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Reasoning in Large Language Models

Collection of papers and resources on how to unlock the reasoning ability of Large Language Models.

Also check out the Awesome-Multimodal-Reasoning collection!

Large Language Models have revolutionized the NLP landscape, showing improved performance and sample efficiency over smaller models. However, increasing model size alone has not proved sufficient for high performance on challenging reasoning tasks, such as solving arithmetic or commonsense problems. We present a collection of papers and resources on how to unlock these abilities.

Contents

Survey

  1. Reasoning with Language Model Prompting: A Survey. Preprint

    Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Huajun Chen. [Paper] [Code], 2022.12

  2. Towards Reasoning in Large Language Models: A Survey. Preprint

    Jie Huang, Kevin Chen-Chuan Chang. [Paper] [Code], 2022.12

Analysis

  1. Can language models learn from explanations in context?. EMNLP 2022

    Andrew K. Lampinen, Ishita Dasgupta, Stephanie C. Y. Chan, Kory Matthewson, Michael Henry Tessler, Antonia Creswell, James L. McClelland, Jane X. Wang, Felix Hill. [Paper], 2022.4

  2. Emergent Abilities of Large Language Models. TMLR 2022

    Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, William Fedus. [Paper] [Blog], 2022.6

  3. Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them. Preprint

    Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V. Le, Ed H. Chi, Denny Zhou, Jason Wei. [Paper] [Code], 2022.10

  4. Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters. Preprint

    Boshi Wang, Sewon Min, Xiang Deng, Jiaming Shen, You Wu, Luke Zettlemoyer, Huan Sun. [Paper] [Code], 2022.12

  5. On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning. Preprint

    Omar Shaikh, Hongxin Zhang, William Held, Michael Bernstein, Diyi Yang. [Paper], 2022.12

  6. Dissociating language and thought in large language models: a cognitive perspective. Preprint

    Kyle Mahowald, Anna A. Ivanova, Idan A. Blank, Nancy Kanwisher, Joshua B. Tenenbaum, Evelina Fedorenko. [Paper], 2023.1

  7. Large Language Models Can Be Easily Distracted by Irrelevant Context. Preprint

    Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed Chi, Nathanael Schärli, Denny Zhou. [Paper], 2023.1

  8. A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity. Preprint

    Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wenliang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, Quyet V. Do, Yan Xu, Pascale Fung. [Paper], 2023.2

  9. Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting. Preprint

    Miles Turpin, Julian Michael, Ethan Perez, Samuel R. Bowman. [Paper] [Code], 2023.5

  10. Faith and Fate: Limits of Transformers on Compositionality. Preprint

    Nouha Dziri, Ximing Lu, Melanie Sclar, Xiang Lorraine Li, Liwei Jiang, Bill Yuchen Lin, Peter West, Chandra Bhagavatula, Ronan Le Bras, Jena D. Hwang, Soumya Sanyal, Sean Welleck, Xiang Ren, Allyson Ettinger, Zaid Harchaoui, Yejin Choi. [Paper], 2023.5

Technique

Reasoning in Large Language Models - An Emergent Ability

  1. Chain of Thought Prompting Elicits Reasoning in Large Language Models. NeurIPS 2022

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, Denny Zhou. [Paper] [Blog], 2022.1

  2. Self-consistency improves chain of thought reasoning in language models. ICLR 2023

    Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, Denny Zhou. [Paper], 2022.3

  3. Iteratively Prompt Pre-trained Language Models for Chain of Thought. EMNLP 2022

    Boshi Wang, Xiang Deng, Huan Sun. [Paper] [Code]

  4. Least-to-most prompting enables complex reasoning in large language models. ICLR 2023

    Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc Le, Ed Chi. [Paper], 2022.5

  5. Large Language Models are Zero-Shot Reasoners. NeurIPS 2022

    Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, Yusuke Iwasawa. [Paper], 2022.5

  6. On the Advance of Making Language Models Better Reasoners. Preprint

    Yifei Li, Zeqi Lin, Shizhuo Zhang, Qiang Fu, Bei Chen, Jian-Guang Lou, Weizhu Chen. [Paper], 2022.6

