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

• URL: http://arxiv.org/abs/2110.01094v1
• Date: Sun, 3 Oct 2021 20:22:54 GMT
• Title: Adversarial Examples Generation for Reducing Implicit Gender Bias in Pre-trained Models
• Authors: Wenqian Ye, Fei Xu, Yaojia Huang, Cassie Huang, Ji A
• Abstract summary: We propose a method to automatically generate implicit gender bias samples at sentence-level and a metric to measure gender bias. The metric will be used to guide the generation of examples from Pre-trained models. Therefore, those examples could be used to impose attacks on Pre-trained Models.
• Score: 2.6329024988388925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Over the last few years, Contextualized Pre-trained Neural Language Models, such as BERT, GPT, have shown significant gains in various NLP tasks. To enhance the robustness of existing pre-trained models, one way is adversarial examples generation and evaluation for conducting data augmentation or adversarial learning. In the meanwhile, gender bias embedded in the models seems to be a serious problem in practical applications. Many researches have covered the gender bias produced by word-level information(e.g. gender-stereotypical occupations), while few researchers have investigated the sentence-level cases and implicit cases. In this paper, we proposed a method to automatically generate implicit gender bias samples at sentence-level and a metric to measure gender bias. Samples generated by our method will be evaluated in terms of accuracy. The metric will be 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 reducing gender bias for future research.

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