Large Language Models (LLMs) exhibit impressive performance across various domains but still struggle with arithmetic reasoning tasks. Recent work shows the effectiveness of prompt design methods in enhancing reasoning capabilities. However, these approaches overlook crucial requirements for prior knowledge of specific concepts, theorems, and tricks to tackle most arithmetic reasoning problems successfully. To address this issue, we propose a novel and effective Teaching-Inspired Integrated Framework, which emulates the instructional process of a teacher guiding students. This method equips LLMs with essential concepts, relevant theorems, and similar problems with analogous solution approaches, facilitating the enhancement of reasoning abilities. Additionally, we introduce two new Chinese datasets, MathMC and MathToF, both with detailed explanations and answers. Experiments are conducted on nine benchmarks which demonstrates that our approach improves the reasoning accuracy of LLMs. With GPT-4 and our framework, we achieve new state-of-the-art performance on four math benchmarks (AddSub, SVAMP, Math23K and AQuA) with accuracies of 98.2% (+3.3%), 93.9% (+0.2%), 94.3% (+7.2%) and 81.1% (+1.2%). Our data and code are available at https://github.com/SallyTan13/Teaching-Inspired-Prompting.
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）
Teaching Algorithmic Reasoning via In-context Learning
Mila（米拉研究所）；Université de Montreal（蒙特利尔大学）；Google Research（谷歌研究院）
大语言模型（LLM）通过扩展模型和数据大小，显示出越来越多的上下文学习能力。尽管取得了这些进展，LLM仍然无法解决算法推理问题。虽然为最终答案提供了一个基本原理，导致了多步推理问题的进一步改进，但Anil et al. 2022表明，即使是简单的算法推理任务，如奇偶校验，也远未解决。在这项工作中，我们确定并研究了成功教授算法推理的四个关键阶段：（1）将算法制定为技能，（2）同时教授多种技能（技能积累），（3）教授如何组合技能（技能组合）和（4）教授如何使用技能作为工具。我们表明，它是可以教算法推理LLMs通过上下文学习，我们称之为算法提示。我们评估我们的方法在各种算术和定量推理任务，并表现出显着的提升性能比现有的提示技术。特别是，对于长奇偶校验、加法、乘法和减法，与最佳可用基线相比，我们分别实现了约10倍、9倍、5倍和2倍的误差减少。
Enhancing Text Annotation through Rationale-Driven Collaborative Few-Shot Prompting
Institute of Artificial Intelligence and Future Networks, Beijing Normal University（北京师范大学人工智能与未来网络研究院）；Hong Kong Baptist University（香港浸会大学）；BNU-HKBU United International College（北京师范大学-香港浸会大学联合国际学院）；College of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）
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）
Enhancing Depression Detection with Chain-of-Thought Prompting: From Emotion to Reasoning Using Large Language Models
College of Information Science and Engineering, Ritsumeikan University（立命馆大学信息科学与工程学院）；Fujita Health University（藤田医科大学）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
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）
Enhancing Document-level Argument Extraction with Definition-augmented Heuristic-driven Prompting for LLMs
School of Engineering and Informatics, University of Sussex（萨塞克斯大学工程与信息学院）；International Business School Suzhou, Xi’an Jiaotong-Liverpool University（西交利物浦大学苏州国际商学院）；Management School, University of Liverpool（利物浦大学管理学院）
Understanding Before Reasoning: Enhancing Chain-of-Thought with Iterative Summarization Pre-Prompting
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science（电子工程学院，上海工程技术大学）；School of Information Science and Engineering, Ningbo University（信息科学与工程学院，宁波大学）
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）
Transfer-Prompting: Enhancing Cross-Task Adaptation in Large Language Models via Dual-Stage Prompts Optimization
School of Artificial intelligence, Jilin University（吉林大学人工智能学院）；Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Jilin University（吉林大学知识驱动人机智能工程研究中心）；International Center of Future Science, Jilin University（吉林大学未来科学国际中心）
"Sorry, Come Again?" Prompting -- Enhancing Comprehension and Diminishing Hallucination with [PAUSE]-injected Optimal Paraphrasing
AI Institute, University of South Carolina（南卡罗来纳大学AI研究院）；Indian Institute of Technology, Kharagpur（印度理工学院克勒格布尔分校）；Islamic University of Technology（伊斯兰科技大学）；Stanford University（斯坦福大学）；Amazon AI（亚马逊AI研究院）
Improving Student-AI Interaction Through Pedagogical Prompting: An Example in Computer Science Education
Carnegie Mellon University（卡内基梅隆大学）；University of Michigan（密歇根大学）；University of Toronto（多伦多大学）；Colgate University（科尔盖特大学）；University of Toronto Mississauga（多伦多大学密西根分校）
