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.
MSP: Multi-Stage Prompting for Making Pre-trained Language Models Better Translators
Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Kuaishou Tech, Co.（快手科技有限公司）；Institute for AI Industry Research, Tsinghua University（清华大学智能产业研究院）；International Innovation Center of Tsinghua University（清华大学上海国际创新中心）
Decomposed Prompting: Probing Multilingual Linguistic Structure Knowledge in Large Language Models
Center for Information and Language Processing (CIS)（信息与语言处理中心）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；ScaDS.AI and TU Dresden（ScaDS.AI 和 梅克伦堡-前波美拉尼亚技术大学）；Department of Statistics, LMU Munich（统计学系，慕尼黑大学）；University of Maryland, College Park（马里兰大学 College Park 分校）
No One Fits All: From Fixed Prompting to Learned Routing in Multilingual LLMs
Department of Computer Science and Information Engineering, National Taiwan University, Taiwan（国立台湾大学计算机科学与资讯工程学系）；Institute of Information Science, Academia Sinica, Taiwan（台湾“中央研究院”信息科学研究所）；AI Research Center (AINTU), National Taiwan University, Taiwan（国立台湾大学人工智能研究中心）
Meta-Task Prompting Elicits Embeddings from Large Language Models
University of Amsterdam（阿姆斯特丹大学）；University of Maryland（马里兰大学）；University of Technology Sydney（悉尼科技大学）；Microsoft Corporation（微软公司）；Tencent IEG（腾讯互动娱乐事业群）
Zero-Shot Chain-of-Thought Reasoning Guided by Evolutionary Algorithms in Large Language Models
School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；National Key Laboratory of General Artificial Intelligence（通用人工智能国家重点实验室）；Key Laboratory of Machine Perceptron (MOE), Peking University（北京大学机器感知教育部重点实验室）
Enhancing Chain of Thought Prompting in Large Language Models via Reasoning Patterns
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Wuhan AI Research（武汉人工智能研究院）
Legal Syllogism Prompting: Teaching Large Language Models for Legal Judgment Prediction
Peking University Law School（北京大学法学院）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；PKU-WUHAN Institute for Artificial Intelligence（北大-武汉人工智能研究院）
PHAnToM: Persona-based Prompting Has An Effect on Theory-of-Mind Reasoning in Large Language Models
Institute of Data Science（数据科学研究所）；NUS Centre for Trusted Internet and Community, National University of Singapore（新加坡国立大学可信互联网与社区中心）；Amazon（亚马逊）；Stanford University（斯坦福大学）；Tsinghua University（清华大学）
Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
Singapore Management University（新加坡管理大学）；Southwest Jiaotong University（西南交通大学）；Singapore University of Technology and Design（新加坡科技设计大学）；East China Normal University（华东师范大学）
Beyond the Parameters: A Technical Survey of Contextual Enrichment in Large Language Models: From In-Context Prompting to Causal Retrieval-Augmented Generation
Enhancing Granular Sentiment Classification with Chain-of-Thought Prompting in Large Language Models
John F Kennedy High school Fremont, USA（约翰·F·肯尼迪高中弗里蒙特分校）；Queensland Academy for Science Mathematics and Technology（昆士兰科学数学与技术学院）；Research Spark Hub Inc（Research Spark Hub公司）
Hypothesis Testing Prompting Improves Deductive Reasoning in Large Language Models
Shanghai Jiao Tong University（上海交通大学）；MoE Key Lab of Artificial Intelligence（教育部人工智能重点实验室）；AI Institute（人工智能研究院）；State Key Lab of Advanced Optical Communication System and Network（先进光通信系统与网络国家重点实验室）
