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š„ š„ Releasing our new paper on AI safety alignment -- Safety Arithmetic: A Framework for Test-time Safety Alignment of Language Models by Steering Parameters and Activations šÆ with Sayan Layek, Somnath Banerjee and Soujanya Poria.
š 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 (HDR) to avoid harmful content and Safety Alignment to promote safe responses.
š Paper: https://arxiv.org/abs/2406.11801v1
š Code: https://github.com/declare-lab/safety-arithmetic
š 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 (HDR) to avoid harmful content and Safety Alignment to promote safe responses.
š Paper: https://arxiv.org/abs/2406.11801v1
š Code: https://github.com/declare-lab/safety-arithmetic
