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Chain-of-Thought (CoT) prompting has been shown to enhance the multi-step reasoning capabilities of Large Language Models (LLMs).
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 1901
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Peter C. Wason. 1968 · 1968
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Psychology of reasoning: Structure and content , volume 86
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Reasoning about Knowledge
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Jason Andress. 2014 · 2014
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Does learning require memorization? A short tale about a long tail
Vitaly Feldman. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
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Data distributional properties drive emergent in-context learning in transformers
Stephanie Chan, Adam Santoro, Andrew Lampinen, Jane Wang, Aaditya Singh, Pierre Richemond, James McClelland, and Felix Hill. 2022 · 2022
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Folio: Natural language reasoning with first-order logic
Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Wenfei Zhou, James Coady, David Peng, Yujie Qiao, Luke Benson, Lucy Sun, Alex Wardle-Solano, Hannah Szabo, Ekaterina Zubova, Matthew Burtell, Jonathan Fan, Yixin Liu, Brian Wong, Malcolm Sailor, Ansong Ni, Linyong Nan, Jungo Kasai, Tao Yu, Rui Zhang, Alexander R. Fabbri, Wojciech Kryscinski, Semih Yavuz, Ye Liu, Xi Victoria Lin, Shafiq Joty, Yingbo Zhou, Caiming Xiong, Rex Ying, Arman Cohan, and Dragomir Radev. 2022 · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
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Text and patterns: For effective chain of thought, it takes two to tango
Aman Madaan and Amir Yazdanbakhsh. 2022 · 2022
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Data contamination: From memorization to exploitation
Inbal Magar and Roy Schwartz. 2022 · 2022
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Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
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Impact of pretraining term frequencies on few-shot numerical reasoning
Yasaman Razeghi, Robert L. Logan IV, Matt Gardner, and Sameer Singh. 2022 · 2022
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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, and Denny Zhou. 2022 · 2022
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An explanation of in-context learning as implicit Bayesian inference
Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma. 2022 · 2022
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The unreliability of explanations in few-shot prompting for textual reasoning
Xi Ye and Greg Durrett. 2022 · 2022
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Towards revealing the mystery behind chain of thought: A theoretical perspective
Guhao Feng, Bohang Zhang, Yuntian Gu, Haotian Ye, Di He, and Liwei Wang. 2023 · 2023
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In-context learning creates task vectors
Roee Hendel, Mor Geva, and Amir Globerson. 2023 · 2023
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Towards reasoning in large language models: A survey
Jie Huang and Kevin Chen-Chuan Chang. 2023 · 2023
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Can large language models truly understand prompts? A case study with negated prompts
Joel Jang, Seonghyeon Ye, and Minjoon Seo. 2023 · 2023
Complementary explanations for effective in-context learning
Xi Ye, Srinivasan Iyer, Asli Celikyilmaz, Veselin Stoyanov, Greg Durrett, and Ramakanth Pasunuru. 2023 · 2023
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Large language models are human-level prompt engineers
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. 2023 · 2023
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Claude 3
Anthropic. 2024 · 2024
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Generalization vs. memorization: Tracing language models’ capabilities back to pretraining data
Antonis Antoniades, Xinyi Wang, Yanai Elazar, Alfonso Amayuelas, Alon Albalak, Kexun Zhang, and William Yang Wang. 2024 · 2024
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Retentive or forgetful? Diving into the knowledge memorizing mechanism of language models
Boxi Cao, Qiaoyu Tang, Hongyu Lin, Shanshan Jiang, Bin Dong, Xianpei Han, Jiawei Chen, Tianshu Wang, and Le Sun. 2024 · 2024
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Understanding in-context learning with a pelican soup framework
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Measuring faithfulness in chain-of-thought reasoning
Tamera Lanham, Anna Chen, Ansh Radhakrishnan, Benoit Steiner, Carson Denison, Danny Hernandez, Dustin Li, Esin Durmus, Evan Hubinger, Jackson Kernion, Kamilė Lukošiūtė, Karina Nguyen, Newton Cheng, Nicholas Joseph, Nicholas Schiefer, Oliver Rausch, Robin Larson, Sam McCandlish, Sandipan Kundu, Saurav Kadavath, Shannon Yang, Thomas Henighan, Timothy Maxwell, Timothy Telleen-Lawton, Tristan Hume, Zac Hatfield-Dodds, Jared Kaplan, Jan Brauner, Samuel R. Bowman, and Ethan Perez. 2023 · 2023
Cited alongside, same era.
Dissecting chain-of-thought: Compositionality through in-context filtering and learning
Yingcong Li, Kartik Sreenivasan, Angeliki Giannou, Dimitris Papailiopoulos, and Samet Oymak. 2023 · 2023
Cited alongside, same era.
R. Thomas McCoy, Shunyu Yao, Dan Friedman, Mathew Hardy, and Thomas L. Griffiths. 2023 · 2023
Cited alongside, same era.
OpenAI. 2023 · 2023
Cited alongside, same era.
What in-context learning “learns” in-context: Disentangling task recognition and task learning
Jane Pan, Tianyu Gao, Howard Chen, and Danqi Chen. 2023 · 2023
Cited alongside, same era.
Reasoning with language model prompting: A survey
Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, and Huajun Chen. 2023 · 2023
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Ting-Rui Chiang and Dani Yogatama. 2024 · 2024
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The impact of reasoning step length on large language models
Mingyu Jin, Qinkai Yu, Dong Shu, Haiyan Zhao, Wenyue Hua, Yanda Meng, Yongfeng Zhang, and Mengnan Du. 2024 · 2024
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In-context learning learns label relationships but is not conventional learning
Jannik Kossen, Yarin Gal, and Tom Rainforth. 2024 · 2024
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Language models, like humans, show content effects on reasoning tasks
Andrew K. Lampinen, Ishita Dasgupta, Stephanie C. Y. Chan, Hannah R. Sheahan, Antonia Creswell, Dharshan Kumaran, James L. McClelland, and Felix Hill. 2024 · 2024
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Chain of thought empowers transformers to solve inherently serial problems
Zhiyuan Li, Hong Liu, Denny Zhou, and Tengyu Ma. 2024 · 2024
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The expressive power of transformers with chain of thought
William Merrill and Ashish Sabharwal. 2024 · 2024
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MetaAI. 2024 · 2024
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Let’s think dot by dot: Hidden computation in transformer language models
Jacob Pfau, William Merrill, and Samuel R. Bowman. 2024 · 2024
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Functional benchmarks for robust evaluation of reasoning performance, and the reasoning gap
Saurabh Srivastava, Annarose M B, Anto P V, Shashank Menon, Ajay Sukumar, Adwaith Samod T, Alan Philipose, Stevin Prince, and Sooraj Thomas. 2024 · 2024
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Chain of thoughtlessness? An analysis of CoT in planning
Kaya Stechly, Karthik Valmeekam, and Subbarao Kambhampati. 2024 · 2024
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Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting
Miles Turpin, Julian Michael, Ethan Perez, and Samuel Bowman. 2024 · 2024
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