and a
portion of the corpus is especially designed to detect culturally-specific
toxic language. We evaluate seven S/LLMs on their ability to detect toxic
content in a culturally-sensitive, multilingual scenario. We find that,
although they typically score acceptably in terms of accuracy, they have low
agreement with human judges when judging holistically the toxicity of a prompt,
and have difficulty discerning harm in context-dependent scenarios,
particularly with subtle-yet-harmful content (e.g. microagressions, bias). We
release of this dataset to contribute to further reduce harmful uses of these
models and improve their safe deployment.
