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Traditional language model-based theorem proving assumes that by training on a sufficient amount of formal proof data, a model will learn to prove theorems.
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Sascha Bohme and Tobias Nipkow · 2010
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Language models are few-shot learners
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Generative language modeling for automated theorem proving
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Markus N Rabe, Dennis Lee, Kshitij Bansal, and Christian Szegedy · 2020
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A promising path towards autoformalization and general artificial intelligence
Christian Szegedy · 2020
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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
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Proof artifact co-training for theorem proving with language models
Jesse Michael Han, Jason Rute, Yuhuai Wu, Edward W Ayers, and Stanislas Polu · 2021
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Measuring mathematical problem solving with the math dataset
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Show your work: Scratchpads for intermediate computation with language models
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Naturalproofs: Mathematical theorem proving in natural language
Sean Welleck, Jiacheng Liu, Ronan Le Bras, Hannaneh Hajishirzi, Yejin Choi, and Kyunghyun Cho · 2021
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Lime: Learning inductive bias for primitives of mathematical reasoning
Yuhuai Wu, Markus N Rabe, Wenda Li, Jimmy Ba, Roger B Grosse, and Christian Szegedy · 2021
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Kunhao Zheng, Jesse Michael Han, and Stanislas Polu · 2021
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Towards a mathematics formalisation assistant using large language models
Baldur: Whole-proof generation and repair with large language models
Emily First, Markus Rabe, Talia Ringer, and Yuriy Brun · 2023
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Temperature-scaled large language models for lean proofstep prediction
Fabian Gloeckle, Baptiste Roziere, Amaury Hayat, and Gabriel Synnaeve · 2023
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Artificial intelligence to assist mathematical reasoning: Proceedings of a workshop, 2023
National Academies of Sciences · 2023
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OpenAI: GPT-4, 2023
OpenAI · 2023
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Beyond human data: Scaling self-training for problem-solving with language models
Avi Singh, John D Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Peter J Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron Parisi, et al · 2023
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Ayush Agrawal, Siddhartha Gadgil, Navin Goyal, Ashvni Narayanan, and Anand Tadipatri · 2022
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Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen · 2022
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Proof artifact co-training for theorem proving with language models
Jesse Michael Han, Jason Rute, Yuhuai Wu, Edward Ayers, and Stanislas Polu · 2022
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Draft, sketch, and prove: Guiding formal theorem provers with informal proofs
Albert Q Jiang, Sean Welleck, Jin Peng Zhou, Wenda Li, Jiacheng Liu, Mateja Jamnik, Timothee Lacroix, Yuhuai Wu, and Guillaume Lample · 2022
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Hypertree proof search for neural theorem proving
Guillaume Lample, Timothee Lacroix, Marie-Anne Lachaux, Aurelien Rodriguez, Amaury Hayat, Thibaut Lavril, Gabriel Ebner, and Xavier Martinet · 2022
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Formal mathematics statement curriculum learning
Stanislas Polu, Jesse Michael Han, Kunhao Zheng, Mantas Baksys, Igor Babuschkin, and Ilya Sutskever · 2022
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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 · 2022
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Naturalprover: Grounded mathematical proof generation with language models
Sean Welleck, Jiacheng Liu, Ximing Lu, Hannaneh Hajishirzi, and Yejin Choi · 2022
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Terence Tao · 2023
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A language-agent approach to formal theorem-proving
Amitayush Thakur, Yeming Wen, and Swarat Chaudhuri · 2023
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Llmstep: Llm proofstep suggestions in lean
Sean Welleck and Rahul Saha · 2023
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Lego-prover: Neural theorem proving with growing libraries
Huajian Xin, Haiming Wang, Chuanyang Zheng, Lin Li, Zhengying Liu, Qingxing Cao, Yinya Huang, Jing Xiong, Han Shi, Enze Xie, et al · 2023
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LeanDojo: Theorem proving with retrieval-augmented language models
Kaiyu Yang, Aidan Swope, Alex Gu, Rahul Chalamala, Peiyang Song, Shixing Yu, Saad Godil, Ryan Prenger, and Anima Anandkumar · 2023
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Lean in 2024
Kevin Buzzard · 2024
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Mustard: Mastering uniform synthesis of theorem and proof data
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A survey on deep learning for theorem proving, 2024
Zhaoyu Li, Jialiang Sun, Logan Murphy, Qidong Su, Zenan Li, Xian Zhang, Kaiyu Yang, and Xujie Si · 2024
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An in-context learning agent for formal theorem-proving, 2024
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Internlm-math: Open math large language models toward verifiable reasoning, 2024
Huaiyuan Ying, Shuo Zhang, Linyang Li, Zhejian Zhou, Yunfan Shao, Zhaoye Fei, Yichuan Ma, Jiawei Hong, Kuikun Liu, Ziyi Wang, Yudong Wang, Zijian Wu, Shuaibin Li, Fengzhe Zhou, Hongwei Liu, Songyang Zhang, Wenwei Zhang, Hang Yan, Xipeng Qiu, Jiayu Wang, Kai Chen, and Dahua Lin · 2024
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Decomposing the enigma: Subgoal-based demonstration learning for formal theorem proving, 2024
Xueliang Zhao, Wenda Li, and Lingpeng Kong · 2024
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Teach language models to reason
Denny Zhou · 2024
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