Toxic language detection systems often falsely flag text that contains
minority group mentions as toxic, as those groups are often the targets of
online hate. Such over-reliance on spurious correlations also causes systems to
struggle with detecting implicitly toxic language. To help mitigate these
issues, we create ToxiGen, a new large-scale and machine-generated dataset of
274k toxic and benign statements about 13 minority groups. We develop a
demonstration-based prompting framework and an adversarial
classifier-in-the-loop decoding method to generate subtly toxic and benign text
with a massive pretrained language model. Controlling machine generation in
this way allows ToxiGen to cover implicitly toxic text at a larger scale, and
about more demographic groups, than previous resources of human-written text.
We conduct a human evaluation on a challenging subset of ToxiGen and find that
annotators struggle to distinguish machine-generated text from human-written
language. We also find that 94.5% of toxic examples are labeled as hate speech
by human annotators. Using three publicly-available datasets, we show that
finetuning a toxicity classifier on our data improves its performance on
human-written data substantially. We also demonstrate that ToxiGen can be used
to fight machine-generated toxicity as finetuning improves the classifier
significantly on our evaluation subset. Our code and data can be found at
this https URL.

本论文介绍了 ToxiGen，一个新的大规模自动生成的 274k 毒性和良性陈述数据集，用于检测涉及 13 个少数群体的文本。通过使用基于展示的提示框架和诱导循环解码方法来生成微妙的毒性和良性文本，ToxiGen 能够覆盖范围更广的暗含毒性文本，包括更多样化的人口群体。与此同时，研究者通过人类评估表明，94.5％的毒性示例被人类标注者标记为仇恨言论。合理的数据利用对文本分类器的提高有积极的作用。