Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models

Anonymous

Paper Link: https://openreview.net/forum?id=kukDxTtVqQf

Paper Type: Long paper (up to eight pages of content + unlimited references and appendices)

Abstract: Recently, NLP models have achieved remarkable progress across a variety of tasks; however, they have also been criticized for being not robust. Many robustness problems can be attributed to models exploiting "spurious correlations", or "shortcuts" between the training data and the task labels. Most existing work identifies a limited set of task-specific shortcuts via human priors or error analyses, which requires extensive expertise and efforts. In this paper, we aim to automatically identify such spurious correlations in NLP models at scale. We first leverage existing interpretability methods to extract tokens that significantly affect model's decision process from the input text. We then distinguish "genuine" tokens and "spurious" tokens by analyzing model predictions across multiple corpora and further verify them through knowledge-aware perturbations. We show that our proposed method can effectively and efficiently identify a scalable set of ``shortcuts'', and mitigating these leads to more robust models in multiple applications.

Presentation Mode: This paper will be presented in person in Seattle

Copyright Consent Signature (type Name Or NA If Not Transferrable): Tianlu Wang

Copyright Consent Name And Address: Tianlu Wang, 1 Hacker Way, Menlo Park, CA 94025