The Illusion of Equivalency: Statistical Characterization of Quantization Effects in LLMs

Post-training quantization of large language models can significantly alter their behavior, despite minimal changes in accuracy and perplexity metrics. Researchers have discovered that quantization effects are not adequately captured by traditional evaluation methods, leading to a misleading sense of equivalency between full-precision and quantized models. To address this, a new metric called correctness agreement has been introduced, which measures the overlap in correct predictions between a base model and its quantized counterpart. This decision-level metric provides a more nuanced understanding of the impact of quantization on model behavior. The findings have significant implications for the deployment of large language models in resource-constrained settings, particularly in high-stakes applications where behavioral changes can have geopolitical consequences¹. So what matters to practitioners is that the choice of evaluation metric can drastically impact the perceived performance of quantized models, underscoring the need for more comprehensive assessment methods.