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Methods such as chain-of-thought prompting and self-consistency have pushed the frontier of language model reasoning performance with no additional training.
A short introduction to boosting
Freund, Y., Schapire, R., and Abe, N · 1999
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Curriculum learning
Bengio, Y., Louradour, J., Collobert, R., and Weston, J · 2009
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Multi-class adaboost
Hastie, T., Rosset, S., Zhu, J., and Zou, H · 2009
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Active learning of inverse models with intrinsically motivated goal exploration in robots
Baranes, A. and Oudeyer, P.-Y · 2013
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
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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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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Irving, G., Christiano, P., and Amodei, D · 2018
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Mathqa: Towards interpretable math word problem solving with operation-based formalisms
Amini, A., Gabriel, S., Lin, P., Koncel-Kedziorski, R., Choi, Y., and Hajishirzi, H · 2019
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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Making pre-trained language models better few-shot learners
Gao, T., Fisch, A., and Chen, D · 2020
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Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J · 2020
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Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Shin, T., Razeghi, Y., Logan IV, R. L., Wallace, E., and Singh, S · 2020
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On the opportunities and risks of foundation models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al · 2021
Cited alongside, same era.
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
Cited alongside, same era.
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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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 · 2021
Cited alongside, same era.
Complexity-based prompting for multi-step reasoning
Fu, Y., Peng, H., Sabharwal, A., Clark, P., and Khot, T · 2022
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Ensemble deep learning: A review
Ganaie, M. A., Hu, M., Malik, A., Tanveer, M., and Suganthan, P · 2022
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Promptboosting: Black-box text classification with ten forward passes
Hou, B., O’Connor, J., Andreas, J., Chang, S., and Zhang, Y · 2022
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Large language models can self-improve
Huang, J., Gu, S. S., Hou, L., Wu, Y., Wang, X., Yu, H., and Han, J · 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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Li, X. L. and Liang, P · 2021
Cited alongside, same era.
What makes good in-context examples for gpt- 3 3 ?
Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., and Chen, W · 2021
Cited alongside, same era.
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., et al · 2021
Cited alongside, same era.
Are nlp models really able to solve simple math word problems?
Patel, A., Bhattamishra, S., and Goyal, N · 2021
Cited alongside, same era.
Learning how to ask: Querying lms with mixtures of soft prompts
Qin, G. and Eisner, J · 2021
Cited alongside, same era.
Learning to retrieve prompts for in-context learning
Rubin, O., Herzig, J., and Berant, J · 2021
Cited alongside, same era.
Calibrate before use: Improving few-shot performance of language models
Zhao, Z., Wallace, E., Feng, S., Klein, D., and Singh, S · 2021
Cited alongside, same era.
Constitutional ai: Harmlessness from ai feedback
Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., Chen, A., Goldie, A., Mirhoseini, A., McKinnon, C., et al · 2022
Cited alongside, same era.
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Solving quantitative reasoning problems with language models
Lewkowycz, A., Andreassen, A., Dohan, D., Dyer, E., Michalewski, H., Ramasesh, V., Slone, A., Anil, C., Schlag, I., Gutman-Solo, T., et al · 2022
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al · 2022
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Chain of thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E., Le, Q., and Zhou, D · 2022
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Star: Bootstrapping reasoning with reasoning
Zelikman, E., Wu, Y., and Goodman, N. D · 2022
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Automatic chain of thought prompting in large language models
Zhang, Z., Zhang, A., Li, M., and Smola, A · 2022
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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
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., and Neubig, G · 2023
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