Fetching the paper…
Reading the bibliography…
Automated theorem proving (ATP) has become an appealing domain for exploring the reasoning ability of the recent successful generative language models.
Learning to reason in large theories without imitation
Kshitij Bansal, Sarah M. Loos, Markus N. Rabe, and Christian Szegedy. 2019b · 1905
Earlier work this paper cites.
A model of two-player evaluation functions
Bruce Abramson and Richard E Korf. 1987 · 1987
Earlier work this paper cites.
A neural probabilistic language model
Yoshua Bengio, Réjean Ducharme, and Pascal Vincent. 2000 · 2000
Earlier work this paper cites.
Generative language modeling for automated theorem proving
Stanislas Polu and Ilya Sutskever. 2020 · 2009
Earlier work this paper cites.
The lean theorem prover (system description)
Leonardo de Moura, Soonho Kong, Jeremy Avigad, Floris van Doorn, and Jakob von Raumer. 2015 · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
Minif2f: a cross-system benchmark for formal olympiad-level mathematics
Kunhao Zheng, Jesse Michael Han, and Stanislas Polu. 2022 · 2015
Earlier work this paper cites.
MAWPS: A math word problem repository
Rik Koncel-Kedziorski, Subhro Roy, Aida Amini, Nate Kushman, and Hannaneh Hajishirzi. 2016 · 2016
Earlier work this paper cites.
Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al. 2017 · 2017
Earlier work this paper cites.
Deep neural solver for math word problems
Yan Wang, Xiaojiang Liu, and Shuming Shi. 2017 · 2017
Earlier work this paper cites.
Metamath: a computer language for mathematical proofs
Norman Megill and David A Wheeler. 2019 · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
Earlier work this paper cites.
Analysing mathematical reasoning abilities of neural models
David Saxton, Edward Grefenstette, Felix Hill, and Pushmeet Kohli. 2019 · 2019
Earlier work this paper cites.
Learning to prove theorems via interacting with proof assistants
Kaiyu Yang and Jia Deng. 2019 · 2019
Cited alongside, same era.
The lean mathematical library
mathlib. 2020 · 2020
Cited alongside, same era.
Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. 2021 · 2021
Cited alongside, same era.
Proof artifact co-training for theorem proving with language models
Jesse Michael Han, Jason Rute, Yuhuai Wu, Edward Ayers, and Stanislas Polu. 2021 · 2021
Cited alongside, same era.
Measuring mathematical problem solving with the MATH dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021 · 2021
Cited alongside, same era.
Expression syntax information bottleneck for math word problems
Jing Xiong, Chengming Li, Min Yang, Xiping Hu, and Bin Hu. 2022 · 2022
Later among the works it cites.
Draft, sketch, and prove: Guiding formal theorem provers with informal proofs
Albert Qiaochu Jiang, Sean Welleck, Jin Peng Zhou, Timothée Lacroix, Jiacheng Liu, Wenda Li, Mateja Jamnik, Guillaume Lample, and Yuhuai Wu. 2023 · 2023
Closest in time.
Fimo: A challenge formal dataset for automated theorem proving
Chengwu Liu, Jianhao Shen, Huajian Xin, Zhengying Liu, Ye Yuan, Haiming Wang, Wei Ju, Chuanyang Zheng, Yichun Yin, Lin Li, et al. 2023 · 2023
Closest in time.
Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al. 2023 · 2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Wenda Li, Lei Yu, Yuhuai Wu, and Lawrence C. Paulson. 2021 · 2021
Cited alongside, same era.
Inter-GPS: Interpretable geometry problem solving with formal language and symbolic reasoning
Pan Lu, Ran Gong, Shibiao Jiang, Liang Qiu, Siyuan Huang, Xiaodan Liang, and Song-Chun Zhu. 2021 · 2021
Cited alongside, same era.
Generate & rank: A multi-task framework for math word problems
Jianhao Shen, Yichun Yin, Lin Li, Lifeng Shang, Xin Jiang, Ming Zhang, and Qun Liu. 2021 · 2021
Cited alongside, same era.
Naturalproofs: Mathematical theorem proving in natural language
Sean Welleck, Jiacheng Liu, Ronan Le Bras, Hanna Hajishirzi, Yejin Choi, and Kyunghyun Cho. 2021 · 2021
Cited alongside, same era.
Thor: Wielding hammers to integrate language models and automated theorem provers
Albert Qiaochu Jiang, Wenda Li, Szymon Tworkowski, Konrad Czechowski, Tomasz Odrzygóźdź, Piotr Miłoś, Yuhuai Wu, and Mateja Jamnik. 2022 · 2022
Cited alongside, same era.
Hypertree proof search for neural theorem proving
Guillaume Lample, Timothée Lacroix, Marie-Anne Lachaux, Aurélien Rodriguez, Amaury Hayat, Thibaut Lavril, Gabriel Ebner, and Xavier Martinet. 2022 · 2022
Cited alongside, same era.
Autoformalization with large language models
Yuhuai Wu, Albert Qiaochu Jiang, Wenda Li, Markus Rabe, Charles Staats, Mateja Jamnik, and Christian Szegedy. 2022 · 2022
Cited alongside, same era.
OpenAI. 2023 · 2023
Closest in time.
Formal mathematics statement curriculum learning
Stanislas Polu, Jesse Michael Han, Kunhao Zheng, Mantas Baksys, Igor Babuschkin, and Ilya Sutskever. 2023 · 2023
Closest in time.
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 · 2023
Closest in time.
Dq-lore: Dual queries with low rank approximation re-ranking for in-context learning
Jiong Xiong, Zixuan Li, Chuanyang Zheng, Zhijiang Guo, Yichun Yin, Enze Xie, Zhicheng Yang, Qingxing Cao, Haiming Wang, Xiongwei Han, Jing Tang, Chengming Li, and Xiaodan Liang. 2023 · 2023
Closest in time.
Leandojo: Theorem proving with retrieval-augmented language models
Kaiyu Yang, Aidan M Swope, Alex Gu, Rahul Chalamala, Peiyang Song, Shixing Yu, Saad Godil, Ryan Prenger, and Anima Anandkumar. 2023 · 2023
Closest in time.
Metamath: Bootstrap your own mathematical questions for large language models
Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T Kwok, Zhenguo Li, Adrian Weller, and Weiyang Liu. 2023 · 2023
Closest in time.
Lyra: Orchestrating dual correction in automated theorem proving
Chuanyang Zheng, Haiming Wang, Enze Xie, Zhengying Liu, Jiankai Sun, Huajian Xin, Jianhao Shen, Zhenguo Li, and Yu Li. 2023 · 2023
Closest in time.
Are NLP models really able to solve simple math word problems?
Arkil Patel, Satwik Bhattamishra, and Navin Goyal. 2021 · 2094
Closest in time.