Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels

This study investigates the impact of post-training quantization on Large Language Models (LLMs) quality, revealing that compression can lead to bias emergence. 3-bit quantization caused 6-21% of previously unbiased items to develop new stereotypical behaviors in models like Qwen2.5-7B, Mistral-7B, and Phi-3.5-mini. This follows a clear dose-response pattern across various precision levels.