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Reasoning encompasses two typical types: deductive reasoning and inductive reasoning.
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, et al. 2020 · 1901
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Clutrr: A diagnostic benchmark for inductive reasoning from text
Koustuv Sinha, Shagun Sodhani, Jin Dong, Joelle Pineau, and William L Hamilton. 2019 · 1908
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Reclor: A reading comprehension dataset requiring logical reasoning
Weihao Yu, Zihang Jiang, Yanfei Dong, and Jiashi Feng. 2020 · 2002
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Explaining answers with entailment trees
Bhavana Dalvi, Peter Jansen, Oyvind Tafjord, Zhengnan Xie, Hannah Smith, Leighanna Pipatanangkura, and Peter Clark. 2021 · 2021
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What makes good in-context examples for gpt- 3 3 ?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2021 · 2021
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Learning to retrieve prompts for in-context learning
Ohad Rubin, Jonathan Herzig, and Jonathan Berant. 2021 · 2021
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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, and Zhifang Sui. 2022 · 2022
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Demystifying prompts in language models via perplexity estimation
Hila Gonen, Srini Iyer, Terra Blevins, Noah A Smith, and Luke Zettlemoyer. 2022 · 2022
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Folio: Natural language reasoning with first-order logic
Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Luke Benson, Lucy Sun, Ekaterina Zubova, Yujie Qiao, Matthew Burtell, et al. 2022 · 2022
Cited alongside, same era.
Towards reasoning in large language models: A survey
Jie Huang and Kevin Chen-Chuan Chang. 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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Large language models still can’t plan (a benchmark for llms on planning and reasoning about change)
Karthik Valmeekam, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati. 2022 · 2022
Cited alongside, same era.
Large language models are not abstract reasoners
Gaël Gendron, Qiming Bao, Michael Witbrock, and Gillian Dobbie. 2023 · 2023
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Large language models as general pattern machines
Suvir Mirchandani, Fei Xia, Pete Florence, Brian Ichter, Danny Driess, Montserrat Gonzalez Arenas, Kanishka Rao, Dorsa Sadigh, and Andy Zeng. 2023 · 2023
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OpenAI. 2023 · 2023
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Logic-lm: Empowering large language models with symbolic solvers for faithful logical reasoning
Liangming Pan, Alon Albalak, Xinyi Wang, and William Yang Wang. 2023 · 2023
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Creator: Tool creation for disentangling abstract and concrete reasoning of large language models
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Zonglin Yang, Li Dong, Xinya Du, Hao Cheng, Erik Cambria, Xiaodong Liu, Jianfeng Gao, and Furu Wei. 2022 · 2022
Cited alongside, same era.
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, Claire Cui, Olivier Bousquet, Quoc Le, et al. 2022 · 2022
Cited alongside, same era.
Graph of thoughts: Solving elaborate problems with large language models
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Michal Podstawski, Hubert Niewiadomski, Piotr Nyczyk, et al. 2023 · 2023
Cited alongside, same era.
Large language models as tool makers
Tianle Cai, Xuezhi Wang, Tengyu Ma, Xinyun Chen, and Denny Zhou. 2023 · 2023
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Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al. 2022a
Cited in the paper.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022b
Cited in the paper.
Fangzhi Xu, Qika Lin, Jiawei Han, Tianzhe Zhao, Jun Liu, and Erik Cambria. 2023a
Cited in the paper.
Yudong Xu, Wenhao Li, Pashootan Vaezipoor, Scott Sanner, and Elias B Khalil. 2023b
Cited in the paper.
Cheng Qian, Chi Han, Yi Fung, Yujia Qin, Zhiyuan Liu, and Heng Ji. 2023 · 2023
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Large language models are in-context semantic reasoners rather than symbolic reasoners
Xiaojuan Tang, Zilong Zheng, Jiaqi Li, Fanxu Meng, Song-Chun Zhu, Yitao Liang, and Muhan Zhang. 2023 · 2023
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Zhaofeng Wu, Linlu Qiu, Alexis Ross, Ekin Akyürek, Boyuan Chen, Bailin Wang, Najoung Kim, Jacob Andreas, and Yoon Kim. 2023 · 2023
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik Narasimhan. 2023 · 2023
Later among the works it cites.