Detecting Hate Speech with GPT-3

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 can identify sexist or racist text with an accuracy between 57 per cent and 68 per cent depending on the category of text and type of learning. With few-shot learning, the model's accuracy can be as high as 88 per cent. Large language models have a role to play in hate speech detection, and with further development could eventually be used to counter hate speech.