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Large Language Models (LLMs) demonstrate impressive ability in handling reasoning tasks.
The empirical case for two systems of reasoning
Sloman, S. A. (1996) · 1996
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Thinking, fast and slow
Kahneman, D. (2011) · 2011
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Learning to solve arithmetic word problems with verb categorization
Hosseini, M. J., Hajishirzi, H., Etzioni, O., & Kushman, N. (2014) · 2014
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Parsing algebraic word problems into equations
Koncel-Kedziorski, R., Hajishirzi, H., Sabharwal, A., Etzioni, O., & Ang, S. D. (2015) · 2015
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Solving general arithmetic word problems
Roy, S., & Roth, D. (2015) · 2015
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Deep neural solver for math word problems
Wang, Y., Liu, X., & Shi, S. (2017) · 2017
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Translating a math word problem to a expression tree
Wang, L., Wang, Y., Cai, D., Zhang, D., & Liu, X. (2018) · 2018
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Modeling intra-relation in math word problems with different functional multi-head attentions
Li, J., Wang, L., Zhang, J., Wang, Y., Dai, B. T., & Zhang, D. (2019) · 2019
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CommonsenseQA: A question answering challenge targeting commonsense knowledge
Talmor, A., Herzig, J., Lourie, N., & Berant, J. (2019) · 2019
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A goal-driven tree-structured neural model for math word problems
Xie, Z., & Sun, S. (2019) · 2019
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A knowledge-aware sequence-to-tree network for math word problem solving
Wu, Q., Zhang, Q., Fu, J., & Huang, X. (2020) · 2020
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Graph-to-tree learning for solving math word problems
Zhang, J., Wang, L., Lee, R. K.-W., Bin, Y., Wang, Y., Shao, J., & Lim, E.-P. (2020) · 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., Hesse, C., & Schulman, J. (2021) · 2021
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Generate & rank: A multi-task framework for math word problems
Shen, J., Yin, Y., Li, L., Shang, L., Jiang, X., Zhang, M., & Liu, Q. (2021) · 2021
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Maieutic prompting: Logically consistent reasoning with recursive explanations
Jung, J., Qin, L., Welleck, S., Brahman, F., Bhagavatula, C., Le Bras, R., & Choi, Y. (2022) · 2022
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Large language models are zero-shot reasoners
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022) · 2022
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Chain of thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., brian ichter, Xia, F., Chi, E. H., Le, Q. V., & Zhou, D. (2022) · 2022
Cited alongside, same era.
Hgen: Learning hierarchical heterogeneous graph encoding for math word problem solving
Zhang, Y., Zhou, G., Xie, Z., & Huang, J. X. (2022) · 2022
Cited alongside, same era.
Let’s sample step by step: Adaptive-consistency for efficient reasoning and coding with llms
Aggarwal, A. M. P., Yang, Y., & Mausam (2023) · 2023
Cited alongside, same era.
Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks
Chen, W., Ma, X., Wang, X., & Cohen, W. W. (2023b) · 2023
Cited alongside, same era.
Selection-inference: Exploiting large language models for interpretable logical reasoning
Creswell, A., Shanahan, M., & Higgins, I. (2023) · 2023
Cited alongside, same era.
A recursive tree-structured neural network with goal forgetting and information aggregation for solving math word problems
Xiao, J., Huang, L., Song, Y., & Tang, N. (2023) · 2023
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Self-evaluation guided beam search for reasoning
Xie, Y., Kawaguchi, K., Zhao, Y., Zhao, X., Kan, M.-Y., He, J., & Xie, Q. (2023) · 2023
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Tree of thoughts: Deliberate problem solving with large language models
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., & Narasimhan, K. R. (2023) · 2023
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Improving language models via plug-and-play retrieval feedback
Yu, W., Zhang, Z., Liang, Z., Jiang, M., & Sabharwal, A. (2023) · 2023
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Progressive-hint prompting improves reasoning in large language models
Zheng, C., Liu, Z., Xie, E., Li, Z., & Li, Y. (2023) · 2023
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PAL: program-aided language models
Gao, L., Madaan, A., Zhou, S., Alon, U., Liu, P., Yang, Y., Callan, J., & Neubig, G. (2023) · 2023
Cited alongside, same era.
Rethinking with retrieval: Faithful large language model inference
He, H., Zhang, H., & Roth, D. (2023) · 2023
Cited alongside, same era.
Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes
Hsieh, C.-Y., Li, C.-L., Yeh, C.-k., Nakhost, H., Fujii, Y., Ratner, A., Krishna, R., Lee, C.-Y., & Pfister, T. (2023) · 2023
Cited alongside, same era.
Towards reasoning in large language models: A survey
Huang, J., & Chang, K. C.-C. (2023) · 2023
Cited alongside, same era.
Decomposed prompting: A modular approach for solving complex tasks
Khot, T., Trivedi, H., Finlayson, M., Fu, Y., Richardson, K., Clark, P., & Sabharwal, A. (2023) · 2023
Cited alongside, same era.
A survey of deep learning for mathematical reasoning
Lu, P., Qiu, L., Yu, W., Welleck, S., & Chang, K.-W. (2023) · 2023
Cited alongside, same era.
Self-refine: Iterative refinement with self-feedback
Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., Gupta, S., Majumder, B. P., Hermann, K., Welleck, S., Yazdanbakhsh, A., & Clark, P. (2023) · 2023
Cited alongside, same era.
Least-to-most prompting enables complex reasoning in large language models
Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q. V., & Chi, E. H. (2023) · 2023
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CRITIC: large language models can self-correct with tool-interactive critiquing
Gou, Z., Shao, Z., Gong, Y., Shen, Y., Yang, Y., Duan, N., & Chen, W. (2024a) · 2024
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Tora: A tool-integrated reasoning agent for mathematical problem solving
Gou, Z., Shao, Z., Gong, Y., Shen, Y., Yang, Y., Huang, M., Duan, N., & Chen, W. (2024b) · 2024
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Candidate-heuristic in-context learning: A new framework for enhancing medical visual question answering with llms
Liang, X., Wang, D., Zhong, H., Wang, Q., Li, R., Jia, R., & Wan, B. (2024) · 2024
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Explainable knowledge reasoning via thought chains for knowledge-based visual question answering
Qiu, C., Xie, Z., Liu, M., & Hu, H. (2024) · 2024
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Scaling llm test-time compute optimally can be more effective than scaling model parameters
Snell, C., Lee, J., Xu, K., & Kumar, A. (2024) · 2024
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An empirical analysis of compute-optimal inference for problem-solving with language models
Wu, Y., Sun, Z., Li, S., Welleck, S., & Yang, Y. (2024) · 2024
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Large language model cascades with mixture of thought representations for cost-efficient reasoning
Yue, M., Zhao, J., Zhang, M., Du, L., & Yao, Z. (2024) · 2024
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Number-enhanced representation with hierarchical recursive tree decoding for math word problem solving
Zhang, Y., Zhou, G., Xie, Z., & Huang, J. X. (2024) · 2024
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Are NLP models really able to solve simple math word problems?
Patel, A., Bhattamishra, S., & Goyal, N. (2021) · 2094
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