The task of toxicity detection is still a relevant task, especially in the
context of safe and fair LMs development. Nevertheless, labeled binary toxicity
classification corpora are not available for all languages, which is
understandable given the resource-intensive nature of the annotation process.
Ukrainian, in particular, is among the languages lacking such resources. To our
knowledge, there has been no existing toxicity classification corpus in
Ukrainian. In this study, we aim to fill this gap by investigating
cross-lingual knowledge transfer techniques and creating labeled corpora by:
(i)~translating from an English corpus, (ii)~filtering toxic samples using
keywords, and (iii)~annotating with crowdsourcing. We compare LLMs prompting
and other cross-lingual transfer approaches with and without fine-tuning
offering insights into the most robust and efficient baselines.

通过在缺少资源的乌克兰语中，使用跨语言知识迁移技术，从英语语料库中翻译、使用关键词过滤有毒样本，并通过众包进行注释，填补了有关乌克兰语毒性分类语料库的差距，比较了 LLMs 诱导和其他跨语言迁移方法以及有无微调的鲁棒性和效率最高的基线模型。