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There has been considerable divergence of opinion on the reasoning abilities of Large Language Models (LLMs).
Artificial Intelligence: A Modern Approach (4th Edition)
Stuart Russell and Peter Norvig · 2020
Earlier work this paper cites.
Inner monologue: Embodied reasoning through planning with language models, 2022
Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson, Igor Mordatch, Yevgen Chebotar, Pierre Sermanet, Noah Brown, Tomas Jackson, Linda Luu, Sergey Levine, Karol Hausman, and Brian Ichter · 2022
Earlier work this paper cites.
Large Language Models are Zero-Shot Reasoners
Takeshi Kojima, S. Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
Earlier work this paper cites.
Gpt-4 can’t reason
Konstantine Arkoudas · 2023
Earlier work this paper cites.
Sparks of artificial general intelligence: Early experiments with gpt-4, 2023
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, and Yi Zhang · 2023
Earlier work this paper cites.
Teaching large language models to self-debug, 2023
Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou · 2023
Earlier work this paper cites.
Faith and Fate: Limits of Transformers on Compositionality
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, and Yejin Choi · 2023
Cited alongside, same era.
Large language models are not abstract reasoners, 2023
Gaël Gendron, Qiming Bao, Michael Witbrock, and Gillian Dobbie · 2023
Cited alongside, same era.
Can LLMs Really Reason and Plan?, 2023
Subbarao Kambhampati · 2023
Cited alongside, same era.
Deductive verification of chain-of-thought reasoning
Zhan Ling, Yunhao Fang, Xuanlin Li, Zhiao Huang, Mingu Lee, Roland Memisevic, and Hao Su · 2023
Cited alongside, same era.
Self-refine: Iterative refinement with self-feedback, 2023
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhsh, and Peter Clark · 2023
Cited alongside, same era.
ARB: Advanced Reasoning Benchmark for Large Language Models
Tomohiro Sawada, Daniel Paleka, Alexander Havrilla, Pranav Tadepalli, Paula Vidas, Alexander Kranias, John J. Nay, Kshitij Gupta, and Aran Komatsuzaki · 2023
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Reflexion: Language Agents with Verbal Reinforcement Learning, June 2023
Noah Shinn, Federico Cassano, Beck Labash, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao · 2023
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Karthik Valmeekam, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati · 2023
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Large language models are better reasoners with self-verification, 2023
Yixuan Weng, Minjun Zhu, Fei Xia, Bin Li, Shizhu He, Kang Liu, and Jun Zhao · 2023
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Tree of thoughts: Deliberate problem solving with large language models, 2023
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan · 2023
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Embers of autoregression: Understanding large language models through the problem they are trained to solve, 2023
R. Thomas McCoy, Shunyu Yao, Dan Friedman, Matthew Hardy, and Thomas L. Griffiths · 2023
Cited alongside, same era.
Archive available at http://archive.dimacs.rutgers.edu/Challenges/
DIMACS Implementation Challenges
Cited in the paper.
Graph Coloring Instances
Michael Trick
Cited in the paper.
Closest in time.
React: Synergizing reasoning and acting in language models, 2023
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao · 2023
Closest in time.