C.6 Limitations
Many axes of potential harms and biases exist beyond what we measure here, and limitation to evaluations
discussed in Chowdhery et al. (2022) are relevant in this work.
Automated measures of toxic language contain several forms of noise and bias (Xu et al., 2021; Garg et al.,
2022), and do not consider diverse perspectives on toxic language (Goyal et al., 2022; Sap et al., 2021).
Additionally, we highlight that improvements in this work related to toxic language and identity groups
remain limited to a biased subset of identity terms and in English only (Smith et al., 2022; Bhatt et al., 2022;
Dev et al., 2021b).
When evaluating toxicity classiﬁcation, we note additional limitations in aggregated metrics (Borkan et al.,
2019a), and challenges with evaluation given diverse perspectives on annotation (Goyal et al., 2022; Sap et al.,
2021). Further, classiﬁers are more commonly built with ﬁne-tuning methods or other parameter-eﬃcient
methods, which we do not evaluate here.
Scaling Instruction-Finetuned Language Models