论文标题
草药：测量预训练的语言模型中的分层区域偏见
HERB: Measuring Hierarchical Regional Bias in Pre-trained Language Models
论文作者
论文摘要
公平性已成为自然语言处理（NLP）中的一个趋势话题，该话题解决了针对某些社会群体（例如性别和宗教）的偏见。但是，语言模型（LMS）中的区域偏见仍然是一个长期存在的全球歧视问题，仍然没有探索。本文通过分析了NLP任务中广泛使用的预训练语言模型所学的区域偏见来弥合差距。除了验证LMS中区域偏见的存在外，我们发现区域组的偏见可能会受到群体的地理聚类的强烈影响。因此，我们提出了一种分层区域偏见评估方法（Herb），该方法利用子区域群集的信息来量化预训练的LMS中的偏差。实验表明，我们的层次度量可以有效地评估有关综合主题的区域偏见，并衡量可以传播到下游任务的潜在区域偏见。我们的代码可在https://github.com/bernard-yang/herb上找到。
Fairness has become a trending topic in natural language processing (NLP), which addresses biases targeting certain social groups such as genders and religions. However, regional bias in language models (LMs), a long-standing global discrimination problem, still remains unexplored. This paper bridges the gap by analysing the regional bias learned by the pre-trained language models that are broadly used in NLP tasks. In addition to verifying the existence of regional bias in LMs, we find that the biases on regional groups can be strongly influenced by the geographical clustering of the groups. We accordingly propose a HiErarchical Regional Bias evaluation method (HERB) utilising the information from the sub-region clusters to quantify the bias in pre-trained LMs. Experiments show that our hierarchical metric can effectively evaluate the regional bias with respect to comprehensive topics and measure the potential regional bias that can be propagated to downstream tasks. Our codes are available at https://github.com/Bernard-Yang/HERB.
