Alignment-Aware Quantization for LLM Safety

• URL: http://arxiv.org/abs/2511.07842v1
• Date: Wed, 12 Nov 2025 01:23:43 GMT
• Title: Alignment-Aware Quantization for LLM Safety
• Abstract summary: Safety and efficiency are important factors when deploying large language models (LLMs)<n>We propose Alignment-Aware Quantization(AAQ), a novel approach that integrates Alignment-Preserving Contrastive( APC) loss into the PTQ pipeline.<n>AAQ is compatible with standard PTQ techniques and enables robust 4-bit (W4A4) quantization across diverse model families.
• Score: 30.635936212381726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Safety and efficiency are both important factors when deploying large language models(LLMs). LLMs are trained to follow human alignment for safety, and post training quantization(PTQ) is applied afterward for efficiency. However, these two objectives are often in conflict, revealing a fundamental flaw in the conventional PTQ paradigm: quantization can turn into a safety vulnerability if it only aims to achieve low perplexity. Models can demonstrate low perplexity yet exhibit significant degradation in alignment with the safety policy, highlighting that perplexity alone is an insufficient and often misleading proxy for model safety. To address this, we propose Alignment-Aware Quantization(AAQ), a novel approach that integrates Alignment-Preserving Contrastive(APC) loss into the PTQ pipeline. Compared to simple reconstruction loss, ours explicitly preserves alignment by encouraging the quantized model to mimic its safe, instruction-tuned model while diverging from the unaligned, pre-trained counterpart. Our method achieves this robust safety alignment without resorting to specialized safety-focused calibration datasets, highlighting its practical utility and broad applicability. AAQ is compatible with standard PTQ techniques and enables robust 4-bit (W4A4) quantization across diverse model families such as LLaMA, Qwen, and Mistral while maintaining safety where previous methods fail. Our work resolves the critical trade-off between efficiency and safety, paving the way toward LLMs that are both efficient and trustworthy. Anonymized code is available in the supplementary material.

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