Abstract: The rise of large-scale language models signifies a new milestone in the field of deep learning, with fine-tuning techniques playing a crucial role in optimizing model performance. This paper provides a comprehensive overview of fine-tuning techniques for large-scale language models. It reviews the development stages of language models, including statistical language models, neural network language models, pre-trained language models, and large language models. The basic concepts of fine-tuning are explored, covering classic fine-tuning, efficient parameter fine-tuning, prompt tuning, and reinforcement learning fine-tuning. The paper delves into the principles and development of each fine-tuning technique, offering a comparative analysis across these four major categories. In conclusion, the paper summarizes the current state of research on fine-tuning techniques and underscores the potential research value in this domain, providing insights into future directions of development.

Key words: large language model, fine-tuning methods, pre-trained models, natural language processing

摘要： 大型语言模型的崛起是深度学习领域的全新里程碑，而微调技术在优化模型性能方面的起到了关键作用。对大型语言模型微调技术进行了全面的综述，回顾了语言模型的统计语言模型、神经网络语言模型、预训练语言模型和大语言模型四个阶段的发展历程和微调技术的基本概念，从经典参数微调、高效参数微调、提示微调和强化学习微调方法四大部分，探讨总结了各微调技术的原理与发展，并进行了一定的对比分析。最后，总结了当前微调技术的研究状况与发展重点，强调了该领域的潜在研究价值，并展望了未来的发展方向。

关键词: 大语言模型, 微调方法, 预训练模型, 自然语言处理