  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.
