The remarkable advancements in large language models (LLMs) have brought about significant improvements in Natural Language Processing(NLP) tasks. This paper presents a comprehensive review of in-context learning techniques, focusing on different types of prompts, including discrete, continuous, few-shot, and zero-shot, and their impact on LLM performance. We explore various approaches to prompt design, such as manual design, optimization algorithms, and evaluation methods, to optimize LLM performance across diverse tasks. Our review covers key research studies in prompt engineering, discussing their methodologies and contributions to the field. We also delve into the challenges faced in evaluating prompt performance, given the absence of a single "best" prompt and the importance of considering multiple metrics. In conclusion, the paper highlights the critical role of prompt design in harnessing the full potential of LLMs and provides insights into the combination of manual design, optimization techniques, and rigorous evaluation for more effective and efficient use of LLMs in various NLP tasks.

该论文综述了大型语言模型在自然语言处理任务中的重要性，并聚焦于不同类型的提示（如离散、连续、少样本和零样本）及其对模型性能的影响。论文探讨了手动设计、优化算法和评估方法等多种提示设计方法，以优化模型在不同任务中的性能。此外，论文还讨论了评估提示性能所面临的挑战，并指出了提示设计在充分利用大型语言模型的潜力中的关键作用，为在自然语言处理任务中更有效、更高效地使用大型语言模型提供了启示。

关于上下文学习中零样本提示设计的实际调查