Fetching the paper…
Reading the bibliography…
Math word problem (MWP) solving requires generating a reasoning path based on a given problem description that often contains irrelevant conditions.
Learning to solve arithmetic word problems with verb categorization
Mohammad Javad Hosseini, Hannaneh Hajishirzi, Oren Etzioni, and Nate Kushman. 2014 · 2014
Earlier work this paper cites.
Parsing algebraic word problems into equations
Rik Koncel-Kedziorski, Hannaneh Hajishirzi, Ashish Sabharwal, Oren Etzioni, and Siena Dumas Ang. 2015 · 2015
Earlier work this paper cites.
Solving general arithmetic word problems
Subhro Roy and Dan Roth. 2015 · 2015
Earlier work this paper cites.
Reasoning about quantities in natural language
Subhro Roy, Tim Vieira, and Dan Roth. 2015 · 2015
Earlier work this paper cites.
Learn to solve algebra word problems using quadratic programming
Lipu Zhou, Shuaixiang Dai, and Liwei Chen. 2015 · 2015
Earlier work this paper cites.
Fundamentals of cognitive psychology, 3rd ed
Ronald T. Kellogg. 2016 · 2016
Earlier work this paper cites.
Learning to use formulas to solve simple arithmetic problems
Arindam Mitra and Chitta Baral. 2016 · 2016
Earlier work this paper cites.
Program induction by rationale generation: Learning to solve and explain algebraic word problems
Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom. 2017 · 2017
Earlier work this paper cites.
Deep neural solver for math word problems
Yan Wang, Xiaojiang Liu, and Shuming Shi. 2017 · 2017
Earlier work this paper cites.
Modeling intra-relation in math word problems with different functional multi-head attentions
Jierui Li, Lei Wang, Jipeng Zhang, Yan Wang, Bing Tian Dai, and Dongxiang Zhang. 2019 · 2019
Earlier work this paper cites.
Template-based math word problem solvers with recursive neural networks
Lei Wang, Dongxiang Zhang, Jipeng Zhang, Xing Xu, Lianli Gao, Bing Tian Dai, and Heng Tao Shen. 2019 · 2019
Earlier work this paper cites.
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, Christopher Hesse, and John Schulman. 2021 · 2021
Cited alongside, same era.
SimCSE: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021 · 2021
Cited alongside, same era.
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 · 2021
Cited alongside, same era.
Generate & rank: A multi-task framework for math word problems
Jianhao Shen, Yichun Yin, Lin Li, Lifeng Shang, Xin Jiang, Ming Zhang, and Qun Liu. 2021 · 2021
Cited alongside, same era.
Heterogeneous line graph transformer for math word problems
Zijian Hu and Meng Jiang. 2022 · 2022
Complexity-based prompting for multi-step reasoning
Yao Fu, Hao Peng, Ashish Sabharwal, Peter Clark, and Tushar Khot. 2023 · 2023
Later among the works it cites.
Pal: Program-aided language models
Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig. 2023 · 2023
Later among the works it cites.
Generalizing math word problem solvers via solution diversification
Zhenwen Liang, Jipeng Zhang, Lei Wang, Yan Wang, Jie Shao, and Xiangliang Zhang. 2023 · 2023
Later among the works it cites.
Large language models can be easily distracted by irrelevant context
Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed Chi, Nathanael Schärli, and Denny Zhou. 2023 · 2023
Later among the works it cites.
Semantic preprocessor for image compression for machines
Mingyi Yang, Luis Herranz, Fei Yang, Luka Murn, Marc Gorriz Blanch, Shuai Wan, Fuzheng Yang, and Marta Mrak. 2023 · 2023
Later among the works it cites.
Automatic chain of thought prompting in large language models
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Semantic feature extraction for generalized zero-shot learning
Junhan Kim, Kyuhong Shim, and Byonghyo Shim. 2022 · 2022
Cited alongside, same era.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang (Shane) Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
Cited alongside, same era.
Mskat: Multi-scale knowledge-aware transformer for vehicle re-identification
Hongchao Li, Chenglong Li, Aihua Zheng, Jin Tang, and Bin Luo. 2022 · 2022
Cited alongside, same era.
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 · 2022
Cited alongside, same era.
Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks
Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W. Cohen. 2023 · 2023
Cited alongside, same era.
Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models
Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, and Ee-Peng Lim. 2023a
Cited in the paper.
Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023b
Cited in the paper.
Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. 2023 · 2023
Later among the works it cites.
Shen Zheng, Yuyu Zhang, Yijie Zhu, Chenguang Xi, Pengyang Gao, Xun Zhou, and Kevin Chen-Chuan Chang. 2023 · 2023
Later among the works it cites.
Large language models are built-in autoregressive search engines
Noah Ziems, Wenhao Yu, Zhihan Zhang, and Meng Jiang. 2023 · 2023
Later among the works it cites.
Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2031
Closest in time.
Are NLP models really able to solve simple math word problems?
Arkil Patel, Satwik Bhattamishra, and Navin Goyal. 2021 · 2094
Closest in time.