Large Language models (LLMs), while powerful, exhibit harmful social biases. Debiasing is often challenging due to computational costs, data constraints, and potential degradation of multi-task language capabilities. This work introduces a novel approach utilizing ChatGPT to generate synthetic training data, aiming to enhance the debiasing of LLMs. We propose two strategies: Targeted Prompting, which provides effective debiasing for known biases but necessitates prior specification of bias in question; and General Prompting, which, while slightly less effective, offers debiasing across various categories. We leverage resource-efficient LLM debiasing using adapter tuning and compare the effectiveness of our synthetic data to existing debiasing datasets. Our results reveal that: (1) ChatGPT can efficiently produce high-quality training data for debiasing other LLMs; (2) data produced via our approach surpasses existing datasets in debiasing performance while also preserving internal knowledge of a pre-trained LLM; and (3) synthetic data exhibits generalizability across categories, effectively mitigating various biases, including intersectional ones. These findings underscore the potential of synthetic data in advancing the fairness of LLMs with minimal retraining cost.

翻译：大语言模型虽功能强大，却表现出有害的社会偏见。由于计算成本、数据限制以及多任务语言能力可能退化等因素，去偏见工作常面临挑战。本研究提出一种创新方法，利用ChatGPT生成合成训练数据以增强大语言模型的去偏见效果。我们提出两种策略：定向提示法可针对已知偏见实现有效去偏，但需预先指定所涉及的偏见类型；通用提示法虽效果稍逊，却能实现跨类别的去偏见。我们通过适配器微调实现资源高效的大语言模型去偏见，并将合成数据与现有去偏见数据集进行效果对比。结果表明：（1）ChatGPT能高效生成用于其他大语言模型去偏见的高质量训练数据；（2）本方法生成的数据在去偏见性能上超越现有数据集，同时保留预训练大语言模型的内在知识；（3）合成数据具有跨类别泛化能力，能有效缓解包括交叉性偏见在内的多种偏见。这些发现揭示了合成数据在最小化重训练成本下推动大语言模型公平性发展的潜力。