Reasoning with Language Model Prompting Papers
🔔 News
- 2024-03-05 We release a new paper: "KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents".
- 2024-02-06 We release a new paper: "EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models" with an HF demo EasyInstruct.
- 2024-01-03 We release a new paper:"A Comprehensive Study of Knowledge Editing for Large Language Models" with a new benchmark KnowEdit! We are looking forward to any comments or discussions on this topic :)
- 2023-7-12 We release EasyEdit, an easy-to-use knowledge editing framework for Large Language Models.
- 2023-6-19 We open-source KnowLM, a knowledgeable large language model framework with pre-training and instruction fine-tuning code (supports multi-machine multi-GPU setup) and various LLMs.
- 2023-3-27 We release EasyInstruct, a package for instructing Large Language Models (LLMs) like ChatGPT in your research experiments. It is designed to be easy to use and easy to extend!
- 2023-2-19 We upload a tutorial of our survey paper to help you learn more about reasoning with language model prompting (Attached with a video (Chinese) of the tutorial).
- 2022-12-19 We release a new survey paper:"Reasoning with Language Model Prompting: A Survey" based on this repository! We are looking forward to any comments or discussions on this topic :)
- 2022-09-14 We create this repository to maintain a paper list on Reasoning with Language Model Prompting.
🔍 Contents
🌟 Introduction
Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications, such as medical diagnosis, negotiation, etc. This paper provides a comprehensive survey of cutting-edge research on reasoning with language model prompting. We introduce research works with comparisons and summaries and provide systematic resources to help beginners. We also discuss the potential reasons for emerging such reasoning abilities and highlight future research directions.
📜 Papers
Overview
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Reasoning with Language Model Prompting: A Survey.
Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Huajun Chen. [abs], 2022.12
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Towards Reasoning in Large Language Models: A Survey.
Jie Huang, Kevin Chen-Chuan Chang. [abs], 2022.12
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A Survey of Deep Learning for Mathematical Reasoning.
Pan Lu, Liang Qiu, Wenhao Yu, Sean Welleck, Kai-Wei Chang. [abs], 2022.12
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A Survey for In-context Learning.
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, Lei Li, Zhifang Sui. [abs], 2022.12
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Knowledge-enhanced Neural Machine Reasoning: A Review.
Tanmoy Chowdhury, Chen Ling, Xuchao Zhang, Xujiang Zhao, Guangji Bai, Jian Pei, Haifeng Chen, Liang Zhao. [abs], 2023.2
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Augmented Language Models: a Survey.
Grégoire Mialon, Roberto Dessì, Maria Lomeli, Christoforos Nalmpantis, Ram Pasunuru, Roberta Raileanu, Baptiste Rozière, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, Edouard Grave, Yann LeCun, Thomas Scialom. [abs], 2023.2
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The Life Cycle of Knowledge in Big Language Models: A Survey.
Boxi Cao, Hongyu Lin, Xianpei Han, Le Sun. [abs], 2023.3
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Is Prompt All You Need? No. A Comprehensive and Broader View of Instruction Learning.
Renze Lou, Kai Zhang, Wenpeng Yin. [abs], 2023.3
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Logical Reasoning over Natural Language as Knowledge Representation: A Survey.
Zonglin Yang, Xinya Du, Rui Mao, Jinjie Ni, Erik Cambria. [abs], 2023.3
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Nature Language Reasoning, A Survey.
Fei Yu, Hongbo Zhang, Benyou Wang. [abs], 2023.3
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A Survey of Large Language Models.
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, Ji-Rong Wen. [abs], 2023.3
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Tool Learning with Foundation Models.
Yujia Qin, Shengding Hu, Yankai Lin, Weize Chen, Ning Ding, Ganqu Cui, Zheni Zeng, Yufei Huang, Chaojun Xiao, Chi Han, Yi Ren Fung, Yusheng Su, Huadong Wang, Cheng Qian, Runchu Tian, Kunlun Zhu, Shihao Liang, Xingyu Shen, Bokai Xu, Zhen Zhang, Yining Ye, Bowen Li, Ziwei Tang, Jing Yi, Yuzhang Zhu, Zhenning Dai, Lan Yan, Xin Cong, Yaxi Lu, Weilin Zhao, Yuxiang Huang, Junxi Yan, Xu Han, Xian Sun, Dahai Li, Jason Phang, Cheng Yang, Tongshuang Wu, Heng Ji, Zhiyuan Liu, Maosong Sun. [abs], 2023.4
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A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future.
Zheng Chu, Jingchang Chen, Qianglong Chen, Weijiang Yu, Tao He, Haotian Wang, Weihua Peng, Ming Liu, Bing Qin, Ting Liu. [abs], 2023.9
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A Survey of Reasoning with Foundation Models: Concepts, Methodologies, and Outlook.
