The Joint Effect of Quantization and Sampling Temperature on LLM Safety Alignment: A Factorial Analysis
arXiv:2606.29581v1 Announce Type: new Abstract: Modern LLM deployments routinely compress models and raise sampling temperature to reduce cost, latency, or repetition, yet safety evaluations usually treat these choices as fixed implementation details. This leaves a practical uncertainty: does a model that is safe at FP16 and greedy decoding remain safe after it is quantized and sampled stochastically, or do the two deployment knobs amplify one another? We study this question with a factorial evaluation of 9 instruction-tuned models from six families, 3 precisions (FP16, GPTQ INT8, AWQ INT4), a
The proliferation of LLMs in diverse deployment scenarios necessitates understanding the joint effects of optimization techniques on their safety, which is becoming a critical research area.
Ensuring the safety alignment of Large Language Models (LLMs) is paramount as they are deployed across various applications, especially when optimized for cost and latency.
This research provides a framework for evaluating LLM safety under common deployment optimizations, highlighting potential interaction effects not previously systematically studied.
- · AI Safety Researchers
- · LLM Deployers
- · Quantization Algorithm Developers
- · LLM Developers (if their models fail safety under quantization)
Systematic evaluation of quantized and temperature-sampled LLMs will inform best practices for safe deployment.
Improved safety understanding could lead to new, safety-aware quantization and sampling techniques.
Safer and more cost-effective LLM deployments could accelerate widespread adoption in sensitive applications.
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Read at arXiv cs.LG