Safety Arithmetic: A Framework for Test-time Safety Alignment of
  Language Models by Steering Parameters and Activations
Authors: name: Rima Hazra, name: Sayan Layek, name: Somnath Banerjee, name: Soujanya Poria

Ensuring the safe alignment of large language models (LLMs) with human values
is critical as they become integral to applications like translation and
question answering. Current alignment methods struggle with dynamic user
intentions and complex objectives, making models vulnerable to generating
harmful content. We propose Safety Arithmetic, a training-free framework
enhancing LLM safety across different scenarios: Base models, Supervised
fine-tuned models (SFT), and Edited models. Safety Arithmetic involves Harm
Direction Removal to avoid harmful content and Safety Alignment to promote safe
responses. Additionally, we present NoIntentEdit, a dataset highlighting edit
instances that could compromise model safety if used unintentionally. Our
experiments show that Safety Arithmetic significantly improves safety measures,
reduces over-safety, and maintains model utility, outperforming existing
methods in ensuring safe content generation.