Despite their impressive performance in a wide range of NLP tasks, Large
Language Models (LLMs) have been reported to encode worrying-levels of gender
bias. Prior work has proposed debiasing methods that require human labelled
examples, data augmentation and fine-tuning of the LLMs, which are
computationally costly. Moreover, one might not even have access to the
internal parameters for performing debiasing such as in the case of
commercially available LLMs such as GPT-4. To address this challenge we propose
bias suppression, a novel alternative to debiasing that does not require access
to model parameters. We show that text-based preambles, generated from manually
designed templates covering counterfactual statements, can accurately suppress
gender biases in LLMs. Moreover, we find that descriptive sentences for
occupations can further suppress gender biases. Interestingly, we find that
bias suppression has a minimal adverse effect on downstream task performance,
while effectively mitigating the gender biases.

大型语言模型（LLMs）中存在的性别偏见令人担忧，但提出了一种无需访问模型参数的新方法，即通过手动设计的文本前言和职业描述句来有效抑制性别偏见，并且对下游任务性能影响最小。