  7. Large Language Models Still Can't Plan. NeurIPS 2022

    Karthik Valmeekam, Alberto Olmo, Sarath Sreedharan, Subbarao Kambhampati. [Paper] [Code], 2022.6

  8. Solving Quantitative Reasoning Problems with Language Models. NeurIPS 2022

    Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur, Guy Gur-Ari, Vedant Misra. [Paper] [Blog], 2022.6

  9. Rationale-Augmented Ensembles in Language Models. Preprint

    Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Denny Zhou. [Paper], 2022.7

  10. Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning. ICLR 2023

    Pan Lu, Liang Qiu, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Tanmay Rajpurohit, Peter Clark, Ashwin Kalyan. [Project] [Paper] [Code], 2022.9

  11. Ask Me Anything: A simple strategy for prompting language models. Preprint

    Simran Arora, Avanika Narayan, Mayee F. Chen, Laurel Orr, Neel Guha, Kush Bhatia, Ines Chami, Frederic Sala, Christopher Ré. [Paper] [Code], 2022.10

  12. Language Models are Multilingual Chain-of-Thought Reasoners. ICLR 2023

    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. [Paper], 2022.10

  13. Measuring and Narrowing the Compositionality Gap in Language Models. Preprint

    Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A. Smith, Mike Lewis. [Paper], 2022.10

  14. Automatic Chain of Thought Prompting in Large Language Models. Preprint

    Zhuosheng Zhang, Aston Zhang, Mu Li, Alex Smola. [Paper] [Code], 2022.10

  15. ReAct: Synergizing Reasoning and Acting in Language Models. NeurIPS 2022 Workshop FMDM

    Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, Yuan Cao. [Project] [Paper] [Code] [Blog], 2022.10

  16. Reflection of Thought: Inversely Eliciting Numerical Reasoning in Language Models via Solving Linear Systems. Preprint

    Fan Zhou, Haoyu Dong, Qian Liu, Zhoujun Cheng, Shi Han, Dongmei Zhang. [Paper], 2022.10

  17. Mind's Eye: Grounded language model reasoning through simulation. ICLR 2023

    Ruibo Liu, Jason Wei, Shixiang Shane Gu, Te-Yen Wu, Soroush Vosoughi, Claire Cui, Denny Zhou, Andrew M. Dai. [Paper], 2022.10

  18. Language Models of Code are Few-Shot Commonsense Learners. EMNLP 2022

    Aman Madaan, Shuyan Zhou, Uri Alon, Yiming Yang, Graham Neubig. [Paper] [Code], 2022.10

  19. Large Language Models Can Self-Improve. Preprint

    Jiaxin Huang, Shixiang Shane Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, Jiawei Han. [Paper], 2022.10

  20. Retrieval Augmentation for Commonsense Reasoning: A Unified Approach. EMNLP 2022

    Wenhao Yu, Chenguang Zhu, Zhihan Zhang, Shuohang Wang, Zhuosheng Zhang, Yuwei Fang, Meng Jiang. [Paper] [Code], 2022.10

  21. PAL: Program-aided Language Models. Preprint

    Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, Graham Neubig. [Project] [Paper] [Code], 2022.11

  22. Unsupervised Explanation Generation via Correct Instantiations. AAAI 2023

    Sijie Cheng, Zhiyong Wu, Jiangjie Chen, Zhixing Li, Yang Liu, Lingpeng Kong. [Paper], 2022.11

  23. Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks. Preprint

    Wenhu Chen, Xueguang Ma, Xinyi Wang, William W. Cohen. [Paper] [Code], 2022.11

  24. Complementary Explanations for Effective In-Context Learning. Preprint

    Xi Ye, Srinivasan Iyer, Asli Celikyilmaz, Ves Stoyanov, Greg Durrett, Ramakanth Pasunuru. [Paper], 2022.11

  25. MURMUR: Modular Multi-Step Reasoning for Semi-Structured Data-to-Text Generation. Preprint

    Swarnadeep Saha, Xinyan Velocity Yu, Mohit Bansal, Ramakanth Pasunuru, Asli Celikyilmaz. [Paper], 2022.12

