Large Language Models (LLMs) can generate biased responses. Yet previous
direct probing techniques contain either gender mentions or predefined gender
stereotypes, which are challenging to comprehensively collect. Hence, we
propose an indirect probing framework based on conditional generation. This
approach aims to induce LLMs to disclose their gender bias even without
explicit gender or stereotype mentions. We explore three distinct strategies to
disclose explicit and implicit gender bias in LLMs. Our experiments demonstrate
that all tested LLMs exhibit explicit and/or implicit gender bias, even when
gender stereotypes are not present in the inputs. In addition, an increased
model size or model alignment amplifies bias in most cases. Furthermore, we
investigate three methods to mitigate bias in LLMs via Hyperparameter Tuning,
Instruction Guiding, and Debias Tuning. Remarkably, these methods prove
effective even in the absence of explicit genders or stereotypes.

大语言模型可以生成具有偏见的回答。然而，先前的直接探测技术包含性别提及或预定义的性别刻板印象，这些很难全面收集。因此，我们提出了一种基于条件生成的间接探测框架，旨在诱使大语言模型揭示其性别偏见，即使没有显式的性别或刻板印象提及。我们探索了三种不同的策略来揭示大语言模型中的显式和隐式性别偏见。我们的实验证明，所有经过测试的大语言模型都表现出显式和 / 或隐式的性别偏见，即使输入中没有性别刻板印象。此外，模型大小的增加或模型对齐会放大大多数情况下的偏见。此外，我们通过超参数调整、指导性指导和去偏调整这三种方法来研究大语言模型中的偏见缓解。值得注意的是，即使没有显式的性别或刻板印象，这些方法也被证明是有效的。