Content Warning: This paper contains examples of misgendering and erasure that could be offensive and potentially triggering. Gender bias in language technologies has been widely studied, but research has mostly been restricted to a binary paradigm of gender. It is essential also to consider non-binary gender identities, as excluding them can cause further harm to an already marginalized group. In this paper, we comprehensively evaluate popular language models for their ability to correctly use English gender-neutral pronouns (e.g., singular they, them) and neo-pronouns (e.g., ze, xe, thon) that are used by individuals whose gender identity is not represented by binary pronouns. We introduce MISGENDERED, a framework for evaluating large language models' ability to correctly use preferred pronouns, consisting of (i) instances declaring an individual's pronoun, followed by a sentence with a missing pronoun, and (ii) an experimental setup for evaluating masked and auto-regressive language models using a unified method. When prompted out-of-the-box, language models perform poorly at correctly predicting neo-pronouns (averaging 7.7% accuracy) and gender-neutral pronouns (averaging 34.2% accuracy). This inability to generalize results from a lack of representation of non-binary pronouns in training data and memorized associations. Few-shot adaptation with explicit examples in the prompt improves performance for neo-pronouns, but only to 64.7% even with 20 shots. We release the full dataset, code, and demo at https://tamannahossainkay.github.io/misgendered/

翻译：内容警告：本文包含可能引发冒犯或不适的性别误指和抹除案例。性别偏见在语言技术领域已被广泛研究，但相关研究大多局限于二元性别范式。纳入非二元性别认同同样重要，因为排除此类群体可能对已有边缘化群体造成进一步伤害。本文系统评估了流行语言模型正确使用英语中性代词（如单数形式they、them）及新创代词（如ze、xe、thon）的能力——这些代词主要用于性别认同无法被二元代词涵盖的个体。我们提出了MISGENDERED框架，用于评估大型语言模型正确使用偏好代词的能力，包含：（i）声明个体代词的示例及后续缺省代词的句子；（ii）采用统一方法评估掩码语言模型与自回归语言模型的实验设置。在无额外提示的初始状态下，语言模型预测新创代词的准确率极低（平均7.7%），中性代词准确率也仅为34.2%。这种泛化能力的缺失源于训练数据中非二元代词表征不足及记忆关联的固化。即便在提示中加入显式示例进行少样本适配，新创代词的预测准确率在20次示例条件下也仅提升至64.7%。完整数据集、代码及演示已发布在：https://tamannahossainkay.github.io/misgendered/