  Existing studies addressing gender bias of pre-trained language models,
usually build a small gender-neutral data set and conduct a second phase
pre-training on the model with such data. However, given the limited size and
concentrated focus of the gender-neutral data, catastrophic forgetting would
occur during second-phase pre-training. Forgetting information in the original
training data may damage the model's downstream performance by a large margin.
In this work, we empirically show that catastrophic forgetting occurs in such
methods by evaluating them with general NLP tasks in GLUE. Then, we propose a
new method, GEnder Equality Prompt (GEEP), to improve gender fairness of
pre-trained models with less forgetting. GEEP freezes the pre-trained model and
learns gender-related prompts with gender-neutral data. Empirical results show
that GEEP not only achieves SOTA performances on gender fairness tasks, but
also forgets less and performs better on GLUE by a large margin.
