  Sophisticated language models such as OpenAI's GPT-3 can generate hateful
text that targets marginalized groups. Given this capacity, we are interested
in whether large language models can be used to identify hate speech and
classify text as sexist or racist? We use GPT-3 to identify sexist and racist
text passages with zero-, one-, and few-shot learning. We find that with zero-
and one-shot learning, GPT-3 is able to identify sexist or racist text with an
accuracy between 48 per cent and 69 per cent. With few-shot learning and an
instruction included in the prompt, the model's accuracy can be as high as 78
per cent. We conclude that large language models have a role to play in hate
speech detection, and that with further development language models could be
used to counter hate speech and even self-police.
