  Some languages allow arguments to be omitted in certain contexts. Yet human
language comprehenders reliably infer the intended referents of these zero
pronouns, in part because they construct expectations about which referents are
more likely. We ask whether Neural Language Models also extract the same
expectations. We test whether 12 contemporary language models display
expectations that reflect human behavior when exposed to sentences with zero
pronouns from five behavioral experiments conducted in Italian by Carminati
(2005). We find that three models - XGLM 2.9B, 4.5B, and 7.5B - capture the
human behavior from all the experiments, with others successfully modeling some
of the results. This result suggests that human expectations about coreference
can be derived from exposure to language, and also indicates features of
language models that allow them to better reflect human behavior.
