The Illusion of Equivalency: Statistical Characterization of Quantization Effects in LLMs
For practitioners deploying quantized LLMs, this work reveals that conventional evaluation metrics mask behavioral changes, motivating the need for behavioral evaluation beyond accuracy.
The paper shows that standard metrics like accuracy and perplexity fail to capture behavioral changes in quantized LLMs, introducing correctness agreement to measure decision-level divergence. They find behavioral divergence emerges at moderate quantization even when task performance appears preserved, with non-linear breakpoints at low bit-widths and higher sensitivity in query/key projections.
Post-training quantization is widely used to deploy large language models in resource-constrained settings, yet its evaluation relies almost exclusively on accuracy and perplexity. We show that these metrics fail to capture behavioral changes induced by quantization. We introduce correctness agreement, a decision-level metric that measures overlap in correct predictions between a base model and its quantized variants, independent of absolute accuracy. Across multiple models and quantization schemes from 8-bit to 2-bit, we find that behavioral divergence emerges under moderate quantization even when task performance appears preserved. To explain this effect, we analyze quantization as a structural operator on attention weights and quantify layer-wise distortions using statistical and distributional measures. Our results reveal non-linear breakpoints at low bit-widths and show that query and key projections are consistently more sensitive than value and output projections. These findings expose an illusion of equivalence between base and quantized models and motivate behavioral evaluation beyond conventional performance metrics.