Review of Enhancement Research for Closed-Source Large Language Model - LIU Hualing, ZHANG Zilong, PENG Hongshuai (Jisuanji kexue yu tansuo)
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.