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Large Language Models (LLMs) have demonstrated remarkable performance in solving math problems, a hallmark of human intelligence.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
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Analysing mathematical reasoning abilities of neural models
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Math word question generation for training the students with learning difficulties
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Personalized mathematical word problem generation
Oleksandr Polozov, Eleanor O’Rourke, Adam M Smith, Luke Zettlemoyer, Sumit Gulwani, and Zoran Popović. 2015 · 2015
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A tag-based english math word problem solver with understanding, reasoning and explanation
Chao-Chun Liang, Kuang-Yi Hsu, Chien-Tsung Huang, Chung-Min Li, Shen-Yu Miao, and Keh-Yih Su. 2016 · 2016
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Program induction by rationale generation: Learning to solve and explain algebraic word problems
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Annotating derivations: A new evaluation strategy and dataset for algebra word problems
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Mapping to declarative knowledge for word problem solving
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A diverse corpus for evaluating and developing English math word problem solvers
Shen-yun Miao, Chao-Chun Liang, and Keh-Yih Su. 2020 · 2020
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Training verifiers to solve math word problems
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Show your work: Scratchpads for intermediate computation with language models
Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, et al. 2021 · 2021
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Are nlp models really able to solve simple math word problems?
Arkil Patel, Satwik Bhattamishra, and Navin Goyal. 2021 · 2021
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Solving math word problems by combining language models with symbolic solvers
Joy He-Yueya, Gabriel Poesia, Rose E Wang, and Noah D Goodman. 2023 · 2023
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Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 2023 · 2023
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Plan, verify and switch: Integrated reasoning with diverse x-of-thoughts
Tengxiao Liu, Qipeng Guo, Yuqing Yang, Xiangkun Hu, Yue Zhang, Xipeng Qiu, and Zheng Zhang. 2023 · 2023
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Wizardmath: Empowering mathematical reasoning for large language models via reinforced evol-instruct
Haipeng Luo, Qingfeng Sun, Can Xu, Pu Zhao, Jianguang Lou, Chongyang Tao, Xiubo Geng, Qingwei Lin, Shifeng Chen, and Dongmei Zhang. 2023 · 2023
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Math word problem generation with mathematical consistency and problem context constraints
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Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen. 2022 · 2022
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Compositional semantic parsing with large language models
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Pal: Program-aided language models
Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig. 2022 · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
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Solving quantitative reasoning problems with language models
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Reasoning with language model prompting: A survey
Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, and Huajun Chen. 2022 · 2022
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Self-consistency improves chain of thought reasoning in language models
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Rewriting math word problems with large language models
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Openwebmath: An open dataset of high-quality mathematical web text
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Gemini: a family of highly capable multimodal models
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Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models
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Large language models as analogical reasoners
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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
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Mammoth: Building math generalist models through hybrid instruction tuning
Xiang Yue, Xingwei Qu, Ge Zhang, Yao Fu, Wenhao Huang, Huan Sun, Yu Su, and Wenhu Chen. 2023 · 2023
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Openmathinstruct-1: A 1.8 million math instruction tuning dataset
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