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Prompted models have demonstrated impressive few-shot learning abilities.
Concepts in a probabilistic language of thought
Goodman, N. D., Tenenbaum, J. B., and Gerstenberg, T · 2014
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Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B · 2015
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Design and implementation of probabilistic programming language anglican
Tolpin, D., van de Meent, J., Yang, H., and Wood, F. D · 2016
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Pyro: Deep universal probabilistic programming
Bingham, E., Chen, J. P., Jankowiak, M., Obermeyer, F., Pradhan, N., Karaletsos, T., Singh, R., Szerlip, P., Horsfall, P., and Goodman, N. D · 2018
Earlier work this paper cites.
Reinforcement learning and control as probabilistic inference: Tutorial and review
Levine, S · 2018
Earlier work this paper cites.
An introduction to probabilistic programming
van de Meent, J.-W., Paige, B., Yang, H., and Wood, F · 2018
Earlier work this paper cites.
PyMC4: Exploiting coroutines for implementing a probabilistic programming framework
Kochurov, M., Carroll, C., Wiecki, T., and Lao, J · 2019
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Composable effects for flexible and accelerated probabilistic programming in NumPyro
Phan, D., Pradhan, N., and Jankowiak, M · 2019
Earlier work this paper cites.
Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., and Others · 2020
Earlier work this paper cites.
The frontier of simulation-based inference
Cranmer, K., Brehmer, J., and Louppe, G · 2020
Earlier work this paper cites.
Generative language modeling for automated theorem proving
Polu, S. and Sutskever, I · 2020
Cited alongside, same era.
Beyond the imitation game: Measuring and extrapolating the capabilities of language models
BIG-bench collaboration · 2021
Cited alongside, same era.
Decision transformer: Reinforcement learning via sequence modeling
Chen, L., Lu, K., Rajeswaran, A., Lee, K., Grover, A., Laskin, M., Abbeel, P., Srinivas, A., and Mordatch, I · 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., Hesse, C., and Schulman, J · 2021
Cited alongside, same era.
WebGPT: Browser-assisted question-answering with human feedback
Nakano, R., Hilton, J., Balaji, S., Wu, J., Ouyang, L., Kim, C., Hesse, C., Jain, S., Kosaraju, V., Saunders, W., Jiang, X., Cobbe, K., Eloundou, T., Krueger, G., Button, K., Knight, M., Chess, B., and Schulman, J · 2021
Cited alongside, same era.
Self-critiquing models for assisting human evaluators, 2022
Saunders, W., Yeh, C., Wu, J., Bills, S., Ouyang, L., Ward, J., and Leike, J · 2022
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Training language models with language feedback
Scheurer, J., Campos, J. A., Chan, J. S., Chen, A., Cho, K., and Perez, E · 2022
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LaMDA: Language models for dialog applications
Thoppilan, R., De Freitas, D., Hall, J., Shazeer, N., Kulshreshtha, A., Cheng, H.-T., Jin, A., Bos, T., Baker, L., Du, Y., Li, Y., Lee, H., Zheng, H. S., Ghafouri, A., Menegali, M., Huang, Y., Krikun, M., Lepikhin, D., Qin, J., Chen, D., Xu, Y., Chen, Z., Roberts, A., Bosma, M., Zhao, V., Zhou, Y., Chang, C.-C., Krivokon, I., Rusch, W., Pickett, M., Srinivasan, P., Man, L., Meier-Hellstern, K., Morris, M. R., Doshi, T., Santos, R. D., Duke, T., Soraker, J., Zevenbergen, B., Prabhakaran, V., Diaz, M., Hutchinson, B., Olson, K., Molina, A., Hoffman-John, E., Lee, J., Aroyo, L., Rajakumar, R., Butryna, A., Lamm, M., Kuzmina, V., Fenton, J., Cohen, A., Bernstein, R., Kurzweil, R., Aguera-Arcas, B., Cui, C., Croak, M., Chi, E., and Le, Q · 2022
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Self-Consistency improves chain of thought reasoning in language models
Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., and Zhou, D · 2022
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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
Cited alongside, same era.
Shaking the foundations: delusions in sequence models for interaction and control
Ortega, P. A., Kunesch, M., Delétang, G., Genewein, T., Grau-Moya, J., Veness, J., Buchli, J., Degrave, J., Piot, B., Perolat, J., et al · 2021
Cited alongside, same era.
URL https://www.alignmentforum.org/posts/qHCDysDnvhteW7kRd/arc-s-first-technical-report-eliciting-latent-knowledge
ARC’s first technical report: Eliciting latent knowledge - AI alignment forum, 2022 · 2022
Cited alongside, same era.
PaLM: Scaling language modeling with pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A., Barnes, P., Tay, Y., Shazeer, N., Prabhakaran, V., Reif, E., Du, N., Hutchinson, B., Pope, R., Bradbury, J., Austin, J., Isard, M., Gur-Ari, G., Yin, P., Duke, T., Levskaya, A., Ghemawat, S., Dev, S., Michalewski, H., Garcia, X., Misra, V., Robinson, K., Fedus, L., Zhou, D., Ippolito, D., Luan, D., Lim, H., Zoph, B., Spiridonov, A., Sepassi, R., Dohan, D., Agrawal, S., Omernick, M., Dai, A. M., Pillai, T. S., Pellat, M., Lewkowycz, A., Moreira, E., Child, R., Polozov, O., Lee, K., Zhou, Z., Wang, X., Saeta, B., Diaz, M., Firat, O., Catasta, M., Wei, J., Meier-Hellstern, K., Eck, D., Dean, J., Petrov, S., and Fiedel, N · 2022
Cited alongside, same era.
Selection-Inference: Exploiting large language models for interpretable logical reasoning
Creswell, A., Shanahan, M., and Higgins, I · 2022
Cited alongside, same era.
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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Foundation posteriors for approximate probabilistic inference
Wu, M. and Goodman, N · 2022
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Promptchainer: Chaining large language model prompts through visual programming, 2022
Wu, T., Jiang, E., Donsbach, A., Gray, J., Molina, A., Terry, M., and Cai, C. J · 2022
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Star: Bootstrapping reasoning with reasoning
Zelikman, E., Wu, Y., and Goodman, N. D · 2022
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Socratic models: Composing Zero-Shot multimodal reasoning with language
Zeng, A., Wong, A., Welker, S., Choromanski, K., Tombari, F., Purohit, A., Ryoo, M., Sindhwani, V., Lee, J., Vanhoucke, V., and Florence, P · 2022
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