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Solving mathematical problems requires advanced reasoning abilities and presents notable challenges for large language models.
Slice sampling
Radford M Neal · 2003
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Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Tensorfloat-32 in the a100 gpu accelerates ai training, hpc up to 20x, 2020
NVIDIA · 2020
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Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He · 2020
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Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He · 2020
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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
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Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al · 2022
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Large language models can self-improve
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Beyond neural scaling laws: beating power law scaling via data pruning
Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, and Ari Morcos · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou · 2022
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Star: Bootstrapping reasoning with reasoning
Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah Goodman · 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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Common 7b language models already possess strong math capabilities
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