  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.
