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Logical reasoning, i.e., deductively inferring the truth value of a conclusion from a set of premises, is an important task for artificial intelligence with wide potential impacts on science, mathematics, and society.
Language models are few-shot learners
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Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars
Luke S Zettlemoyer and Michael Collins. 2005 · 2005
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An introduction to non-classical logic: From if to is
Graham Priest. 2008 · 2008
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Learning dependency-based compositional semantics
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DeepProbLog: Neural probabilistic logic programming
Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, and Luc De Raedt. 2018 · 2018
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First experiments with neural translation of informal to formal mathematics
Qingxiang Wang, Cezary Kaliszyk, and Josef Urban. 2018 · 2018
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A survey on semantic parsing
Aishwarya Kamath and Rajarshi Das. 2019 · 2019
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Integrating learning and reasoning with deep logic models
Giuseppe Marra, Francesco Giannini, Michelangelo Diligenti, and Marco Gori. 2019 · 2019
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Exploration of neural machine translation in autoformalization of mathematics in mizar
Qingxiang Wang, Chad Brown, Cezary Kaliszyk, and Josef Urban. 2020 · 2020
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Towards bridging the neuro-symbolic gap: Deep deductive reasoners
Monireh Ebrahimi, Aaron Eberhart, Federico Bianchi, and Pascal Hitzler. 2021 · 2021
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Show your work: Scratchpads for intermediate computation with language models
Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, et al. 2021 · 2021
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ProofWriter: Generating implications, proofs, and abductive statements over natural language
Oyvind Tafjord, Bhavana Dalvi, and Peter Clark. 2021 · 2021
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Exploring length generalization in large language models
Cem Anil, Yuhuai Wu, Anders Andreassen, Aitor Lewkowycz, Vedant Misra, Vinay Ramasesh, Ambrose Slone, Guy Gur-Ari, Ethan Dyer, and Behnam Neyshabur. 2022 · 2022
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Efficient training of language models to fill in the middle
Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, John Schulman, Christine McLeavey, Jerry Tworek, and Mark Chen. 2022 · 2022
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Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen. 2022 · 2022
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Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2022 · 2022
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David Dohan, Winnie Xu, Aitor Lewkowycz, Jacob Austin, David Bieber, Raphael Gontijo Lopes, Yuhuai Wu, Henryk Michalewski, Rif A Saurous, Jascha Sohl-Dickstein, et al. 2022 · 2022
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A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level
Iddo Drori, Sarah Zhang, Reece Shuttleworth, Leonard Tang, Albert Lu, Elizabeth Ke, Kevin Liu, Linda Chen, Sunny Tran, Newman Cheng, et al. 2022 · 2022
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ProofNet: Autoformalizing and formally proving undergraduate-level mathematics
Zhangir Azerbayev, Bartosz Piotrowski, Hailey Schoelkopf, Edward W Ayers, Dragomir Radev, and Jeremy Avigad. 2023 · 2023
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Binding language models in symbolic languages
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Selection-inference: Exploiting large language models for interpretable logical reasoning
Antonia Creswell, Murray Shanahan, and Irina Higgins. 2023 · 2023
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Faith and fate: Limits of transformers on compositionality
Nouha Dziri, Ximing Lu, Melanie Sclar, Xiang Lorraine Li, Liwei Jian, Bill Yuchen Lin, Peter West, Chandra Bhagavatula, Ronan Le Bras, Jena D Hwang, et al. 2023 · 2023
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PAL: Program-aided language models
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Andrew Drozdov, Nathanael Schärli, Ekin Akyürek, Nathan Scales, Xinying Song, Xinyun Chen, Olivier Bousquet, and Denny Zhou. 2022 · 2022
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Formal specifications from natural language
Christopher Hahn, Frederik Schmitt, Julia J Tillman, Niklas Metzger, Julian Siber, and Bernd Finkbeiner. 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
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Towards reasoning in large language models: A survey
Jie Huang and Kevin Chen-Chuan Chang. 2022 · 2022
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A generalist neural algorithmic learner
Borja Ibarz, Vitaly Kurin, George Papamakarios, Kyriacos Nikiforou, Mehdi Bennani, Róbert Csordás, Andrew Joseph Dudzik, Matko Bošnjak, Alex Vitvitskyi, Yulia Rubanova, et al. 2022 · 2022
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The Stack: 3 TB of permissively licensed source code
Denis Kocetkov, Raymond Li, Loubna Ben Allal, Jia Li, Chenghao Mou, Carlos Muñoz Ferrandis, Yacine Jernite, Margaret Mitchell, Sean Hughes, Thomas Wolf, et al. 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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Holistic evaluation of language models
Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, et al. 2022 · 2022
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Large language models cannot self-correct reasoning yet
Jie Huang, Xinyun Chen, Swaroop Mishra, Huaixiu Steven Zheng, Adams Wei Yu, Xinying Song, and Denny Zhou. 2023 · 2023
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LAMBADA: Backward chaining for automated reasoning in natural language
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Mind’s eye: Grounded language model reasoning through simulation
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Augmented language models: a survey
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Is self-repair a silver bullet for code generation?
Theo X Olausson, Jeevana Priya Inala, Chenglong Wang, Jianfeng Gao, and Armando Solar-Lezama. 2023 · 2023
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GPT-4 technical report
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Logic-lm: Empowering large language models with symbolic solvers for faithful logical reasoning
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Testing the general deductive reasoning capacity of large language models using ood examples
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Toolformer: Language models can teach themselves to use tools
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
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Large language models as analogical reasoners
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Least-to-most prompting enables complex reasoning in large language models
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