# Quantized Large Language Models: A Systematic Study of Confidence Calibration

> This article interprets the uncertainty-aware-inference research project, deeply analyzes the impact of Post-Training Quantization (PTQ) on the confidence calibration of large language models (LLMs) of different scales, and explores the potential of knowledge distillation in restoring calibration quality.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-10T19:06:28.000Z
- 最近活动: 2026-04-10T19:15:48.102Z
- 热度: 157.8
- 关键词: 大语言模型, 模型量化, 置信度校准, 知识蒸馏, PTQ, 模型部署, AI可靠性
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-github-chuanbinp-uncertainty-aware-inference
- Canonical: https://www.zingnex.cn/forum/thread/llm-github-chuanbinp-uncertainty-aware-inference
- Markdown 来源: floors_fallback

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## [Introduction] Core Summary of Confidence Calibration Research for Quantized Large Language Models

This article interprets the uncertainty-aware-inference project, systematically analyzes the impact of Post-Training Quantization (PTQ) on the confidence calibration of large language models (LLMs) of different scales, and finds that quantization impairs calibration quality (the lower the precision, the larger the model scale, and the more obvious the impact on generation tasks). It also verifies that knowledge distillation can effectively restore part of the calibration performance, and provides practical insights such as quantization strategy selection, post-calibration processing techniques, and monitoring evaluation.

## Research Background: The Contradiction Between Quantization and LLM Reliability

Large language models have high deployment costs, and Post-Training Quantization (PTQ) technology is widely used in resource-constrained environments (compressing weights to low precision such as 8-bit or 4-bit). However, whether quantization affects model reliability—especially the confidence calibration ability (the ability of prediction probability to reflect actual correctness)—is a key issue. The uncertainty-aware-inference project conducts a systematic study on this.

## Importance of Confidence Calibration: Decision Basis for High-Risk Scenarios

Confidence calibration refers to the matching between the model's prediction confidence and actual accuracy. Poor calibration (overconfidence/underconfidence) affects decision reliability. In high-risk scenarios such as medical diagnosis (affecting doctors' adoption of AI recommendations), autonomous driving (determining the timing of human takeover), and financial risk control (affecting the rate of misjudgment and missed judgment), calibration quality is crucial.

## Research Design and Methods: Multi-Dimensional Coverage and Standard Evaluation

**Model Coverage**: Covers architectures such as LLaMA, Mistral, Falcon, parameter scales from 7B to 70B, and quantization precisions like INT8/INT4; **Evaluation Metrics**: Uses standard metrics such as ECE (Expected Calibration Error), MCE (Maximum Calibration Error), reliability diagrams, and Brier score to measure calibration quality.

## Key Findings: Negative Impact of Quantization and Recovery Effect of Distillation

**Negative Impact of Quantization**: PTQ impairs calibration quality; the lower the precision (e.g., INT4 vs. INT8), the larger the model scale, and the more obvious the impact on generation tasks (compared to classification tasks); **Recovery via Distillation**: Knowledge distillation with a full-precision model as the teacher and a quantized model as the student can significantly improve ECE and partially restore performance, but it requires a trade-off with additional computing resources.

## Practical Insights: Recommendations for Quantization Strategies and Calibration Recovery

**Quantization Strategies**: Prioritize INT8, keep key layers at high precision, mixed-precision quantization; **Post-Calibration Processing**: Temperature scaling, Platt scaling, bucket calibration; **Monitoring and Evaluation**: Regular sampling to evaluate ECE, establish a baseline for confidence distribution, analyze high-confidence wrong predictions.

## Technical Details and Limitations: Focus on PTQ and Dataset Diversity

The study only focuses on PTQ (commonly used in industry, low cost) and does not involve Quantization-Aware Training (QAT); the evaluation uses multi-task benchmark datasets (question answering, reasoning, code generation, etc.) to enhance the robustness of the conclusions.

## Future Directions and Summary: Development Path for Reliable Quantized LLMs

**Future Directions**: Dynamic quantization, calibration-aware quantization objectives, other uncertainty representations; **Summary**: This study quantifies the impact of PTQ on LLM calibration, verifies the effectiveness of distillation, provides empirical references for deployment, and helps establish more reliable quantized LLM practices.