Children acquiring English make systematic errors on subject control
sentences even after they have reached near-adult competence (C. Chomsky,
1969), possibly due to heuristics based on semantic roles (Maratsos, 1974).
Given the advanced fluency of large generative language models, we ask whether
model outputs are consistent with these heuristics, and to what degree
different models are consistent with each other. We find that models can be
categorized by behavior into three separate groups, with broad differences
between the groups. The outputs of models in the largest group are consistent
with positional heuristics that succeed on subject control but fail on object
control. This result is surprising, given that object control is orders of
magnitude more frequent in the text data used to train such models. We examine
to what degree the models are sensitive to prompting with agent-patient
information, finding that raising the salience of agent and patient relations
results in significant changes in the outputs of most models. Based on this
observation, we leverage an existing dataset of semantic proto-role annotations
(White, et al. 2020) to explore the connections between control and labeling
event participants with properties typically associated with agents and
patients.

大规模生成语言模型的输出结果与语义启发式算法一致，但其中最大的一组模型在主语控制方面表现优异，而在宾语控制方面则表现不佳；通过使用代理 - 患者信息，可以显著地改变大多数模型的输出结果，我们进一步利用语义原型角色注释探讨了控制与事件参与者标签属性之间的联系。