发表机构
Northeastern University; Shandong University; LMU Munich; MCML（东北大学; 山东大学; 慕尼黑大学; 慕尼黑计算与机器学习中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对大型语言模型的高内存与高计算需求问题，开展以目标为中心的量化感知训练（QAT）综述，梳理其理论基础、方法分类、评估范式及挑战，为未来研究指明方向。
AI 中文摘要
大型语言模型（LLMs）的快速发展带来了极高的内存占用和密集的计算需求。量化感知训练（Quantization-Aware Training，QAT）技术应运而生，成为解决这些挑战的有前景的方案，它在模型训练过程中显式模拟量化效应，生成的低比特模型可达到与全精度模型相当的准确率。本研究提供以目标为中心的QAT综述，旨在阐明其理论基础和不断发展的实现格局，通过以目标为中心的分类法系统回顾现有QAT方法，综合不同目标在误差特性、数值格式和策略可迁移性方面的差异，进一步总结QAT评估范式，探讨优化与部署中的挑战，并概述未来研究的潜在方向。
英文摘要
The rapid development of LLMs incurs prohibitive memory footprints and intensive computational demands. Quantization-Aware Training (QAT) techniques have emerged as a promising solution to address these challenges by explicitly simulating quantization effects during model training, yielding low-bit models that achieve accuracy comparable to their full-precision counterparts. In this work, we provide a target-centric survey of QAT, aimed at clarifying both its theoretical foundations and its evolving implementation landscape. We systematically review existing QAT methods through a target-centric taxonomy and synthesize cross-target differences in error characteristics, numerical formats, and strategy transferability. We further summarize QAT evaluation paradigms and discuss challenges in optimization and deployment, outlining potential directions for future research.
CommentsAccepted to EMNLP 2026 (Main Conference)
