With the impressive performance in various downstream tasks, large language
models (LLMs) have been widely integrated into production pipelines, like
recruitment and recommendation systems. A known issue of models trained on
natural language data is the presence of human biases, which can impact the
fairness of the system. This paper investigates LLMs' behavior with respect to
gender stereotypes, in the context of occupation decision making. Our framework
is designed to investigate and quantify the presence of gender stereotypes in
LLMs' behavior via multi-round question answering. Inspired by prior works, we
construct a dataset by leveraging a standard occupation classification
knowledge base released by authoritative agencies. We tested three LLMs
(RoBERTa-large, GPT-3.5-turbo, and Llama2-70b-chat) and found that all models
exhibit gender stereotypes analogous to human biases, but with different
preferences. The distinct preferences of GPT-3.5-turbo and Llama2-70b-chat may
imply the current alignment methods are insufficient for debiasing and could
introduce new biases contradicting the traditional gender stereotypes.

本论文研究了大型语言模型在职业决策过程中与性别刻板印象相关的行为，通过多轮问答的方式，探究和量化了性别刻板印象在语言模型行为中的存在，并测试了三种模型的偏好。发现所有模型都存在类似于人类偏见的性别刻板印象，但偏好不同，且 GPT-3.5-turbo 和 Llama2-70b-chat 的不同偏好可能意味着当前的对齐方法对消除偏见不足够，甚至可能引入与传统性别刻板印象相矛盾的新偏见。