  Large language models (LLMs) are increasingly popular but are also prone to
generating bias, toxic or harmful language, which can have detrimental effects
on individuals and communities. Although most efforts is put to assess and
mitigate toxicity in generated content, it is primarily concentrated on
English, while it's essential to consider other languages as well. For
addressing this issue, we create and release FrenchToxicityPrompts, a dataset
of 50K naturally occurring French prompts and their continuations, annotated
with toxicity scores from a widely used toxicity classifier. We evaluate 14
different models from four prevalent open-sourced families of LLMs against our
dataset to assess their potential toxicity across various dimensions. We hope
that our contribution will foster future research on toxicity detection and
mitigation beyond Englis
