Sorting through the noise: Testing robustness of information processing in pre-trained language models

Understand

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.