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Math reasoning is becoming an ever increasing area of focus as we scale large language models.
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
Earlier work this paper cites.
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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Evaluating large language models trained on code, 2021
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri 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, Josh 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
Earlier work this paper cites.
Star: Bootstrapping reasoning with reasoning
Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah D. Goodman · 2022
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Solving quantitative reasoning problems with language models
Aitor Lewkowycz, Anders Johan Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Venkatesh Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur, Guy Gur-Ari, and Vedant Misra · 2022
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Time spent thinking in online chess reflects the value of computation, Oct 2022
Evan Russek, Daniel Acosta-Kane, Bas van Opheusden, Marcelo G Mattar, and Tom Griffiths · 2022
Earlier work this paper cites.
Rephrase and respond: Let large language models ask better questions for themselves
Yihe Deng, Weitong Zhang, Zixiang Chen, and Quanquan Gu · 2023
Earlier work this paper cites.
Xwin-lm, 9 2023
Xwin-LM Team · 2023
Earlier work this paper cites.
Openwebmath: An open dataset of high-quality mathematical web text, 2023
Keiran Paster, Marco Dos Santos, Zhangir Azerbayev, and Jimmy Ba · 2023
Earlier work this paper cites.
Redpajama: an open dataset for training large language models, Oct 2023
Together Computer · 2023
Earlier work this paper cites.
Testing language models on a held-out high school national finals exam
Keiran Paster · 2023
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context, 2024
Gemini Team · 2024
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Writing good multiple choice test questions, 2024
Cynthia J. Brame · 2024
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Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al · 2024
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Data diversity matters for robust instruction tuning, 2024
Alexander Bukharin, Shiyang Li, Zhengyang Wang, Jingfeng Yang, Bing Yin, Xian Li, Chao Zhang, Tuo Zhao, and Haoming Jiang · 2024
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V-star: Training verifiers for self-taught reasoners
Arian Hosseini, Xingdi Yuan, Nikolay Malkin, Aaron Courville, Alessandro Sordoni, and Rishabh Agarwal · 2024
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Keep guessing? when considering inference scaling, mind the baselines, 2024
Gal Yona, Or Honovich, Omer Levy, and Roee Aharoni · 2024
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Meng Fang, Xiangpeng Wan, Fei Lu, Fei Xing, and Kai Zou · 2024
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Quantifying variance in evaluation benchmarks, 2024
Lovish Madaan, Aaditya K. Singh, Rylan Schaeffer, Andrew Poulton, Sanmi Koyejo, Pontus Stenetorp, Sharan Narang, and Dieuwke Hupkes · 2024
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Large language monkeys: Scaling inference compute with repeated sampling, 2024
Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V. Le, Christopher Ré, and Azalia Mirhoseini · 2024
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Changing answer order can decrease mmlu accuracy, 2024
Vipul Gupta, David Pantoja, Candace Ross, Adina Williams, and Megan Ung · 2024
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Omni-math: A universal olympiad level mathematic benchmark for large language models, 2024
Bofei Gao, Feifan Song, Zhe Yang, Zefan Cai, Yibo Miao, Qingxiu Dong, Lei Li, Chenghao Ma, Liang Chen, Runxin Xu, Zhengyang Tang, Benyou Wang, Daoguang Zan, Shanghaoran Quan, Ge Zhang, Lei Sha, Yichang Zhang, Xuancheng Ren, Tianyu Liu, and Baobao Chang · 2024
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Testing information, 2024
HMMT · 2024
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Aidar Myrzakhan, Sondos Mahmoud Bsharat, and Zhiqiang Shen · 2024
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Chatgpt, 2024a
OpenAI
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Learning to reason with llms, 2024b
OpenAI
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Hugh Zhang, Jeff Da, Dean Lee, Vaughn Robinson, Catherine Wu, Will Song, Tiffany Zhao, Pranav Raja, Dylan Slack, Qin Lyu, Sean Hendryx, Russell Kaplan, Michele Lunati, and Summer Yue · 2024
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Scale’s seal research lab launches expert-evaluated llm leaderboards, 2024
ScaleAI · 2024
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Frontiermath: A benchmark for evaluating advanced mathematical reasoning in ai, 2024
Elliot Glazer, Ege Erdil, Tamay Besiroglu, Diego Chicharro, Evan Chen, Alex Gunning, Caroline Falkman Olsson, Jean-Stanislas Denain, Anson Ho, Emily de Oliveira Santos, Olli Järviniemi, Matthew Barnett, Robert Sandler, Matej Vrzala, Jaime Sevilla, Qiuyu Ren, Elizabeth Pratt, Lionel Levine, Grant Barkley, Natalie Stewart, Bogdan Grechuk, Tetiana Grechuk, Shreepranav Varma Enugandla, and Mark Wildon · 2024
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Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold, 2024
Amrith Setlur, Saurabh Garg, Xinyang Geng, Naman Garg, Virginia Smith, and Aviral Kumar · 2024
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Language models show human-like content effects on reasoning tasks, 2024
Ishita Dasgupta, Andrew K. Lampinen, Stephanie C. Y. Chan, Hannah R. Sheahan, Antonia Creswell, Dharshan Kumaran, James L. McClelland, and Felix Hill · 2024
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Amc historical statistics, 2024
MAA · 2024
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