  Large Language Models (LLMs) are trained primarily on minimally processed web
text, which exhibits the same wide range of social biases held by the humans
who created that content. Consequently, text generated by LLMs can
inadvertently perpetuate stereotypes towards marginalized groups, like the
LGBTQIA+ community. In this paper, we perform a comparative study of how LLMs
generate text describing people with different sexual identities. Analyzing
bias in the text generated by an LLM using regard score shows measurable bias
against queer people. We then show that a post-hoc method based on
chain-of-thought prompting using SHAP analysis can increase the regard of the
sentence, representing a promising approach towards debiasing the output of
LLMs in this setting.
