  Aligned language models face a significant limitation as their fine-tuning
often results in compromised safety. To tackle this, we propose a simple method
RESTA that performs LLM safety realignment. RESTA stands for REstoring Safety
through Task Arithmetic. At its core, it involves a simple arithmetic addition
of a safety vector to the weights of the compromised model. We demonstrate the
effectiveness of RESTA in both parameter-efficient and full fine-tuning,
covering a wide range of downstream tasks, including instruction following in
Chinese, English, and Hindi, as well as problem-solving capabilities in Code
and Math. We also showcase the generalizability of RESTA on three existing
safety evaluation benchmarks and a multilingual benchmark dataset proposed as a
part of this work, consisting of 550 harmful questions covering 11 categories,
each with 5 sub-categories of harm. Overall, RESTA decreases the harmfulness of
the compromised model from 18.6% to 5.1% and from 9.2% to 1.5% in
parameter-efficient and full fine-tuning, respectively, while maintaining most
of the model's performance on the task. We release the source codes at:
https://github.com/declare-lab/resta.