  26. Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model. Preprint

    Parishad BehnamGhader, Santiago Miret, Siva Reddy. [Paper] [Code], 2022.12

  27. Large Language Models are reasoners with Self-Verification. Preprint

    Yixuan Weng, Minjun Zhu, Shizhu He, Kang Liu, Jun Zhao. [Paper] [Code], 2022.12

  28. Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions. Preprint

    Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, Ashish Sabharwal. [Paper] [Code], 2022.12

  29. Language Models as Inductive Reasoners. Preprint

    Zonglin Yang, Li Dong, Xinya Du, Hao Cheng, Erik Cambria, Xiaodong Liu, Jianfeng Gao, Furu Wei. [Paper], 2022.12

  30. LAMBADA: Backward Chaining for Automated Reasoning in Natural Language. Preprint

    Seyed Mehran Kazemi, Najoung Kim, Deepti Bhatia, Xin Xu, Deepak Ramachandran. [Paper], 2022.12

  31. Rethinking with Retrieval: Faithful Large Language Model Inference. Preprint

    Hangfeng He, Hongming Zhang, Dan Roth. [Paper], 2023.1

  32. Specializing Smaller Language Models towards Multi-Step Reasoning. Preprint

    Yao Fu, Hao Peng, Litu Ou, Ashish Sabharwal, Tushar Khot. [Paper], 2023.1

  33. Faithful Chain-of-Thought Reasoning. Preprint

    Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, Chris Callison-Burch. [Paper], 2023.1

  34. Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning. Preprint

    Yunhu Ye, Binyuan Hui, Min Yang, Binhua Li, Fei Huang, Yongbin Li. [Paper], 2023.1

  35. Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models. Preprint

    Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, Weizhu Chen. [Paper], 2023.2

  36. Multimodal Chain-of-Thought Reasoning in Language Models. Preprint

    Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, Alex Smola. [Paper] [Code], 2023.2

  37. Active Prompting with Chain-of-Thought for Large Language Models. Preprint

    Shizhe Diao, Pengcheng Wang, Yong Lin, Tong Zhang. [Paper] [Code], 2023.2

  38. Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data. Preprint

    KaShun Shum, Shizhe Diao, Tong Zhang. [Paper] [Code], 2023.2

  39. Language Is Not All You Need: Aligning Perception with Language Models. Preprint

    Shaohan Huang, Li Dong, Wenhui Wang, Yaru Hao, Saksham Singhal, Shuming Ma, Tengchao Lv, Lei Cui, Owais Khan Mohammed, Barun Patra, Qiang Liu, Kriti Aggarwal, Zewen Chi, Johan Bjorck, Vishrav Chaudhary, Subhojit Som, Xia Song, Furu Wei. [Paper] [Code], 2023.2

  40. ART: Automatic multi-step reasoning and tool-use for large language models. Preprint

    Bhargavi Paranjape, Scott Lundberg, Sameer Singh, Hannaneh Hajishirzi, Luke Zettlemoyer, Marco Tulio Ribeiro. [Paper], 2023.3

  41. REFINER: Reasoning Feedback on Intermediate Representations. Preprint

    Debjit Paul, Mete Ismayilzada, Maxime Peyrard, Beatriz Borges, Antoine Bosselut, Robert West, Boi Faltings. [Project] [Paper] [Code], 2023.4

  42. Tree of Thoughts: Deliberate Problem Solving with Large Language Models. Preprint

    Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, Karthik Narasimhan. [Paper] [Code], 2023.5

  43. Reasoning Implicit Sentiment with Chain-of-Thought Prompting ACL

    Hao Fei, Bobo Li, Qian Liu, Lidong Bing, Fei Li, Tat-Seng Chua. [Paper] [Code], 2023.05

  44. LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond. Preprint

    Philippe Laban, Wojciech Kryściński, Divyansh Agarwal, Alexander R. Fabbri, Caiming Xiong, Shafiq Joty, Chien-Sheng Wu. [Paper], 2023.5

