Pre-trained language models learn socially harmful biases from their training
corpora, and may repeat these biases when used for generation. We study gender
biases associated with the protagonist in model-generated stories. Such biases
may be expressed either explicitly ("women can't park") or implicitly (e.g. an
unsolicited male character guides her into a parking space). We focus on
implicit biases, and use a commonsense reasoning engine to uncover them.
Specifically, we infer and analyze the protagonist's motivations, attributes,
mental states, and implications on others. Our findings regarding implicit
biases are in line with prior work that studied explicit biases, for example
showing that female characters' portrayal is centered around appearance, while
male figures' focus on intellect.

研究表明，预训练语言模型从其训练语料库中学习到具有社会伤害性的偏见，并可能在生成过程中重复这些偏见。本文研究模型生成故事中与主角相关的性别偏见，使用常识推理引擎揭示了其中的隐含偏见，包括主角的动机、属性、心态以及对他人的影响。我们的研究发现对于隐含的偏见与之前研究显性偏见的结论一致，例如女性角色的描写主要集中在外貌上，而男性角色则侧重于智力方面。