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Large language models (LLMs) solve problems more accurately and interpretably when instructed to work out the answer step by step using a ``chain-of-thought'' (CoT) prompt.
Monte Carlo sampling methods using Markov chains and their applications
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Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
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Markov chains for exploring posterior distributions
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Cover, T. M · 1999
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The stochastic EM algorithm: Estimation and asymptotic results
Nielsen, S. F · 2000
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Safe and effective importance sampling
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Pattern recognition and machine learning
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On resampling algorithms for particle filters
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Notes on the kl-divergence between a markov chain and its equilibrium distribution
Murray, I. and Salakhutdinov, R · 2008
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Training restricted boltzmann machines using approximations to the likelihood gradient
Tieleman, T · 2008
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Reweighted wake-sleep, 2015
Bornschein, J. and Bengio, Y · 2015
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Importance weighted autoencoders
Burda, Y., Grosse, R., and Salakhutdinov, R · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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SGDR: Stochastic gradient descent with warm restarts
Loshchilov, I. and Hutter, F · 2017
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Sticking the landing: Simple, lower-variance gradient estimators for variational inference
Roeder, G., Wu, Y., and Duvenaud, D. K · 2017
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Rebar: Low-variance, unbiased gradient estimates for discrete latent variable models
Tucker, G., Mnih, A., Maddison, C. J., Lawson, J., and Sohl-Dickstein, J · 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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Revisiting reweighted wake-sleep for models with stochastic control flow, 2019
Le, T. A., Kosiorek, A. R., Siddharth, N., Teh, Y. W., and Wood, F · 2019
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Explain yourself! leveraging language models for commonsense reasoning
Rajani, N. F., McCann, B., Xiong, C., and Socher, R · 2019
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CommonsenseQA: A question answering challenge targeting commonsense knowledge
Talmor, A., Herzig, J., Lourie, N., and Berant, J · 2019
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Learning to learn generative programs with memoised wake-sleep
Deep Latent Variable Models for Natural Language Processing
Lievin, V · 2022
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Probabilistic Machine Learning: An introduction
Murphy, K. P · 2022
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Talm: Tool augmented language models, 2022
Parisi, A., Zhao, Y., and Fiedel, N · 2022
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Solving math word problems with process-and outcome-based feedback
Uesato, J., Kushman, N., Kumar, R., Song, F., Siegel, N., Wang, L., Creswell, A., Irving, G., and Higgins, I · 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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Hewitt, L. B., Le, T. A., and Tenenbaum, J. B · 2020
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Markovian score climbing: Variational inference with kl (p|| q)
Naesseth, C., Lindsten, F., and Blei, D · 2020
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Unsupervised commonsense question answering with self-talk
Shwartz, V., West, P., Le Bras, R., Bhagavatula, C., and Choi, Y · 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., and Schulman, J · 2021
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The power of scale for parameter-efficient prompt tuning
Lester, B., Al-Rfou, R., and Constant, N · 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
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Scaling instruction-finetuned language models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, E., Wang, X., Dehghani, M., Brahma, S., et al · 2022
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Zelikman, E., Wu, Y., and Goodman, N. D · 2022
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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., Bousquet, O., Le, Q., and Chi, E · 2022
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Anil, R., Dai, A. M., Firat, O., Johnson, M., Lepikhin, D., Passos, A., Shakeri, S., Taropa, E., Bailey, P., Chen, Z., et al · 2023
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Tpu v4: An optically reconfigurable supercomputer for machine learning with hardware support for embeddings
Jouppi, N., Kurian, G., Li, S., Ma, P., Nagarajan, R., Nai, L., Patil, N., Subramanian, S., Swing, A., Towles, B., et al · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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Toolformer: Language models can teach themselves to use tools, 2023
Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., and Scialom, T · 2023
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Reflexion: an autonomous agent with dynamic memory and self-reflection, 2023
Shinn, N., Labash, B., and Gopinath, A · 2023
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Turpin, M., Michael, J., Perez, E., and Bowman, S. R · 2023
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React: Synergizing reasoning and acting in language models, 2023
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y · 2023
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Explanation selection using unlabeled data for In-Context learning
Ye, X. and Durrett, G · 2023
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