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.

翻译：提示是一种主流的范式，它能使大型语言模型适应特定的自然语言处理任务，而无需修改其内部参数。因此，详细的补充知识需要被整合到外部提示中，这在实际应用中不可避免地带来了额外的人工努力和计算负担。作为减轻资源消耗的有效解决方案，高效提示方法已引起广泛关注。我们提供了高层次的数学表达式，以深入讨论针对不同提示组件的自动提示工程，以及在连续和离散空间中的提示压缩。最后，我们强调了有前景的未来研究方向，以启发对此领域感兴趣的研究人员。