  45. Reasoning with Language Model is Planning with World Model. Preprint

    Shibo Hao, Yi Gu, Haodi Ma, Joshua Jiahua Hong, Zhen Wang, Daisy Zhe Wang, Zhiting Hu. [Paper], 2023.5

Scaling Smaller Language Models to Reason

  1. Scaling Instruction-Finetuned Language Models. Preprint

    Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, Jason Wei. [Paper], 2022.10

  2. Distilling Multi-Step Reasoning Capabilities of Large Language Models into Smaller Models via Semantic Decompositions. Preprint

    Kumar Shridhar, Alessandro Stolfo, Mrinmaya Sachan. [Paper], 2022.12

  3. Teaching Small Language Models to Reason. Preprint

    Lucie Charlotte Magister, Jonathan Mallinson, Jakub Adamek, Eric Malmi, Aliaksei Severyn. [Paper], 2022.12

  4. Large Language Models Are Reasoning Teachers. Preprint

    Namgyu Ho, Laura Schmid, Se-Young Yun. [Paper] [Code], 2022.12

  5. Specializing Smaller Language Models towards Multi-Step Reasoning. Preprint

    Yao Fu, Hao Peng, Litu Ou, Ashish Sabharwal, Tushar Khot. [Paper], 2023.1

Benchmark

Reasoning Ability Benchmarks
Arithmetic GSM8K / SVAMP / ASDiv / AQuA / MAWPS / AddSub / MultiArith / SingleEq / SingleOp / Lila
Commonsense CommonsenseQA / StrategyQA / ARC / BoolQ / HotpotQA / OpenBookQA / PIQA
Symbolic CoinFlip / LastLetterConcatenation / ReverseList
Logical ReClor / LogiQA / ProofWriter
Other BIG-bench / AGIEval / ALERT / CONDAQA / SCAN / WikiWhy

Note: Although there is no official version for the Symbolic Reasoning benchmarks, you can generate your own here!

Other Useful Resources

  • Chain-of-Thought Hub Benchmarking LLM reasoning performance with chain-of-thought prompting.
  • ThoughtSource Central and open resource for data and tools related to chain-of-thought reasoning in large language models.
  • CoTEVer Chain of Thought Prompting Annotation Toolkit for Explanation Verification.
  • AgentChain Chain together LLMs for reasoning & orchestrate multiple large models for accomplishing complex tasks.
  • Cascades Python library which enables complex compositions of language models such as scratchpads, chain of thought, tool use, selection-inference, and more.
  • LogiTorch PyTorch-based library for logical reasoning on natural language.
  • Promptify Solve NLP Problems with LLM's & Easily generate different NLP Task prompts for popular generative models like GPT, PaLM, and more.
  • MiniChain Tiny library for large language models.
  • LlamaIndex Provides a central interface to connect your LLM's with external data.
  • EasyInstruct Easy to use package for instructing Large Language Models (LLMs) like GPT-3 in research experiments.

Other Awesome Lists

  • Awesome-Multimodal-Reasoning Collection of papers and resources on Multimodal Reasoning, including Vision-Language Models, Multimodal Chain-of-Thought, Visual Inference, and others.
  • Chain-of-ThoughtsPapers A trend starts from "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models".
  • LM-reasoning Collection of papers and resources on Reasoning in Large Language Models.
  • Prompt4ReasoningPapers Repository for the paper "Reasoning with Language Model Prompting: A Survey".
  • ReasoningNLP Paper list on reasoning in NLP
  • Instruction-Tuning-Papers Reading list of Instruction-tuning.
  • Deep-Reasoning-Papers Recent Papers including Neural Symbolic Reasoning, Logical Reasoning, Visual Reasoning, planning and any other topics connecting deep learning and reasoning.
  • Awesome-LLM Curated list of Large Language Model.

Contributing

  • Add a new paper or update an existing paper, thinking about which category the work should belong to.
  • Use the same format as existing entries to describe the work.
  • Add the abstract link of the paper (/abs/ format if it is an arXiv publication).

Don't worry if you do something wrong, it will be fixed for you!

Contributors