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Language models have achieved remarkable performance on a wide range of tasks that require natural language understanding.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · 1901
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Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J. (2019) · 1910
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Hol light: A tutorial introduction
Harrison, J. (1996) · 1996
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leancop 2.0 and ileancop 1.2: High performance lean theorem proving in classical and intuitionistic logic (system descriptions)
Otten, J. (2008) · 2008
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The isabelle framework
Wenzel, M., Paulson, L. C., and Nipkow, T. (2008) · 2008
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Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J. (2020) · 2009
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Generative language modeling for automated theorem proving
Polu, S. and Sutskever, I. (2020) · 2009
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Mizar in a nutshell
Grabowski, A., Kornilowicz, A., and Naumowicz, A. (2010) · 2010
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First-order theorem proving and Vampire
Kovács, L. and Voronkov, A. (2013) · 2013
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System Description: E 1.8
Schulz, S. (2013) · 2013
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Learning to solve arithmetic word problems with verb categorization
Hosseini, M. J., Hajishirzi, H., Etzioni, O., and Kushman, N. (2014) · 2014
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The lean theorem prover (system description)
de Moura, L. M., Kong, S., Avigad, J., van Doorn, F., and von Raumer, J. (2015) · 2015
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Parsing algebraic word problems into equations
Koncel-Kedziorski, R., Hajishirzi, H., Sabharwal, A., Etzioni, O., and Ang, S. D. (2015) · 2015
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Deepmath - Deep Sequence Models for Premise Selection
Alemi, A. A., Chollet, F., Een, N., Irving, G., Szegedy, C., and Urban, J. (2016) · 2016
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Sympy: symbolic computing in python
Meurer, A., Smith, C. P., Paprocki, M., Čertík, O., Kirpichev, S. B., Rocklin, M., Kumar, A., Ivanov, S., Moore, J. K., Singh, S., Rathnayake, T., Vig, S., Granger, B. E., Muller, R. P., Bonazzi, F., Gupta, H., Vats, S., Johansson, F., Pedregosa, F., Curry, M. J., Terrel, A. R., Roučka, v., Saboo, A., Fernando, I., Kulal, S., Cimrman, R., and Scopatz, A. (2017) · 2017
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Reinforcement learning of theorem proving
Kaliszyk, C., Urban, J., Michalewski, H., and Olšák, M. (2018) · 2018
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A simple method for commonsense reasoning
Trinh, T. H. and Le, Q. V. (2018) · 2018
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The curious case of neural text degeneration
Holtzman, A., Buys, J., Du, L., Forbes, M., and Choi, Y. (2019) · 2019
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Metamath: A Computer Language for Pure Mathematics
Megill, N. D. and Wheeler, D. A. (2019) · 2019
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al. (2019) · 2019
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Program synthesis with large language models
Austin, J., Odena, A., Nye, M., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C., Terry, M., Le, Q., et al. (2021) · 2021
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LIME: learning inductive bias for primitives of mathematical reasoning
Wu, Y., Rabe, M. N., Li, W., Ba, J., Grosse, R. B., and Szegedy, C. (2021) · 2021
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Quantifying memorization across neural language models
Carlini, N., Ippolito, D., Jagielski, M., Lee, K., Tramer, F., and Zhang, C. (2022) · 2022
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Palm: Scaling language modeling with pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A., Barnes, P., Tay, Y., Shazeer, N., Prabhakaran, V., Reif, E., Du, N., Hutchinson, B., Pope, R., Bradbury, J., Austin, J., Isard, M., Gur-Ari, G., Yin, P., Duke, T., Levskaya, A., Ghemawat, S., Dev, S., Michalewski, H., Garcia, X., Misra, V., Robinson, K., Fedus, L., Zhou, D., Ippolito, D., Luan, D., Lim, H., Zoph, B., Spiridonov, A., Sepassi, R., Dohan, D., Agrawal, S., Omernick, M., Dai, A. M., Pillai, T. S., Pellat, M., Lewkowycz, A., Moreira, E., Child, R., Polozov, O., Lee, K., Zhou, Z., Wang, X., Saeta, B., Diaz, M., Firat, O., Catasta, M., Wei, J., Meier-Hellstern, K., Eck, D., Dean, J., Petrov, S., and Fiedel, N. (2022) · 2022
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The Coq reference manual
development team, T. C. (2022) · 2022
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Proof artifact co-training for theorem proving with language models
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Isarstep: a benchmark for high-level mathematical reasoning
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Training compute-optimal large language models
Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. d. L., Hendricks, L. A., Welbl, J., Clark, A., Hennigan, T., Noland, E., Millican, K., Driessche, G. v. d., Damoc, B., Guy, A., Osindero, S., Simonyan, K., Elsen, E., Rae, J. W., Vinyals, O., and Sifre, L. (2022) · 2022
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Thor: Wielding hammers to integrate language models and automated theorem provers
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Competition-level code generation with alphacode
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Formal mathematics statement curriculum learning
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Scaling up models and data with t5x
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Chain of thought prompting elicits reasoning in large language models
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Autoformalization with large language models
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