Model interpretability in toxicity detection greatly profits from token-level
annotations. However, currently such annotations are only available in English.
We introduce a dataset annotated for offensive language detection sourced from
a news forum, notable for its incorporation of the Austrian German dialect,
comprising 4,562 user comments. In addition to binary offensiveness
classification, we identify spans within each comment constituting vulgar
language or representing targets of offensive statements. We evaluate
fine-tuned language models as well as large language models in a zero- and
few-shot fashion. The results indicate that while fine-tuned models excel in
detecting linguistic peculiarities such as vulgar dialect, large language
models demonstrate superior performance in detecting offensiveness in
AustroTox. We publish the data and code.

模型解释性在毒性检测中获益于对令牌级注释。我们介绍了一个从新闻论坛获取的用于侮辱语言检测的数据集，其特点是包含奥地利德语方言，包含 4562 个用户评论。除了二进制侮辱分类外，我们还确定了每个评论中构成粗俗语言或代表侮辱性陈述目标的部分。我们以零次和少次迭代的方式评估了微调的语言模型和大型语言模型。结果表明，虽然微调模型在检测粗俗方言等语言特异性方面表现卓越，但大型语言模型在检测 AustroTox 的侮辱性方面表现出更高的性能。我们发布数据和代码。