计算机工程与应用2024，Vol.60Issue(17)：17-33,17.DOI:10.3778/j.issn.1002-8331.2312-0035
大语言模型微调技术的研究综述
Comprehensive Review of Large Language Model Fine-Tuning
摘要
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 statis-tical 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 tech-nique,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.关键词
大语言模型/微调方法/预训练模型/自然语言处理Key words
large language model/fine-tuning methods/pre-trained models/natural language processing分类
信息技术与安全科学引用本文复制引用
张钦彤,王昱超,王鹤羲,王俊鑫,陈海..大语言模型微调技术的研究综述[J].计算机工程与应用,2024,60(17):17-33,17.基金项目
广东省教育科学规划课题(2022GXJK47) （2022GXJK47）
认知智能全国重点实验室智能教育开放课题(iED2023-005). （iED2023-005）
