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Recent advancements in large-scale models, such as GPT-4, have showcased remarkable capabilities in addressing standard queries.
Analysing Mathematical Reasoning Abilities of Neural Models
Saxton, D.; Grefenstette, E.; Hill, F.; and Kohli, P. 2019 · 1904
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The Goldbach Conjecture , volume 4
Wang, Y. 2002 · 2002
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The crystallographic restriction, permutations, and Goldbach’s conjecture
Bamberg, J.; Cairns, G.; and Kilminster, D. 2003 · 2003
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Refinements of Goldbach’s conjecture, and the generalized Riemann hypothesis
Granville, A. 2007 · 2007
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The interactive effect of exercise intensity and task difficulty on human cognitive processing
Kamijo, K.; Nishihira, Y.; Higashiura, T.; and Kuroiwa, K. 2007 · 2007
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On the cognitive process of human problem solving
Wang, Y.; and Chiew, V. 2010 · 2010
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A study on Goldbach conjecture
Carbó-Dorca, R. 2016 · 2016
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Evaluating large language models trained on code
Chen, M.; Tworek, J.; Jun, H.; Yuan, Q.; Pinto, H. P. d. O.; Kaplan, J.; Edwards, H.; Burda, Y.; Joseph, N.; Brockman, G.; et al. 2021 · 2021
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Show Your Work: Scratchpads for Intermediate Computation with Language Models
Nye, M.; Andreassen, A. J.; Gur-Ari, G.; Michalewski, H.; Austin, J.; Bieber, D.; Dohan, D.; Lewkowycz, A.; Bosma, M.; Luan, D.; Sutton, C.; and Odena, A. 2021 · 2021
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Selection-inference: Exploiting large language models for interpretable logical reasoning
Creswell, A.; Shanahan, M.; and Higgins, I. 2022 · 2022
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Handbook of learning and cognitive processes
Estes, W. 2022 · 2022
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Promptmaker: Prompt-based prototyping with large language models
Jiang, E.; Olson, K.; Toh, E.; Molina, A.; Donsbach, A.; Terry, M.; and Cai, C. J. 2022 · 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 · 2022
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Neural theory-of-mind? on the limits of social intelligence in large lms
Sap, M.; LeBras, R.; Fried, D.; and Choi, Y. 2022 · 2022
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Interactive and visual prompt engineering for ad-hoc task adaptation with large language models
Strobelt, H.; Webson, A.; Sanh, V.; Hoover, B.; Beyer, J.; Pfister, H.; and Rush, A. M. 2022 · 2022
Cited alongside, same era.
Large language models are human-level prompt engineers
Zhou, Y.; Muresanu, A. I.; Han, Z.; Paster, K.; Pitis, S.; Chan, H.; and Ba, J. 2022 · 2022
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4nums.com - Best Math Game Online
4nums.com. 2023 · 2023
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Prompting large language models with the socratic method
Chang, E. Y. 2023 · 2023
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“So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy
Dwivedi, Y. K.; Kshetri, N.; Hughes, L.; Slade, E. L.; Jeyaraj, A.; Kar, A. K.; Baabdullah, A. M.; Koohang, A.; Raghavan, V.; Ahuja, M.; et al. 2023 · 2023
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OpenAI. 2023 · 2023
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Wang, X.; Wei, J.; Schuurmans, D.; Le, Q.; Chi, E.; Narang, S.; Chowdhery, A.; and Zhou, D. 2022 · 2022
Cited alongside, same era.
A Proof of Goldbach Conjecture by Mirror-Prime Decomposition
Wang, Y. 2022 · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J.; Wang, X.; Schuurmans, D.; Bosma, M.; Xia, F.; Chi, E.; Le, Q. V.; Zhou, D.; et al. 2022 · 2022
Cited alongside, same era.
Promptchainer: Chaining large language model prompts through visual programming
Wu, T.; Jiang, E.; Donsbach, A.; Gray, J.; Molina, A.; Terry, M.; and Cai, C. J. 2022 · 2022
Cited alongside, same era.
Automatic chain of thought prompting in large language models
Zhang, Z.; Zhang, A.; Li, M.; and Smola, A. 2022 · 2022
Cited alongside, same era.
Paranjape, B.; Lundberg, S.; Singh, S.; Hajishirzi, H.; Zettlemoyer, L.; and Ribeiro, M. T. 2023 · 2023
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Distilling Reasoning Capabilities into Smaller Language Models
Shridhar, K.; Stolfo, A.; and Sachan, M. 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.; and Narasimhan, K. 2023 · 2023
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Empirical verification of the even Goldbach conjecture and computation of prime gaps up
Oliveira e Silva, T.; Herzog, S.; and Pardi, S. 2014 · 2060
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