Large language models (LLMs) have shown remarkable performance on many different Natural Language Processing (NLP) tasks. Prompt engineering plays a key role in adding more to the already existing abilities of LLMs to achieve significant performance gains on various NLP tasks. Prompt engineering requires composing natural language instructions called prompts to elicit knowledge from LLMs in a structured way. Unlike previous state-of-the-art (SoTA) models, prompt engineering does not require extensive parameter re-training or fine-tuning based on the given NLP task and thus solely operates on the embedded knowledge of LLMs. Additionally, LLM enthusiasts can intelligently extract LLMs' knowledge through a basic natural language conversational exchange or prompt engineering, allowing more and more people even without deep mathematical machine learning background to experiment with LLMs. With prompt engineering gaining popularity in the last two years, researchers have come up with numerous engineering techniques around designing prompts to improve accuracy of information extraction from the LLMs. In this paper, we summarize different prompting techniques and club them together based on different NLP tasks that they have been used for. We further granularly highlight the performance of these prompting strategies on various datasets belonging to that NLP task, talk about the corresponding LLMs used, present a taxonomy diagram and discuss the possible SoTA for specific datasets. In total, we read and present a survey of 44 research papers which talk about 39 different prompting methods on 29 different NLP tasks of which most of them have been published in the last two years.

翻译：大语言模型（LLMs）在众多自然语言处理（NLP）任务中展现出卓越的性能。提示工程在进一步增强LLMs现有能力、实现各类NLP任务性能显著提升方面发挥着关键作用。提示工程需要构建称为提示的自然语言指令，以结构化方式从LLMs中引导知识。与以往最先进（SoTA）模型不同，提示工程无需基于给定NLP任务进行大量参数重训练或微调，仅需利用LLMs内嵌的知识即可运作。此外，LLM爱好者可通过基础的自然语言对话交互或提示工程，智能地提取LLMs的知识，这使得越来越多即使不具备深厚数学机器学习背景的人也能对LLMs进行实验。随着提示工程在过去两年中日益普及，研究人员提出了大量围绕提示设计的工程技术，以提高从LLMs中提取信息的准确性。本文总结了不同的提示技术，并根据其所应用的NLP任务进行了归类整合。我们进一步细粒度地展示了这些提示策略在属于该NLP任务的各类数据集上的性能表现，讨论了所使用的相应LLMs，呈现了分类体系图，并探讨了特定数据集上可能的SoTA方法。总计，我们研读并综述了44篇研究论文，这些论文涉及29种不同NLP任务中的39种提示方法，其中大部分发表于过去两年内。