Safety Arithmetic: A Framework for Test-time Safety Alignment of
  Language Models by Steering Parameters and Activations
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.
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Rima Hazra
admin_assigned
2024-06-19T21:10:27.967Z
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RIMA HAZRA
user
rimahazra
667321b1a0ea9fb0d7be1b06
Sayan Layek
claimed_verified
2024-06-21T07:06:59.440Z
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Sayan Layek
user
caprion
667321b1a0ea9fb0d7be1b07
Somnath Banerjee
claimed_verified
2024-06-20T12:42:51.287Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1650829776213-62659abbe25d2b490db94d35.jpeg
Somnath Banerjee
user
leowin
667321b1a0ea9fb0d7be1b08
Soujanya Poria
admin_assigned
2024-06-19T21:10:33.255Z
626b626405fe1cb65725aca1
https://cdn-avatars.huggingface.co/v1/production/uploads/626b626405fe1cb65725aca1/ZVSbhynzpQhVGq9kGywW6.png
Soujanya Poria
user
soujanyaporia
Safety Arithmetic: A Framework for Test-time Safety Alignment of
  Language Models by Steering Parameters and Activations
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.
Safety Arithmetic is a training-free framework that enhances the safety of large language models by removing harmful content and aligning with safe responses across various scenarios.
LLMs Safety Arithmetic Harm Direction Removal Safety Alignment NoIntentEdit over-safety