Abstract: There is no simple definition of sexism, butit can be described as prejudice, stereotyping,or discrimination, especially against women,based on their gender. In online interactions,sexism is common. One out of ten Americanadults says that they have been harassed be-cause of their gender and have been the targetof sexism, so sexism is a growing issue. TheExplainable Detection of Online Sexism sharedtask in SemEval-2023 aims at building sexismdetection systems for the English language. Inorder to address the problem, we use largelanguage models such as RoBERTa and De-BERTa. In addition, we present Random LayerAdversarial Training (RLAT) for transformers,and show its significant impact on solving allsubtasks. Moreover, we use virtual adversar-ial training and contrastive learning to improveperformance on subtask A. Upon completionof subtask A, B, and C test sets, we obtainedmacro-F1 of 84.45, 67.78, and 52.52, respec-tively outperforming proposed baselines on allsubtasks. Our code is publicly available onGithub.