论文标题
GPT-2中的隐性因果关系：案例研究
Implicit causality in GPT-2: a case study
论文作者
论文摘要
该案例研究调查了语言模型（GPT-2）能够在句子完成任务中捕获母语人士对隐性因果关系的直觉。我们首先重现了早期的结果（显示与主体或对象一致的代词的较低的惊喜值，具体取决于哪一个对应于动词的隐式因果关系偏见），然后检查性别和动词频率对模型性能的影响。我们的第二项研究研究了GPT-2的推理能力：模型是否能够为主题的因果关系偏见而产生更明智的动机？我们还开发了一种方法，以避免人类评估者受到模型产生的淫秽和疏忽的偏见。
This case study investigates the extent to which a language model (GPT-2) is able to capture native speakers' intuitions about implicit causality in a sentence completion task. We first reproduce earlier results (showing lower surprisal values for pronouns that are congruent with either the subject or object, depending on which one corresponds to the implicit causality bias of the verb), and then examine the effects of gender and verb frequency on model performance. Our second study examines the reasoning ability of GPT-2: is the model able to produce more sensible motivations for why the subject VERBed the object if the verbs have stronger causality biases? We also developed a methodology to avoid human raters being biased by obscenities and disfluencies generated by the model.
