Ethos: Rectifying Language Models in Orthogonal Parameter Space

TL;DR

Ethos is a novel method that rectifies language models by identifying and negating undesired knowledge in orthogonal parameter space, effectively reducing bias, toxicity, and memorization without harming overall performance.

Contribution

Ethos introduces a new task arithmetic approach that distinguishes beneficial from undesired knowledge in language models using principal components, improving bias and toxicity mitigation.

Findings

More effective in removing bias and toxicity.

Maintains overall model performance.

Applicable to debiasing, detoxification, and memorization unlearning.

Abstract

Language models (LMs) have greatly propelled the research on natural language processing. However, LMs also raise concerns regarding the generation of biased or toxic content and the potential disclosure of private information from the training dataset. In this work, we present a new efficient approach, Ethos, that rectifies LMs to mitigate toxicity and bias in outputs and avoid privacy leakage. Ethos is built on task arithmetic. However, unlike current task arithmetic algorithms, Ethos distinguishes general beneficial and undesired knowledge when reconstructing task vectors. Specifically, Ethos first obtains a set of principal components from the pre-trained models using singular value decomposition. Then, by projecting the task vector onto principal components, Ethos identifies the principal components that encode general or undesired knowledge. Ethos performs negating using the task vector with undesired knowledge only, thereby minimizing collateral damage on general model utility. We demonstrate the efficacy of our approach on three different tasks: debiasing, detoxification, and memorization unlearning. Evaluations show Ethos is more effective in removing undesired knowledge and maintaining the overall model performance compared to current task arithmetic methods.