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Autoformalization is the task of translating natural language materials into machine-verifiable formalisations.
Two new evaluation datasets for low-resource machine translation: Nepali-english and sinhala-english
Francisco Guzmán, Peng-Jen Chen, Myle Ott, Juan Miguel Pino, Guillaume Lample, Philipp Koehn, Vishrav Chaudhary, and Marc’Aurelio Ranzato · 1902
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Principia Mathematica
Alfred North Whitehead and Bertrand Russell · 1927
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Isabelle - A Generic Theorem Prover (with a contribution by T. Nipkow) , volume 828 of Lecture Notes in Computer Science
Lawrence C. Paulson · 1994
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HOL light: A tutorial introduction
John Harrison · 1996
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The coq proof assistant reference manual
Bruno Barras, Samuel Boutin, Cristina Cornes, Judicaël Courant, Yann Coscoy, David Delahaye, Daniel de Rauglaudre, Jean-Christophe Filliâtre, Eduardo Giménez, Hugo Herbelin, et al · 1999
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GLU variants improve transformer
Noam Shazeer · 2002
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Automatic translation in formalized mathematics
Grzegorz Bancerek · 2006
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Proof claimed for deep connection between primes
Peter Ball · 2012
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The lean theorem prover (system description)
Leonardo Mendonça de Moura, Soonho Kong, Jeremy Avigad, Floris van Doorn, and Jakob von Raumer · 2015
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Professor forcing: A new algorithm for training recurrent networks
Anirudh Goyal, Alex Lamb, Ying Zhang, Saizheng Zhang, Aaron C. Courville, and Yoshua Bengio · 2016
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Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch · 2016
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Unsupervised neural machine translation
Mikel Artetxe, Gorka Labaka, Eneko Agirre, and Kyunghyun Cho · 2018
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Unsupervised machine translation using monolingual corpora only
Guillaume Lample, Alexis Conneau, Ludovic Denoyer, and Marc’Aurelio Ranzato · 2018
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Why abc is still a conjecture
Peter Scholze and Jakob Stix · 2018
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First experiments with neural translation of informal to formal mathematics
Qingxiang Wang, Cezary Kaliszyk, and Josef Urban · 2018
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Roformer: Enhanced transformer with rotary position embedding
Jianlin Su, Yu Lu, Shengfeng Pan, Bo Wen, and Yunfeng Liu · 2021
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Learning plausible and useful conjectures
Albert Q Jiang, Wenda Li, and Mateja Jamnik · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, and Ryan Lowe · 2022
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Autoformalization with large language models
Yuhuai Wu, Albert Qiaochu Jiang, Wenda Li, Markus N. Rabe, Charles Staats, Mateja Jamnik, and Christian Szegedy · 2022
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minif2f: a cross-system benchmark for formal olympiad-level mathematics
Kunhao Zheng, Jesse Michael Han, and Stanislas Polu · 2022
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Root mean square layer normalization
Biao Zhang and Rico Sennrich · 2019
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Exploration of neural machine translation in autoformalization of mathematics in mizar
Qingxiang Wang, Chad E. Brown, Cezary Kaliszyk, and Josef Urban · 2020
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Pondé de Oliveira Pinto, Jared Kaplan, Harrison Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Joshua Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba · 2021
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Lisa: Language models of isabelle proofs
Albert Qiaochu Jiang, Wenda Li, Jesse Michael Han, and Yuhuai Wu · 2021
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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, Timothée Lacroix, Yuhuai Wu, and Guillaume Lample
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample
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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
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Evaluating language models for mathematics through interactions
Katherine M. Collins, Albert Q. Jiang, Simon Frieder, Lionel Wong, Miri Zilka, Umang Bhatt, Thomas Lukasiewicz, Yuhuai Wu, Joshua B. Tenenbaum, William Hart, Timothy Gowers, Wenda Li, Adrian Weller, and Mateja Jamnik · 2023
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Easylm: A simple and scalable training framework for large language models, March 2023
Xinyang Geng · 2023
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OpenAI · 2023
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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
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