Large Language Models (LLMs) exhibit remarkable proficiency in addressing a diverse array of tasks within the Natural Language Processing (NLP) domain, with various prompt design strategies significantly augmenting their capabilities. However, these prompts, while beneficial, each possess inherent limitations. The primary prompt design methodologies are twofold: The first, exemplified by the Chain of Thought (CoT), involves manually crafting prompts specific to individual datasets, hence termed Expert-Designed Prompts (EDPs). Once these prompts are established, they are unalterable, and their effectiveness is capped by the expertise of the human designers. When applied to LLMs, the static nature of EDPs results in a uniform approach to both simple and complex problems within the same dataset, leading to the inefficient use of tokens for straightforward issues. The second method involves prompts autonomously generated by the LLM, known as LLM-Derived Prompts (LDPs), which provide tailored solutions to specific problems, mitigating the limitations of EDPs. However, LDPs may encounter a decline in performance when tackling complex problems due to the potential for error accumulation during the solution planning process. To address these challenges, we have conceived a novel Prompt Recursive Search (PRS) framework that leverages the LLM to generate solutions specific to the problem, thereby conserving tokens. The framework incorporates an assessment of problem complexity and an adjustable structure, ensuring a reduction in the likelihood of errors. We have substantiated the efficacy of PRS framework through extensive experiments using LLMs with different numbers of parameters across a spectrum of datasets in various domains. Compared to the CoT method, the PRS method has increased the accuracy on the BBH dataset by 8% using Llama3-7B model, achieving a 22% improvement.
Leveraging Large Language Models for Automated Causal Loop Diagram Generation: Enhancing System Dynamics Modeling through Curated Prompting Techniques
MGH Institute for Technology Assessment, Harvard Medical School（哈佛医学院MGH技术评估研究所）；Melbourne Business School, University of Melbourne（墨尔本大学墨尔本商学院）；Sloan School of Management, Massachusetts Institute of Technology（麻省理工学院斯隆管理学院）
Advancing Multi-Step Mathematical Reasoning in Large Language Models through Multi-Layered Self-Reflection with Auto-Prompting
Luiz de Queiroz College of Agriculture University of São Paulo（圣保罗大学农业学院）；School of Engineering and Sciences Tecnologico de Monterrey（蒙特雷技术学院工程与科学学院）；Institute of Mathematics and Computer Sciences University of São Paulo（圣保罗大学数学与计算机科学研究所）
How to Ask the AI: A User Perspective Survey for Large Language Model Prompting
School of Computer Science and Technology, Guangdong University of Technology（广东工业大学计算机科学与技术学院）；Department of Computer Science, Hong Kong Baptist University（香港浸会大学计算机科学系）
From Prompting to Preference Optimization: A Comparative Study of LLM-based Automated Essay Scoring
Faculty of Information Technology, University of Science, Ho Chi Minh City, Vietnam（信息科技学院，科学大学，胡志明市，越南）；Vietnam National University, Ho Chi Minh City, Vietnam（越南国家大学，胡志明市，越南）
HiFo-Prompt: Prompting with Hindsight and Foresight for LLM-based Automatic Heuristic Design
School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China（西安交通大学数学与统计学院）；School of Electronics and Information, Northwest Polytechnical University, Xi’an, China（西北工业大学电子与信息学院）
HPSS: Heuristic Prompting Strategy Search for LLM Evaluators
The Conversational Artificial Intelligence (CoAI) Group, Tsinghua University（清华大学对话人工智能（CoAI）小组）；University of Electronic Science and Technology of China（电子科技大学）；Zhipu AI（智谱AI）；The Knowledge Engineering Group (KEG), Tsinghua University（清华大学知识工程小组（KEG））