Jiankai Sun, Chuanyang Zheng, Enze Xie, Zhengying Liu, Ruihang Chu, Jianing Qiu, Jiaqi Xu, Mingyu Ding, Hongyang Li, Mengzhe Geng, Yue Wu, Wenhai Wang, Junsong Chen, Zhangyue Yin, Xiaozhe Ren, Jie Fu, Junxian He, Wu Yuan, Qi Liu, Xihui Liu, Yu Li, Hao Dong, Yu Cheng, Ming Zhang, Pheng Ann Heng, Jifeng Dai, Ping Luo, Jingdong Wang, Ji-Rong Wen, Xipeng Qiu, Yike Guo, Hui Xiong, Qun Liu, Zhenguo Li. [abs], 2023.12
Methods
Strategy Enhanced Reasoning
Prompt Engineering
Single-Stage
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Prompting Contrastive Explanations for Commonsense Reasoning Tasks.
Bhargavi Paranjape, Julian Michael, Marjan Ghazvininejad, Luke Zettlemoyer, Hannaneh Hajishirzi. [abs], 2021.6
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Template Filling for Controllable Commonsense Reasoning.
Dheeraj Rajagopal, Vivek Khetan, Bogdan Sacaleanu, Anatole Gershman, Andrew Fano, Eduard Hovy. [abs], 2021.11
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Chain of Thought Prompting Elicits Reasoning in Large Language Models.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, Denny Zhou. [abs], 2022.1
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Large Language Models are Zero-Shot Reasoners.
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, Yusuke Iwasawa. [abs], 2022.5
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Psychologically-informed chain-of-thought prompts for metaphor understanding in large language models.
Ben Prystawski, Paul Thibodeau, Noah Goodman. [abs], 2022.9
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Complexity-based Prompting for Multi-step Reasoning.
Yao Fu, Hao Peng, Ashish Sabharwal, Peter Clark, Tushar Khot. [abs], 2022.10
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Language Models are Multilingual Chain-of-thought Reasoners.
Freda Shi, Mirac Suzgun, Markus Freitag, Xuezhi Wang, Suraj Srivats, Soroush Vosoughi, Hyung Won Chung, Yi Tay, Sebastian Ruder, Denny Zhou, Dipanjan Das, Jason Wei. [abs], 2022.10
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Automatic Chain of Thought Prompting in Large Language Models.
Zhuosheng Zhang, Aston Zhang, Mu Li, Alex Smola. [abs], 2022.10
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Large Language Models are few(1)-shot Table Reasoners.
Wenhu Chen. [abs], 2022.10
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Teaching Algorithmic Reasoning via In-context Learning.
Hattie Zhou, Azade Nova, Hugo Larochelle, Aaron Courville, Behnam Neyshabur, Hanie Sedghi. [abs], 2022.11
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Active Prompting with Chain-of-Thought for Large Language Models.
Shizhe Diao, Pengcheng Wang, Yong Lin, Tong Zhang. [abs], 2023.2
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Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data.
KaShun Shum, Shizhe Diao, Tong Zhang. [abs], 2023.2
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A prompt pattern catalog to enhance prompt engineering with chatgpt.
Jules White, Quchen Fu, Sam Hays, Michael Sandborn, Carlos Olea, Henry Gilbert, Ashraf Elnashar, Jesse Spencer-Smith, Douglas C Schmidt. [abs], 2023.2
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ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, anLearning to Reason and Memorize with Self-Notesd Software Design.
Jules White, Sam Hays, Quchen Fu, Jesse Spencer-Smith, Douglas C Schmidt. [abs], 2023.3
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Learning to Reason and Memorize with Self-Notes.
Jack lanchantin, Shubham Toshniwal, Jason Weston, Arthur Szlam, Sainbayar Sukhbaatar. [abs], 2023.5
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Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models.
Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, Ee-Peng Lim. [abs], 2023.5
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Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Large Language Models.
Yao Yao, Zuchao Li, Hai Zhao. [abs], 2023.5
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Re-Reading Improves Reasoning in Language Models.
Xiaohan Xu, Chongyang Tao, Tao Shen, Can Xu, Hongbo Xu, Guodong Long, Jian-guang Lou. [abs], 2023.9
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Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL.
Hao Sun, Alihan Huyuk, Mihaela van der Schaar.[abs], 2023.9
Multi-Stage
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Iteratively Prompt Pre-trained Language Models for Chain of Thought.
Boshi Wang, Xiang Deng, Huan Sun. [abs], 2022.3
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Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning.
Antonia Creswell, Murray Shanahan, Irina Higgins. [abs], 2022.5
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Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Olivier Bousquet, Quoc Le, Ed Chi. [abs], 2022.5
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Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations.
Jaehun Jung, Lianhui Qin, Sean Welleck, Faeze Brahman, Chandra Bhagavatula, Ronan Le Bras, Yejin Choi. [abs],