Improving Quantized Model Performance in Qualitative Analysis with Multi-Pass Prompt Verification

Quantization is a powerful strategy to build capable and resource-efficient large language models (LLMs) by reducing the bitwidth of the parameters. While quantized LLMs achieve state-of-the-art performance on unperturbed inputs using standard predictive metrics, their performance on perturbed inputs, measured using reliability metrics, remains underexplored, despite its importance for reliable deployment. To address this gap, we first conduct a comprehensive reliability evaluation of quantized LLMs consisting of three key components: (1) Uncertainty: We assess the trustworthiness of LLMs quantized to 2, 3, 4, and 8 bits using six different quantization methods, employing established uncertainty metrics. (2) Calibration: We assess how well-calibrated the uncertainty estimates of quantized models are across model scales and bit precisions. (3) Robustness: We design character-level and word-level input perturbations to evaluate the reliability of quantized models under semantically-preserving variations in the inputs that arise in real-world applications. Second, we characterize how reliability scales with the total number of model bits. Our study reveals that while the performance scales monotonically with the total number of bits, the reliability scalings are nonlinear. A reliability peak occurs for 4-bit quantized models, indicating that quantizing moderately sized models offers the best reliability-efficiency trade-off. Additionally, our empirical findings reveal that quantization enhances the robustness of LLMs to natural input perturbations.

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.

For large language models (LLMs), post-training quantization (PTQ) significantly reduces memory and computational overhead. Despite rapid advancements, existing papers often lack experiments on the same ground. In addition, since quantization solutions typically contain multiple components, it is important to analyze the connections among numerous existing methods to gain an in-depth understanding of this field. To bridge these gaps, we conduct an up-to-date and extensive review of the state-of-the-art methods and perform comprehensive evaluations of low-bit quantization on representative text-only LLMs on the same ground to ensure fair comparisons. To our knowledge, this fair and extensive investigation remains critically important yet significantly underexplored in the research community. To better understand the connections, we first decouple published quantization into two steps: pre-quantization transformation to reduce outliers, and quantization error mitigation to offset performance loss. Second, we extensively evaluate the impact of quantization settings, including granularity and symmetry. Third, we analyze the performance of the latest MXFP4 and NVFP4 data formats. Our experimental results first demonstrate that optimizing rotation and scaling yields the best pre-quantization transformation, while combining low-rank compensation with GPTQ occasionally outperforms using GPTQ alone. Second, we verify that finer granularity improves performance at the cost of storage and asymmetric quantization benefits activations significantly more than weights. Third, we find that the format and precision of scaling factors have a significant impact on performance; notably, rotation-based transformation is effective for INT4 but demonstrates little improvement for MXFP4 and NVFP4, inspiring further investigation in this field.