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Large Language Models (LLMs) are trained primarily on minimally processed webtext, which exhibits the same wide range of social biases held by the humanswho created that content. Consequently, text generated by LLMs caninadvertently perpetuate stereotypes towards marginalized groups, like theLGBTQIA+ community. In this paper, we perform a comparative study of how LLMsgenerate text describing people with different sexual identities. Analyzingbias in the text generated by an LLM using regard score shows measurable biasagainst queer people. We then show that a post-hoc method based onchain-of-thought prompting using SHAP analysis can increase the regard of thesentence, representing a promising approach towards debiasing the output ofLLMs in this setting.

Queer People are People First: Deconstructing Sexual Identity Stereotypes in Large Language Models