Collapsed Language Models Promote Fairness

Abstract: To mitigate societal biases implicitly encoded in recent successful pretrained LLMs, a diverse array of approaches have been proposed to encourage model fairness, focusing on prompting, data augmentation, regularized fine-tuning, and more. Despite the development, it is nontrivial to reach a principled understanding of fairness and an effective algorithm that can consistently debias LLMs. In this work, by rigorous evaluations of Neural Collapse -- a learning phenomenon happen in last-layer representations and classifiers in deep networks -- on fairness-related words, we find that debiased LLMs exhibit collapsed alignment between token representations and word embeddings. More importantly, this observation inspires us to design a principled fine-tuning method that can effectively improve fairness in a wide range of debiasing methods, while still preserving the performance of LLMs on standard natural language understanding tasks. We attach our code at https://github.com/Xujxyang/Fairness-NC-main.