  Animacy - whether an entity is alive and sentient - is fundamental to
cognitive processing, impacting areas such as memory, vision, and language.
However, animacy is not always expressed directly in language: in English it
often manifests indirectly, in the form of selectional constraints on verbs and
adjectives. This poses a potential issue for transformer language models (LMs):
they often train only on text, and thus lack access to extralinguistic
information from which humans learn about animacy. We ask: how does this impact
LMs' animacy processing - do they still behave as humans do? We answer this
question using open-source LMs. Like previous studies, we find that LMs behave
much like humans when presented with entities whose animacy is typical.
However, we also show that even when presented with stories about atypically
animate entities, such as a peanut in love, LMs adapt: they treat these
entities as animate, though they do not adapt as well as humans. Even when the
context indicating atypical animacy is very short, LMs pick up on subtle clues
and change their behavior. We conclude that despite the limited signal through
which LMs can learn about animacy, they are indeed sensitive to the relevant
lexical semantic nuances available in English.
