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。

预训练语言模型在有毒文本分类中的公平性可能存在差异

Your fairness may vary: Pretrained language model fairness in toxic text  classification

Deep transformer models have pushed performance on NLP tasks to new limits,
suggesting sophisticated treatment of complex linguistic inputs, such as
phrases. However, we have limited understanding of how these models handle
representation of phrases, and whether this reflects sophisticated composition
of phrase meaning like that done by humans. In this paper, we present
systematic analysis of phrasal representations in state-of-the-art pre-trained
transformers. We use tests leveraging human judgments of phrase similarity and
meaning shift, and compare results before and after control of word overlap, to
tease apart lexical effects versus composition effects. We find that phrase
representation in these models relies heavily on word content, with little
evidence of nuanced composition. We also identify variations in phrase
representation quality across models, layers, and representation types, and
make corresponding recommendations for usage of representations from these
models.

本文系统地分析了最新预训练 transformer 模型中对词组的表征方式，并使用人类对词组相似度和语义转换的判断来比较单词重叠与组成效应对因素间的影响。结果表明这些模型对词汇内容依赖很强，且未发现有像人类那样的精细组成效应。同时，本文还发现了在不同模型、层次和表征类型之间的词组表征质量的差异，并为使用这些模型的表征提出了相应的建议。