The popularity of pretrained language models in natural language processing
systems calls for a careful evaluation of such models in down-stream tasks,
which have a higher potential for societal impact. The evaluation of such
systems usually focuses on accuracy measures. Our findings in this paper call
for attention to be paid to fairness measures as well. Through the analysis of
more than a dozen pretrained language models of varying sizes on two toxic text
classification tasks (English), we demonstrate that focusing on accuracy
measures alone can lead to models with wide variation in fairness
characteristics. Specifically, we observe that fairness can vary even more than
accuracy with increasing training data size and different random
initializations. At the same time, we find that little of the fairness
variation is explained by model size, despite claims in the literature. To
improve model fairness without retraining, we show that two post-processing
methods developed for structured, tabular data can be successfully applied to a
range of pretrained language models. Warning: This paper contains samples of
offensive text.

本研究分析了不同大小的预训练语言模型在两个有毒文本分类任务上的公平性，发现仅关注准确性度量可能会导致具有广泛公平特性变化的模型。我们发现，尽管文献中有所声称，但公平变化很少与模型大小有关。为了提高模型的公平性，该研究表明可以成功地将适用于结构化表格数据的两种后处理方法应用于各种预训练语言模型中。