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A curated collection of research papers on Large Language Models (LLMs), transformers, prompting, fine-tuning, and multi-modal systems. Maintained by Sean Moran.

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Improving Gender Fairness Of Pre-trained Language Models Without Catastrophic Forgetting

Zahra Fatemi, Chen Xing, Wenhao Liu, Caiming Xiong. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) 2021 – 15 citations

[Paper]    
Ethics and Bias Fairness Bias Mitigation Prompting Pre-Training Training Techniques

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.

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