Large language models (LLMs) have shown remarkable advances in language
generation and understanding but are also prone to exhibiting harmful social
biases. While recognition of these behaviors has generated an abundance of bias
mitigation techniques, most require modifications to the training data, model
parameters, or decoding strategy, which may be infeasible without access to a
trainable model. In this work, we leverage the zero-shot capabilities of LLMs
to reduce stereotyping in a technique we introduce as zero-shot self-debiasing.
With two approaches, self-debiasing via explanation and self-debiasing via
reprompting, we show that self-debiasing can significantly reduce the degree of
stereotyping across nine different social groups while relying only on the LLM
itself and a simple prompt, with explanations correctly identifying invalid
assumptions and reprompting delivering the greatest reductions in bias. We hope
this work opens inquiry into other zero-shot techniques for bias mitigation.

本文介绍了一种零样本自我去偏见技术，通过解释和重提出两种方法，利用大型语言模型来降低刻板印象。我们展示了自我去偏见技术能够在不需要修改训练数据、模型参数或解码策略的情况下，减少九个不同社会群体的刻板印象程度，其中解释能够正确识别无效假设，重提出能够实现最大程度的偏见减少。我们希望本研究能够引发对其他零样本偏见减轻技术的探索。