of toxic and non-toxic samples). We report the AUC-ROC of the model using the normalized log-likelihoods
as classiﬁcation score. Given the class imbalance in the evaluation data, accuracy is not considered as a good
reference metric (Borkan et al., 2019a). We leave analysis of more detailed metrics for future work.
Table 7 shows that toxicity classiﬁcation capabilities increase signiﬁcantly with instruction ﬁnetuning, as
we observe a consistent improvement both in zero and few-shot classiﬁcation. All the instruction ﬁnetuned
models in a zero-shot classiﬁcation scenario outperform the base models, even when comparing with their
performance in the 10-shot scenario. These results can be seen as a step forward in the use of language
models for toxicity detection. However, these models are not yet on a par with systems speciﬁcally designed
for this task, such as Perspective API, which achieves an AUC on 97.0% on the same evaluation dataset.
Scaling Instruction-Finetuned Language Models