Disclosure and Mitigation of Gender Bias in LLMs
Xiangjue Dong Yibo Wang Philip S. Yu James Caverlee
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.