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Using Large Language Models for complex mathematical reasoning is difficult, primarily due to the complexity of multi-step reasoning.
Introduction to information retrieval , volume 39
Schütze, H., Manning, C. D., and Raghavan, P · 2008
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Solving general arithmetic word problems
Roy, S. and Roth, D · 2016
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Program induction by rationale generation: Learning to solve and explain algebraic word problems
Ling, W., Yogatama, D., Dyer, C., and Blunsom, P · 2017
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., Riedel, S., and Kiela, D · 2020
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Training verifiers to solve math word problems
Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., et al · 2021
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Are nlp models really able to solve simple math word problems?
Patel, A., Bhattamishra, S., and Goyal, N · 2021
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Chen, W., Ma, X., Wang, X., and Cohen, W. W · 2022
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Large language models are zero-shot reasoners
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y · 2022
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Self-consistency improves chain of thought reasoning in language models
Wang, X., Wei, J., Schuurmans, D., Le, Q. V., Chi, E. H., Narang, S., Chowdhery, A., and Zhou, D · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., ichter, b., Xia, F., Chi, E., Le, Q. V., and Zhou, D · 2022
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Retrieval augmentation for commonsense reasoning: A unified approach
Yu, W., Zhu, C., Zhang, Z., Wang, S., Zhang, Z., Fang, Y., and Jiang, M · 2022
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Llemma: An open language model for mathematics
Azerbayev, Z., Schoelkopf, H., Paster, K., Santos, M. D., McAleer, S., Jiang, A. Q., Deng, J., Biderman, S., and Welleck, S · 2023
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Graph of thoughts: Solving elaborate problems with large language models
Besta, M., Blach, N., Kubicek, A., Gerstenberger, R., Gianinazzi, L., Gajda, J., Lehmann, T., Podstawski, M., Niewiadomski, H., Nyczyk, P., et al · 2023
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Chain-of-verification reduces hallucination in large language models
Art: Automatic multi-step reasoning and tool-use for large language models
Paranjape, B., Lundberg, S., Singh, S., Hajishirzi, H., Zettlemoyer, L., and Ribeiro, M. T · 2023
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High-school students’ productive struggles during the simplification of trigonometrical expressions and the proving of trigonometrical identities
Sayster, A · 2023
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Lego-prover: Neural theorem proving with growing libraries
Xin, H., Wang, H., Zheng, C., Li, L., Liu, Z., Cao, Q., Huang, Y., Xiong, J., Shi, H., Xie, E., et al · 2023
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Leandojo: Theorem proving with retrieval-augmented language models
Yang, K., Swope, A. M., Gu, A., Chalamala, R., Song, P., Yu, S., Godil, S., Prenger, R., and Anandkumar, A · 2023
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Dhuliawala, S., Komeili, M., Xu, J., Raileanu, R., Li, X., Celikyilmaz, A., and Weston, J · 2023
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Mathprompter: Mathematical reasoning using large language models
Imani, S., Du, L., and Shrivastava, H · 2023
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Levonian, Z., Li, C., Zhu, W., Gade, A., Henkel, O., Postle, M.-E., and Xing, W · 2023
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Lightman, H., Kosaraju, V., Burda, Y., Edwards, H., Baker, B., Lee, T., Leike, J., Schulman, J., Sutskever, I., and Cobbe, K · 2023
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Large language model guided tree-of-thought
Long, J · 2023
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Measuring mathematical problem solving with the math dataset
Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., and Steinhardt, J
Cited in the paper.
Measuring mathematical problem solving with the math dataset
Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., and Steinhardt, J
Cited in the paper.
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., and Narasimhan, K · 2023
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Mathematica online, Version 14.0
Inc., W. R · 2024
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Solving olympiad geometry without human demonstrations
Trinh, T. H., Wu, Y., Le, Q. V., He, H., and Luong, T · 2024
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Can neural networks do arithmetic? a survey on the elementary numerical skills of state-of-the-art deep learning models
Testolin, A · 2076
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