计算机科学与探索2025，Vol.19Issue(5)：1141-1156,16.DOI:10.3778/j.issn.1673-9418.2407021
面向闭源大语言模型的增强研究综述
Review of Enhancement Research for Closed-Source Large Language Model
摘要
Abstract
With the rapid development of large language models in the field of natural language processing,performance enhancement of closed-source large language models represented by the GPT family has become a challenge.Due to the inaccessibility of parameter weights inside the models,traditional training methods,such as fine-tuning techniques,are difficult to be applied to closed-source large language models,which makes it difficult for further optimization on these models.Meanwhile,closed-source large language models have been widely used in downstream real-world tasks,and thus it is important to investigate how to enhance the performance of closed-source large language models.This paper focuses on the enhancement of closed-source large language models,analyzes three techniques,namely prompt engineering,retrieval augmented generation,and agent,and further subdivides them according to the technical characteristics and modular architectures of the different methods.The core idea,main method and application effect of each technology are introduced in detail,and the superiority and limitation of different augmentation methods in terms of reasoning ability,generation credibility and task adaptability are studied.In addition,this paper also discusses the combined application of these three techniques,combining with specific cases to emphasize the great potential of the combined techniques in enhancing model performance.Finally,this paper summarizes the research status and problems of the existing techniques,and looks forward to the future development of enhancement techniques for closed-source large language models.关键词
闭源模型/大语言模型/提示工程/检索增强生成/智能体Key words
closed-source model/large language model/prompt engineering/retrieval augmented generation/agent分类
计算机与自动化引用本文复制引用
刘华玲,张子龙,彭宏帅..面向闭源大语言模型的增强研究综述[J].计算机科学与探索,2025,19(5):1141-1156,16.基金项目
国家自然科学基金(12371272) （12371272）
上海市2022年度"科技创新行动计划"基础研究领域项目(22JC1400800). This work was supported by the National Natural Science Foundation of China(12371272),and the Science and Technology Innovation Action Plan Basic Research Program of Science and Technology Commission of Shanghai Municipality in 2022(22JC1400800). （22JC1400800）
