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Over the last few years, Contextualized Pre-trained Neural Language Models,such as BERT, GPT, have shown significant gains in various NLP tasks. Toenhance the robustness of existing pre-trained models, one way is adversarialexamples generation and evaluation for conducting data augmentation oradversarial learning. In the meanwhile, gender bias embedded in the modelsseems to be a serious problem in practical applications. Many researches havecovered the gender bias produced by word-level information(e.g.gender-stereotypical occupations), while few researchers have investigated thesentence-level cases and implicit cases.  In this paper, we proposed a method to automatically generate implicit genderbias samples at sentence-level and a metric to measure gender bias. Samplesgenerated by our method will be evaluated in terms of accuracy. The metric willbe used to guide the generation of examples from Pre-trained models. Therefore,those examples could be used to impose attacks on Pre-trained Models. Finally,we discussed the evaluation efficacy of our generated examples on reducinggender bias for future research.

Adversarial Examples Generation for Reducing Implicit Gender Bias in Pre-trained Models