Large Language Models (LLMs) have exhibited remarkable capabilities in many complex tasks including mathematical reasoning. However, traditional approaches heavily rely on ensuring self-consistency within single prompting method, which limits the exploration of diverse problem-solving strategies. This study addresses these limitations by performing an experimental analysis of distinct prompting methods within the domain of mathematical reasoning. Our findings demonstrate that each method explores a distinct search space, and this differentiation becomes more evident with increasing problem complexity. To leverage this phenomenon, we applied efficient sampling process that uniformly combines samples from these diverse methods, which not only expands the maximum search space but achieves higher performance with fewer runs compared to single methods. Especially, within the subset of difficult questions of MATH dataset named MATH-hard, The maximum search space was achieved while utilizing approximately 43% fewer runs than single methods on average. These findings highlight the importance of integrating diverse problem-solving strategies to enhance the reasoning abilities of LLMs.
LLMs can Find Mathematical Reasoning Mistakes by Pedagogical Chain-of-Thought
Shanghai Business School（上海商学院）；Learnable.AI Inc.（Learnable.AI公司）；Centre for Frontier AI Research, A*STAR（前沿人工智能研究中心，A*STAR）；Institute of High-Performance Computing, A*STAR（高性能计算研究所，A*STAR）；The Hong Kong Polytechnic University（香港理工大学）；Microsoft Research Asia（微软亚洲研究院）
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）
PromptGuard: An Orchestrated Prompting Framework for Principled Synthetic Text Generation for Vulnerable Populations using LLMs with Enhanced Safety, Fairness, and Controllability
Posts and Telecommunications Institute of Technology（邮电技术研究所）；Hanoi Architectural University（河内建筑大学）
AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；City University of Hong Kong（香港城市大学）；Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；The Hong Kong University of Science and Technology（香港科技大学）；University of California, Merced（加州大学默塞德分校）；Chongqing University（重庆大学）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）
SarcasmBench: Towards Evaluating Large Language Models on Sarcasm Understanding
College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）；The Hong Kong Polytechnic University（香港理工大学）；College of Software Engineering, Zhengzhou University of Light Industry（郑州轻工业大学软件学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Information Technology, Halmstad University（哈尔姆斯塔德大学信息技术学院）
A Survey on Large Language Models for Mathematical Reasoning
National Key Laboratory for Novel Software Technology（国家新型软件技术重点实验室）；School of Artificial Intelligence（人工智能学院）；Nanyang Technological University（南洋理工大学）；Skywork AI Singapore（Skywork AI新加坡）；The Chinese University of Hong Kong Shenzhen（香港中文大学（深圳））
From Prediction to Application: Language Model-based Code Knowledge Tracing with Domain Adaptive Pre-Training and Automatic Feedback System with Pedagogical Prompting for Comprehensive Programming Education
Enuma Inc（Enuma公司）；University of Memphis（孟菲斯大学）；Daegu National University of Education（大邱教育大学）；Seoul National University（首尔大学）；Seoul Metropolitan Office of Education（首尔市教育厅）；Korea University（高丽大学）
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）
