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Large language models (LLMs) have demonstrated remarkable capabilities in language generation, understanding, and few-shot learning in recent years.
Society of mind
M. Minsky · 1988
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Deep reinforcement learning from human preferences
P. F. Christiano, J. Leike, T. Brown, M. Martic, S. Legg, and D. Amodei · 2017
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G. Irving, P. Christiano, and D. Amodei · 2018
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Explain yourself! leveraging language models for commonsense reasoning
N. F. Rajani, B. McCann, C. Xiong, and R. Socher · 2019
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Fine-tuning language models from human preferences
D. M. Ziegler, N. Stiennon, J. Wu, T. B. Brown, A. Radford, D. Amodei, P. Christiano, and G. Irving · 2019
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Compositional visual generation with energy based models
Y. Du, S. Li, and I. Mordatch · 2020
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REALM: Retrieval-augmented language model pre-training
K. Guu, K. Lee, Z. Tung, P. Pasupat, and M.-W. Chang · 2020
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Measuring massive multitask language understanding
D. Hendrycks, C. Burns, S. Basart, A. Zou, M. Mazeika, D. Song, and J. Steinhardt · 2020
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Training verifiers to solve math word problems
K. Cobbe, V. Kosaraju, M. Bavarian, M. Chen, H. Jun, L. Kaiser, M. Plappert, J. Tworek, J. Hilton, R. Nakano, et al · 2021
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Show your work: Scratchpads for intermediate computation with language models
M. Nye, A. J. Andreassen, G. Gur-Ari, H. Michalewski, J. Austin, D. Bieber, D. Dohan, A. Lewkowycz, M. Bosma, D. Luan, et al · 2021
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Prompt programming for large language models: Beyond the few-shot paradigm
L. Reynolds and K. McDonell · 2021
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Flamingo: A visual language model for few-shot learning
J.-B. Alayrac, J. Donahue, P. Luc, A. Miech, I. Barr, Y. Hasson, K. Lenc, A. Mensch, K. Millican, M. Reynolds, et al · 2022
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Language models (mostly) know what they know
S. Kadavath, T. Conerly, A. Askell, T. Henighan, D. Drain, E. Perez, N. Schiefer, Z. H. Dodds, N. DasSarma, E. Tran-Johnson, et al · 2022
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Large language models are zero-shot reasoners
T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa · 2022
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Factuality enhanced language models for open-ended text generation
N. Lee, W. Ping, P. Xu, M. Patwary, P. N. Fung, M. Shoeybi, and B. Catanzaro · 2022
Lamda: Language models for dialog applications
R. Thoppilan, D. De Freitas, J. Hall, N. Shazeer, A. Kulshreshtha, H.-T. Cheng, A. Jin, T. Bos, L. Baker, Y. Du, et al · 2022
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Self-consistency improves chain of thought reasoning in language models
X. Wang, J. Wei, D. Schuurmans, Q. Le, E. Chi, and D. Zhou · 2022
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Chain of thought prompting elicits reasoning in large language models
J. Wei, X. Wang, D. Schuurmans, M. Bosma, E. Chi, Q. Le, and D. Zhou · 2022
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Star: Bootstrapping reasoning with reasoning
E. Zelikman, Y. Wu, J. Mu, and N. Goodman · 2022
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Socratic models: Composing zero-shot multimodal reasoning with language
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Cited alongside, same era.
Solving quantitative reasoning problems with language models
A. Lewkowycz, A. Andreassen, D. Dohan, E. Dyer, H. Michalewski, V. Ramasesh, A. Slone, C. Anil, I. Schlag, T. Gutman-Solo, et al · 2022
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Chatgpt: Optimizing language models for dialogue, Dec 2022
OpenAI · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al · 2022
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
A. Srivastava, A. Rastogi, A. Rao, A. A. M. Shoeb, A. Abid, A. Fisch, A. R. Brown, A. Santoro, A. Gupta, A. Garriga-Alonso, et al · 2022
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Fsmosca/pgn-standard: Portable game notation specification and implementation guide
Fsmosca
Cited in the paper.
Composing ensembles of pre-trained models via iterative consensus
S. Li, Y. Du, J. B. Tenenbaum, A. Torralba, and I. Mordatch
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Competition-level code generation with alphacode
Y. Li, D. Choi, J. Chung, N. Kushman, J. Schrittwieser, R. Leblond, T. Eccles, J. Keeling, F. Gimeno, A. Dal Lago, et al
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A. Zeng, A. Wong, S. Welker, K. Choromanski, F. Tombari, A. Purohit, M. Ryoo, V. Sindhwani, J. Lee, V. Vanhoucke, et al · 2022
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Reduce, reuse, recycle: Compositional generation with energy-based diffusion models and mcmc
Y. Du, C. Durkan, R. Strudel, J. B. Tenenbaum, S. Dieleman, R. Fergus, J. Sohl-Dickstein, A. Doucet, and W. Grathwohl · 2023
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Self-refine: Iterative refinement with self-feedback
A. Madaan, N. Tandon, P. Gupta, S. Hallinan, L. Gao, S. Wiegreffe, U. Alon, N. Dziri, S. Prabhumoye, Y. Yang, et al · 2023
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An important next step on our ai journey, Feb 2023
S. Pichai · 2023
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Reflexion: an autonomous agent with dynamic memory and self-reflection
N. Shinn, B. Labash, and A. Gopinath · 2023
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