BERT Probe : A python package for probing attention based robustness evaluation of BERT models

Abstract

Transformer models based on attention-based architectures have been significantly successful in establishing state-of-the-art results in natural language processing (NLP). However, recent work about adversarial robustness of attention-based models show that their robustness is susceptible to adversarial inputs causing spurious outputs thereby raising questions about trustworthiness of such models. In this paper, we present BERT Probe which is a python-based package for evaluating robustness to attention attribution based on character-level and word-level evasion attacks and empirically quantifying potential vulnerabilities for sequence classification tasks. Additionally, BERT Probe also provides two out-of-the-box defenses against character-level attention attribution-based evasion attacks.