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

我们创建并发布了 FrenchToxicityPrompts 数据集，评估了 14 种不同的大型语言模型对于 50K 个自然发生的法语提示及其延续的潜在有害性，并希望这一贡献能促进对除英语以外语言的有害性检测和缓解的未来研究。