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Recent advances in artificial intelligence (AI) have produced highly capable and controllable systems.
A universal modular actor formalism for artificial intelligence
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Garlan, D. and Shaw, M · 1993
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Horvitz, E · 1999
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Automated protein model building combined with iterative structure refinement
Perrakis, A., Morris, R. J., and Lamzin, V. S · 1999
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Making reliable distributed systems in the presence of software errors
Armstrong, J · 2003
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Actor model of computation: Scalable robust information systems
Hewitt, C. E · 2010
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Meta-reasoning: Monitoring and control of thinking and reasoning
Ackerman, R. and Thompson, V. A · 2017
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Guidelines for human-ai interaction
Amershi, S., Weld, D. S., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S. T., Bennett, P. N., Inkpen, K., Teevan, J., Kikin-Gil, R., and Horvitz, E · 2019
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Language models are few-shot learners
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Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Ponde, H., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., Ray, A., Puri, R., Krueger, G., Petrov, M., Khlaaf, H., Sastry, G., Mishkin, P., Chan, B., Gray, S., Ryder, N., Pavlov, M., Power, A., Kaiser, L., Bavarian, M., Winter, C., Tillet, P., Such, F. P., Cummings, D. W., Plappert, M., Chantzis, F., Barnes, E., Herbert-Voss, A., Guss, W. H., Nichol, A., Babuschkin, I., Balaji, S. A., Jain, S., Carr, A., Leike, J., Achiam, J., Misra, V., Morikawa, E., Radford, A., Knight, M. M., Brundage, M., Murati, M., Mayer, K., Welinder, P., McGrew, B., Amodei, D., McCandlish, S., Sutskever, I., and Zaremba, W · 2021
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Measuring coding challenge competence with apps
Hendrycks, D., Basart, S., Kadavath, S., Mazeika, M., Arora, A., Guo, E., Burns, C., Puranik, S., He, H., Song, D., and Steinhardt, J · 2021
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Show your work: Scratchpads for intermediate computation with language models
Nye, M. I., 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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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2021
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Image super-resolution via iterative refinement
Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D. J., and Norouzi, M · 2021
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Langchain
Chase, H · 2022
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Chen, W., Ma, X., Wang, X., and Cohen, W. W · 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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Competition-level code generation with alphacode
Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Dal Lago, A., et al · 2022
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Data contamination: From memorization to exploitation
Magar, I. and Schwartz, R · 2022
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Learning to model editing processes
Reid, M. and Neubig, G · 2022
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Camel: Communicative agents for" mind" exploration of large scale language model society
Li, G., Hammoud, H. A. A. K., Itani, H., Khizbullin, D., and Ghanem, B · 2023
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Chameleon: Plug-and-play compositional reasoning with large language models
Lu, P., Peng, B., Cheng, H., Galley, M., Chang, K.-W., Wu, Y. N., Zhu, S.-C., and Gao, J · 2023
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Self-refine: Iterative refinement with self-feedback
Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al · 2023
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Codeforces.com, 2023
Mirzayanov, M · 2023
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When to show a suggestion? integrating human feedback in ai-assisted programming
Mozannar, H., Bansal, G., Fourney, A., and Horvitz, E · 2023
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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
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Parsel: A (de-)compositional framework for algorithmic reasoning with language models, 2022
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Teaching large language models to self-debug
Chen, X., Lin, M., Schärli, N., and Zhou, D · 2023
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Promptbreeder: Self-referential self-improvement via prompt evolution
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Metagpt: Meta programming for multi-agent collaborative framework
Hong, S., Zheng, X., Chen, J., Cheng, Y., Wang, J., Zhang, C., Wang, Z., Yau, S. K. S., Lin, Z., Zhou, L., Ran, C., Xiao, L., and Wu, C · 2023
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Large language models cannot self-correct reasoning yet
Huang, J., Chen, X., Mishra, S., Zheng, H. S., Yu, A. W., Song, X., and Zhou, D · 2023
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Refiner: Reasoning feedback on intermediate representations
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Mathematical discoveries from program search with large language models
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Toolformer: Language models can teach themselves to use tools
Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., and Scialom, T · 2023
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Reflexion: Language agents with verbal reinforcement learning
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Generating sequences by learning to self-correct
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Autogen: Enabling next-gen LLM applications via multi-agent conversation framework
Wu, Q., Bansal, G., Zhang, J., Wu, Y., Zhang, S., Zhu, E., Li, B., Jiang, L., Zhang, X., and Wang, C · 2023
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Answering questions by meta-reasoning over multiple chains of thought
Yoran, O., Wolfson, T., Bogin, B., Katz, U., Deutch, D., and Berant, J · 2023
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Multimodal chain-of-thought reasoning in language models
Zhang, Z., Zhang, A., Li, M., Zhao, H., Karypis, G., and Smola, A. J · 2023
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