  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.
