  Pre-trained LMs have shown impressive performance on downstream NLP tasks,
but we have yet to establish a clear understanding of their sophistication when
it comes to processing, retaining, and applying information presented in their
input. In this paper we tackle a component of this question by examining
robustness of models' ability to deploy relevant context information in the
face of distracting content. We present models with cloze tasks requiring use
of critical context information, and introduce distracting content to test how
robustly the models retain and use that critical information for prediction. We
also systematically manipulate the nature of these distractors, to shed light
on dynamics of models' use of contextual cues. We find that although models
appear in simple contexts to make predictions based on understanding and
applying relevant facts from prior context, the presence of distracting but
irrelevant content has clear impact in confusing model predictions. In
particular, models appear particularly susceptible to factors of semantic
similarity and word position. The findings are consistent with the conclusion
that LM predictions are driven in large part by superficial contextual cues,
rather than by robust representations of context meaning.
