针对现有提示技术任务特定、通用性差且非专业用户使用门槛高的问题，本文提出HELP ME THINK提示策略，引导GPT3通过提问结合用户回答完成任务，在多类高难度任务上验证了有效性，为挖掘大语言模型能力提供了新思路。
摘要：
控制语言模型生成的文本并实现内容定制是一项长期存在的挑战。现有旨在提供控制能力的提示技术均为任务特定型，缺乏通用性，这让非专业用户在为自身任务寻找合适方法时面临海量选择。这类技术所需的投入（比如编写示例、解释、指令等）进一步限制了它们在非专业用户中的普及。本文提出了一种简单的提示策略HELP ME THINK，该策略引导GPT3通过提出一系列相关问题并结合用户的回答来执行任务，从而为非专业用户提供帮助。我们在多种任务上验证了HELP ME THINK技术的有效性，尤其聚焦于那些普通人类难以完成、需要大量思考才能执行的任务。我们希望本研究能推动开发非传统方式来释放大语言模型的能力。
英文摘要：
Controlling the text generated by language models and customizing the content has been a long-standing challenge. Existing prompting techniques proposed in pursuit of providing control are task-specific and lack generality; this provides overwhelming choices for non-expert users to find a suitable method for their task. The effort associated with those techniques, such as in writing examples, explanations, instructions, etc. further limits their adoption among non-expert users. In this paper, we propose a simple prompting strategy HELP ME THINK where we encourage GPT3 to help non-expert users by asking a set of relevant questions and leveraging user answers to execute the task. We demonstrate the efficacy of our technique HELP ME THINK on a variety of tasks. Specifically, we focus on tasks that are hard for average humans and require significant thinking to perform. We hope our work will encourage the development of unconventional ways to harness the power of large language models.
Rewarding How Models Think Pedagogically: Integrating Pedagogical Reasoning and Thinking Rewards for LLMs in Education
Chosun University（昌原大学）；Korea University（韩国大学）；Seoul National University（首尔国立大学）；Korea Institute for Curriculum and Evaluation（韩国课程与评价研究院）；Upstage；Indiana University Bloomington（印第安纳大学布卢明顿分校）；Nanyang Technological University（南洋理工大学）
Think Step by Step: Chain-of-Gesture Prompting for Error Detection in Robotic Surgical Videos
Tsinghua University（清华大学）；National University of Singapore（新加坡国立大学）；University College London（伦敦大学学院）；Wellcome/EPSRC Centre for Interventional and Surgical Sciences（Wellcome/EPSRC介入与外科科学中心）
Thinking Less to Simulate Better: Intuitive Prompting Improves LLM Agents Simulating Individual Social Media Reactions, Including Unfamiliar Content
Institute for Artificial Intelligence Research and Development of Serbia（塞尔维亚人工智能研发研究所）；University of Belgrade（贝尔格莱德大学）；Institute for Philosophy and Social Theory（哲学与社会理论研究所）；Faculty of Political Sciences（政治科学学院）；University of Vienna（维也纳大学）；Faculty of Social Sciences（社会科学学院）；Universitas Negeri Padang（巴东国立大学）；Faculty of Engineering（工程学院）；University of Novi Sad（诺维萨德大学）；Faculty of Philosophy（哲学学院）；Nanyang Technological University（南洋理工大学）；School of Social Sciences（社会科学学院）
Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step
University of California Los Angeles（加利福尼亚大学洛杉矶分校）；Allen Institute for Artificial Intelligence（艾伦人工智能研究所）；University of Southern California（南加利福尼亚大学）；Yonsei University（延世大学）；University of Washington（华盛顿大学）
Self-Prompting Large Language Models for Zero-Shot Open-Domain QA
Shanghai Jiao Tong University（上海交通大学）；SJTU-Paris Elite Institute of Technology, Shanghai Jiao Tong University（上海交通大学巴黎卓越工程师学院）；School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）
SoT: Structured-of-Thought Prompting Guides Multilingual Reasoning in Large Language Models
Key Laboratory of Big Data & Artificial Intelligence in Transportation (Beijing Jiaotong University), Ministry of Education（大数据与人工智能交通 key laboratory（北京交通大学））；School of Computer Science and Technology, Beijing Jiaotong University（计算机科学与技术学院（北京交通大学））；University of Montreal（蒙特利尔大学）
Multi-Persona Thinking for Bias Mitigation in Large Language Models
Dept. Computing Science, Alberta Machine Intelligence Institute (Amii) University of Alberta, Canada（计算科学系，阿尔伯塔机器智能研究所（Amii），阿尔伯塔大学，加拿大）；Canada CIFAR AI Chair, Amii（加拿大 CIFAR 人工智能主席，Amii）
Chain of Grounded Objectives: Bridging Process and Goal-oriented Prompting for Code Generation
Electronics and Telecommunications Research Institute（电子电信研究院）；Department of Computer Science and Engineering, Seoul National University（计算机科学与工程系，首尔国立大学）
Teaching LLMs to Think Mathematically: A Critical Study of Decision-Making via Optimization
Data Science Department, Princess Sumaya University for Technology（普里姆斯萨大学技术学院数据科学系）；Electrical and Computer Engineering Department, Virginia Tech（弗吉尼亚理工大学电气与计算机工程系）；Computer Science Department, Princess Sumaya University for Technology（普里姆斯萨大学技术学院计算机科学系）；Computer Science Department, King Fahd University of Petroleum and Minerals（国王法赫德石油与矿物大学计算机科学系）
