[31] Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A. Smith, and Mike Lewis. Measuring and narrowing the compositionality gap in language models. ArXiv, abs/2210.03350, 2022.
[32] Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models. ArXiv, abs/2210.03629, 2022.
[33] Zhibin Gou, Zhihong Shao, Yeyun Gong, Yelong Shen, Yujiu Yang, Nan Duan, and Weizhu Chen. Critic: Large language models can self-correct with tool-interactive critiquing. ArXiv, abs/2305.11738, 2023.
[34] Shima Imani, Liang Du, and H. Shrivastava. Mathprompter: Mathematical reasoning using large language models. ArXiv, abs/2303.05398, 2023.
[35] Pan Lu, Baolin Peng, Hao Cheng, Michel Galley, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, and Jianfeng Gao. Chameleon: Plug-and-play compositional reasoning with large language models. ArXiv, abs/2304.09842, 2023.
[36] Bhargavi Paranjape, Scott M. Lundberg, Sameer Singh, Hanna Hajishirzi, Luke Zettlemoyer, and Marco Tulio Ribeiro. Art: Automatic multi-step reasoning and tool-use for large language models. ArXiv, abs/2303.09014, 2023.
[37] Iddo Drori, Sarah Zhang, Reece Shuttleworth, Leonard Tang, Albert Lu, Elizabeth Ke, Kevin Liu, Linda Chen, Sunny Tran, Newman Cheng, Roman Wang, Nikhil Singh, Taylor Lee Patti, J. Lynch, Avi Shporer, Nakul Verma, Eugene Wu, and Gilbert Strang. A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level. Proceedings of the National Academy of Sciences of the United States of America, 119, 2021.
[38] Chenfei Wu, Sheng-Kai Yin, Weizhen Qi, Xiaodong Wang, Zecheng Tang, and Nan Duan. Visual chatgpt: Talking, drawing and editing with visual foundation models. ArXiv, abs/2303.04671, 2023.
[39] Yongliang Shen, Kaitao Song, Xu Tan, Dong Sheng Li, Weiming Lu, and Yue Ting Zhuang. Huggingppt: Solving ai tasks with chatgpt and its friends in hugging face. ArXiv, abs/2303.17580, 2023.
[40] Cheng Qian, Chi Han, Yi Ren Fung, Yujia Qin, Zhiyuan Liu, and Heng Ji. Creator: Disentangling abstract and concrete reasonings of large language models through tool creation. ArXiv, abs/2305.14318, 2023.
[41] Mohammad Javad Hosseini, Hannaneh Hajishirzi, Oren Etzioni, and Nate Kushman. Learning to solve arithmetic word problems with verb categorization. In EMNLP, pages 523-533, 2014.
[42] Rik Koncel-Kedziorski, Hannaneh Hajishirzi, Ashish Sabharwal, Oren Etzioni, and Siena Dumas Ang. Parsing algebraic word problems into equations. Transactions of the Association for Computational Linguistics, 3:585-597, 2015.
[43] Subhro Roy and Dan Roth. Solving general arithmetic word problems. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1743-1752, Lisbon, Portugal, September 2015. Association for Computational Linguistics. doi: 10.18653/v1/D15-1202. URL https://aclanthology.org/D15-1202.
[44] Danqing Huang, Shuming Shi, Chin-Yew Lin, Jian Yin, and Wei-Ying Ma. How well do computers solve math word problems? large-scale dataset construction and evaluation. In Annual Meeting of the Association for Computational Linguistics, 2016.
[45] Shyam Upadhyay and Ming-Wei Chang. Annotating derivations: A new evaluation strategy and dataset for algebra word problems. In Conference of the European Chapter of the Association for Computational Linguistics, 2016.
[46] Yan Wang, Xiaojiang Liu, and Shuming Shi. Deep neural solver for math word problems. In EMNLP, 2017.