Fairness-Aware Structured Pruning in Transformers
Abdelrahman Zayed Goncalo Mordido Samira Shabanian Ioana Baldini Sarath Chandar
The increasing size of large language models (LLMs) has introduced challenges
in their training and inference. Removing model components is perceived as a
solution to tackle the large model sizes, however, existing pruning methods
solely focus on performance, without considering an essential aspect for the
responsible use of LLMs: model fairness. It is crucial to address the fairness
of LLMs towards diverse groups, such as women, Black people, LGBTQ+, Jewish
communities, among others, as they are being deployed and available to a wide
audience. In this work, first, we investigate how attention heads impact
fairness and performance in pre-trained transformer-based language models. We
then propose a novel method to prune the attention heads that negatively impact
fairness while retaining the heads critical for performance, i.e. language
modeling capabilities. Our approach is practical in terms of time and
resources, as it does not require fine-tuning the final pruned, and fairer,
model. Our findings demonstrate a reduction in gender bias by 19%, 19.5%,
39.5%, 34.7%, 23%, and 8% for DistilGPT-2, GPT-2, GPT-Neo of two different
sizes, GPT-J, and Llama 2 models, respectively, in comparison to the biased
model, with only a slight decrease in performance.