  Targeted syntactic evaluations of language models ask whether models show
stable preferences for syntactically acceptable content over minimal-pair
unacceptable inputs. Most targeted syntactic evaluation datasets ask models to
make these judgements with just a single context-free sentence as input. This
does not match language models' training regime, in which input sentences are
always highly contextualized by the surrounding corpus. This mismatch raises an
important question: how robust are models' syntactic judgements in different
contexts? In this paper, we investigate the stability of language models'
performance on targeted syntactic evaluations as we vary properties of the
input context: the length of the context, the types of syntactic phenomena it
contains, and whether or not there are violations of grammaticality. We find
that model judgements are generally robust when placed in randomly sampled
linguistic contexts. However, they are substantially unstable for contexts
containing syntactic structures matching those in the critical test content.
Among all tested models (GPT-2 and five variants of OPT), we significantly
improve models' judgements by providing contexts with matching syntactic
structures, and conversely significantly worsen them using unacceptable
contexts with matching but violated syntactic structures. This effect is
amplified by the length of the context, except for unrelated inputs. We show
that these changes in model performance are not explainable by simple features
matching the context and the test inputs, such as lexical overlap and
dependency overlap. This sensitivity to highly specific syntactic features of
the context can only be explained by the models' implicit in-context learning
abilities.
