Large Language Models (LLMs) have revolutionized natural language processing tasks with remarkable success. However, their formidable size and computational demands present significant challenges for practical deployment, especially in resource-constrained environments. As these challenges become increasingly pertinent, the field of model compression has emerged as a pivotal research area to alleviate these limitations. This paper presents a comprehensive survey that navigates the landscape of model compression techniques tailored specifically for LLMs. Addressing the imperative need for efficient deployment, we delve into various methodologies, encompassing quantization, pruning, knowledge distillation, and more. Within each of these techniques, we highlight recent advancements and innovative approaches that contribute to the evolving landscape of LLM research. Furthermore, we explore benchmarking strategies and evaluation metrics that are essential for assessing the effectiveness of compressed LLMs. By providing insights into the latest developments and practical implications, this survey serves as an invaluable resource for both researchers and practitioners. As LLMs continue to evolve, this survey aims to facilitate enhanced efficiency and real-world applicability, establishing a foundation for future advancements in the field.

翻译：大型语言模型在自然语言处理任务中取得了显著成功，彻底改变了该领域的发展格局。然而，其庞大的规模与计算需求给实际部署带来了重大挑战，尤其在资源受限环境中尤为突出。随着这些挑战日益凸显，模型压缩领域已成为缓解上述局限性的关键研究方向。本文系统梳理了专门针对大型语言模型的模型压缩技术全景，针对高效部署的迫切需求，深入探讨了量化、剪枝、知识蒸馏等多种方法论，并重点阐述了各项技术中的最新进展与创新性方法，这些成果持续推动大型语言模型研究领域的发展演进。此外，本文还探索了评估压缩后大型语言模型有效性的基准测试策略与评价指标。通过深入剖析最新发展动态与实践启示，本综述为研究人员与从业者提供了宝贵资源。随着大型语言模型的持续演进，本文旨在促进其效率提升与现实应用可行性，为该领域的未来进展奠定基础。