[1] R. N. Shepard, “The step to rationality: The efficacy of thought experiments in science, ethics, and free will,” Cognitive Science, vol. 32, no. 1, pp. 3–35, 2008.
[2] D. C. Dennett, Intuition pumps and other tools for thinking. WW Norton & Company, 2013.
[3] T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al., "Language models are few-shot learners," Advances in neural information processing systems, vol. 33, pp. 1877-1901, 2020.
[4] A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann, et al., “Palm: Scaling language modeling with pathways,” arXiv preprint arXiv:2204.02311, 2022.
[5] H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, et al., "Llama: Open and efficient foundation language models," arXiv preprint arXiv:2302.13971, 2023.
[6] B. bench authors, "Beyond the imitation game: Quantifying and extrapolating the capabilities of language models," Transactions on Machine Learning Research, 2023.
[7] K. Cobbe, V. Kosaraju, M. Bavarian, M. Chen, H. Jun, L. Kaiser, M. Plappert, J. Tworek, J. Hilton, R. Nakano, et al., "Training verifiers to solve math word problems," arXiv preprint arXiv:2110.14168, 2021.
[8] M. Nye, A. J. Andreassen, G. Gur-Ari, H. Michalewski, J. Austin, D. Bieber, D. Dohan, A. Lewkowycz, M. Bosma, D. Luan, et al., "Show your work: Scratchpads for intermediate computation with language models," arXiv preprint arXiv:2112.00114, 2021.
[9] T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa, “Large language models are zero-shot reasoners,” arXiv preprint arXiv:2205.11916, 2022.
[10] J. Wei, X. Wang, D. Schuurmans, M. Bosma, brian richter, F. Xia, E. H. Chi, Q. V. Le, and D. Zhou, "Chain of thought prompting elicits reasoning in large language models," in Advances in Neural Information Processing Systems (A. H. Oh, A. Agarwal, D. Belgrave, and K. Cho, eds.), 2022.
[11] D. Dohan, W. Xu, A. Lewkowycz, J. Austin, D. Bieber, R. G. Lopes, Y. Wu, H. Michalewski, R. A. Saurous, J. Sohl-dickstein, et al., "Language model cascades," arXiv preprint arXiv:2207.10342, 2022.
[12] A. K. Lampinen, I. Dasgupta, S. C. Chan, K. Matthewson, M. H. Tessler, A. Creswell, J. L. McClelland, J. X. Wang, and F. Hill, "Can language models learn from explanations in context?", arXiv preprint arXiv:2204.02329, 2022.
[13] E. Zelikman, Y. Wu, J. Mu, and N. Goodman, "STaR: Bootstrapping reasoning with reasoning," in Advances in Neural Information Processing Systems (A. H. Oh, A. Agarwal, D. Belgrave, and K. Cho, eds.), 2022.
[14] X. Wang, J. Wei, D. Schuurmans, Q. V. Le, E. H. Chi, S. Narang, A. Chowdhery, and D. Zhou, "Self-consistency improves chain of thought reasoning in language models," in The Eleventh International Conference on Learning Representations, 2022.
[15] D. Zhou, N. Scharli, L. Hou, J. Wei, N. Scales, X. Wang, D. Schuurmans, C. Cui, O. Bousquet, Q. Le, et al., "Least-to-most prompting enables complex reasoning in large language models," arXiv preprint arXiv:2205.10625, 2022.
[16] S. Yao, D. Yu, J. Zhao, I. Shafran, T. L. Griffiths, Y. Cao, and K. Narasimhan, "Tree of thoughts: Deliberate problem solving with large language models," arXiv preprint arXiv:2305.10601, 2023.
[17] D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent dirichlet allocation," Journal of machine Learning research, vol. 3, no. Jan, pp. 993-1022, 2003.