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.

翻译：大语言模型（LLMs）主要基于经过最低限度处理的网络文本进行训练，这些文本展现了与创建这些内容的人类所持有的同样广泛的社会偏见。因此，LLMs生成的文本可能无意中强化对边缘化群体（如LGBTQIA+群体）的刻板印象。本文通过比较研究，探究了LLMs如何描述具有不同性身份的人物。使用关注度评分分析LLMs生成文本中的偏见，结果显示对性少数群体存在可测量的偏见。我们进一步证明，一种基于链式思维提示的后期处理方法，结合SHAP分析，能够提升句子的关注度，为在这种场景下消除LLMs输出偏差提供了有前景的方向。