Large Language Models (LLMs) trained with self-supervision on vast corpora of web text fit to the social biases of that text. Without intervention, these social biases persist in the model's predictions in downstream tasks, leading to representational harm. Many strategies have been proposed to mitigate the effects of inappropriate social biases learned during pretraining. Simultaneously, methods for model compression have become increasingly popular to reduce the computational burden of LLMs. Despite the popularity and need for both approaches, little work has been done to explore the interplay between these two. We perform a carefully controlled study of the impact of model compression via quantization and knowledge distillation on measures of social bias in LLMs. Longer pretraining and larger models led to higher social bias, and quantization showed a regularizer effect with its best trade-off around 20% of the original pretraining time.

翻译：大型语言模型通过自监督学习在大量网络文本语料上进行训练，会拟合这些文本中的社会偏见。若不加以干预，这些社会偏见在下游任务的模型预测中持续存在，导致表征危害。目前已提出多种策略来缓解预训练过程中习得的不当社会偏见的影响。与此同时，模型压缩方法因能降低大型语言模型的计算负担而日益普及。尽管这两种方法都备受关注且具有实际需求，但鲜有研究探讨它们之间的相互作用。我们通过精心控制的研究，分析了量化和知识蒸馏这两种模型压缩技术对大型语言模型社会偏见指标的影响。更长的预训练时间和更大的模型规模会导致更高的社会偏见，而量化则表现出正则化效应，其最佳平衡点约为原始预训练时间的20%。