In efforts to keep up with the rapid progress and use of large language models, gender bias research is becoming more prevalent in NLP. Non-English bias research, however, is still in its infancy with most work focusing on English. In our work, we study how grammatical gender bias relating to politeness levels manifests in Japanese and Korean language models. Linguistic studies in these languages have identified a connection between gender bias and politeness levels, however it is not yet known if language models reproduce these biases. We analyze relative prediction probabilities of the male and female grammatical genders using templates and find that informal polite speech is most indicative of the female grammatical gender, while rude and formal speech is most indicative of the male grammatical gender. Further, we find politeness levels to be an attack vector for allocational gender bias in cyberbullying detection models. Cyberbullies can evade detection through simple techniques abusing politeness levels. We introduce an attack dataset to (i) identify representational gender bias across politeness levels, (ii) demonstrate how gender biases can be abused to bypass cyberbullying detection models and (iii) show that allocational biases can be mitigated via training on our proposed dataset. Through our findings we highlight the importance of bias research moving beyond its current English-centrism.

翻译：为紧跟大语言模型的快速发展与应用进程，性别偏见研究在自然语言处理领域日益普及。然而，非英语语种的偏见研究仍处于起步阶段，多数工作集中于英语。本研究探讨与礼貌程度相关的语法性别偏见如何体现于日语和韩语语言模型中。语言学研究表明，这两种语言中的性别偏见与礼貌程度存在关联，但尚未明确语言模型是否复现此类偏见。我们通过模板分析男性和女性语法性别的相对预测概率，发现非正式礼貌用语最显著指代女性语法性别，而粗鲁与正式用语最显著指代男性语法性别。此外，我们发现礼貌程度可作为网络霸凌检测模型中分配性性别偏见的攻击向量：攻击者通过滥用礼貌程度的简单技术即可逃避检测。我们提出一个攻击数据集，用于：(i) 识别不同礼貌程度下的表征性性别偏见；(ii) 展示如何利用性别偏见绕过网络霸凌检测模型；(iii) 证明通过所提数据集训练可缓解分配性偏见。本研究结果凸显了将偏见研究拓展至当前英语中心主义之外的重要性。