  Deep learning models are susceptible to adversarial examples that have
imperceptible perturbations in the original input, resulting in adversarial
attacks against these models. Analysis of these attacks on the state of the art
transformers in NLP can help improve the robustness of these models against
such adversarial inputs. In this paper, we present Adv-OLM, a black-box attack
method that adapts the idea of Occlusion and Language Models (OLM) to the
current state of the art attack methods. OLM is used to rank words of a
sentence, which are later substituted using word replacement strategies. We
experimentally show that our approach outperforms other attack methods for
several text classification tasks.
