Efficient Prompting Methods for Large Language Models: A Survey

• URL: http://arxiv.org/abs/2404.01077v2
• Date: Mon, 02 Dec 2024 08:47:24 GMT
• Title: Efficient Prompting Methods for Large Language Models: A Survey
• Authors: Kaiyan Chang, Songcheng Xu, Chenglong Wang, Yingfeng Luo, Xiaoqian Liu, Tong Xiao, Jingbo Zhu,
• Abstract summary: Efficient Prompting Methods have attracted a wide range of attention.<n>We discuss Automatic Prompt Engineering for different prompt components and Prompt Compression in continuous and discrete spaces.
• Score: 50.82812214830023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prompting is a mainstream paradigm for adapting large language models to specific natural language processing tasks without modifying internal parameters. Therefore, detailed supplementary knowledge needs to be integrated into external prompts, which inevitably brings extra human efforts and computational burdens for practical applications. As an effective solution to mitigate resource consumption, Efficient Prompting Methods have attracted a wide range of attention. We provide mathematical expressions at a high level to deeply discuss Automatic Prompt Engineering for different prompt components and Prompt Compression in continuous and discrete spaces. Finally, we highlight promising future directions to inspire researchers interested in this field.

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