As modern Large Language Models (LLMs) shatter many state-of-the-art
benchmarks in a variety of domains, this paper investigates their behavior in
the domains of ethics and fairness, focusing on protected group bias. We
conduct a two-part study: first, we solicit sentence continuations describing
the occupations of individuals from different protected groups, including
gender, sexuality, religion, and race. Second, we have the model generate
stories about individuals who hold different types of occupations. We collect
>10k sentence completions made by a publicly available LLM, which we subject to
human annotation. We find bias across minoritized groups, but in particular in
the domains of gender and sexuality, as well as Western bias, in model
generations. The model not only reflects societal biases, but appears to
amplify them. The model is additionally overly cautious in replies to queries
relating to minoritized groups, providing responses that strongly emphasize
diversity and equity to an extent that other group characteristics are
overshadowed. This suggests that artificially constraining potentially harmful
outputs may itself lead to harm, and should be applied in a careful and
controlled manner.

在伦理和公平的领域中，现代大型语言模型 (Large Language Models，LLMs) 在破解很多最先进的基准测试中表现出色，该研究主要探讨 LLMs 在受保护群体偏见方面的行为，发现 LLMs 存在对性别、性取向和西方文化的偏见，并且模型不仅反映了社会偏见，而且似乎放大了这些偏见。尽管该模型对涉及受保护群体的问题过于谨慎，强调多样性和公平，但这种人工限制潜在有害输出的做法本身可能会造成伤害，应该谨慎和有控制地应用。