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Large language models (LLMs) have significantly advanced the field of artificial intelligence.
Classical conditioning ii: current research and theory
Rescorla, R. A · 1972
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Evaluation of a behavioral measure of risk taking: the balloon analogue risk task (bart)
Lejuez, C. W. et al · 2002
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Detecting regime shifts: The causes of under-and overreaction
Massey, C. and Wu, G · 2005
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Model-based influences on humans’ choices and striatal prediction errors
Daw, N. D., Gershman, S. J., Seymour, B., Dayan, P., and Dolan, R. J · 2011
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Computational psychiatry
Montague, P. R., Dolan, R. J., Friston, K. J., and Dayan, P · 2012
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Humans use directed and random exploration to solve the explore–exploit dilemma
Wilson, R. C., Geana, A., White, J. M., Ludvig, E. A., and Cohen, J. D · 2014
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On the functional form of temporal discounting: An optimized adaptive test
Cavagnaro, D. R., Aranovich, G. J., McClure, S. M., Pitt, M. A., and Myung, J. I · 2016
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Deep reinforcement learning from human preferences
Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., and Amodei, D · 2017
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Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension, 2017
Joshi, M., Choi, E., Weld, D. S., and Zettlemoyer, L · 2017
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Behavioural and neural characterization of optimistic reinforcement learning
Lefebvre, G., Lebreton, M., Meyniel, F., Bourgeois-Gironde, S., and Palminteri, S · 2017
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Deconstructing the human algorithms for exploration
Gershman, S. J · 2018
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Computational phenotyping: using models to understand individual differences in personality, development, and mental illness
Patzelt, E. H., Hartley, C. A., and Gershman, S. J · 2018
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Domain-general enhancements of metacognitive ability through adaptive training
Carpenter, J., Sherman, M. T., Kievit, R. A., Seth, A. K., Lau, H., and Fleming, S. M · 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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A theory of learning to infer
Dasgupta, I., Schulz, E., Tenenbaum, J. B., and Gershman, S. J · 2020
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Scaling laws for neural language models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D · 2020
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Umap: Uniform manifold approximation and projection for dimension reduction, 2020
McInnes, L., Healy, J., and Melville, J · 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
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Exploration beyond bandits
Brändle, F., Binz, M., and Schulz, E · 2021
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Evaluating large language models trained on code, 2021
Chen, M., Tworek, J., Jun, H., Yuan, Q., de Oliveira Pinto, H. P., 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., Plappert, M., Chantzis, F., Barnes, E., Herbert-Voss, A., Guss, W. H., Nichol, A., Paino, A., Tezak, N., Tang, J., Babuschkin, I., Balaji, S., Jain, S., Saunders, W., Hesse, C., Carr, A. N., Leike, J., Achiam, J., Misra, V., Morikawa, E., Radford, A., Knight, 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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Training verifiers to solve math word problems, 2021
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.
Measuring massive multitask language understanding, 2021
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J · 2021
Cited alongside, same era.
Sources of metacognitive inefficiency
Shekhar, M. and Rahnev, D · 2021
Cited alongside, same era.
Understanding the capabilities, limitations, and societal impact of large language models
Tamkin, A., Brundage, M., Clark, J., and Ganguli, D · 2021
Cited alongside, same era.
Collins, K. M., Wong, C., Feng, J., Wei, M., and Tenenbaum, J. B · 2022
Cited alongside, same era.
Google · 2023
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Human-like intuitive behavior and reasoning biases emerged in large language models but disappeared in chatgpt
Hagendorff, T., Fabi, S., and Kosinski, M · 2023
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Prompting frameworks for large language models: A survey, 2023
Liu, X., Wang, J., Sun, J., Yuan, X., Dong, G., Di, P., Wang, W., and Wang, D · 2023
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McCoy, R. T., Yao, S., Friedman, D., Hardy, M., and Griffiths, T. L · 2023
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Introducing mpt-30b: Raising the bar for open-source foundation models
MosaicML · 2023
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Language models show human-like content effects on reasoning
Dasgupta, I., Lampinen, A. K., Chan, S. C., Creswell, A., Kumaran, D., McClelland, J. L., and Hill, F · 2022
Cited alongside, same era.
Large language models are zero-shot reasoners
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y · 2022
Cited alongside, same era.
Can language models learn from explanations in context?
Lampinen, A. K., Dasgupta, I., Chan, S. C., Matthewson, K., Tessler, M. H., Creswell, A., McClelland, J. L., Wang, J. X., and Hill, F · 2022
Cited alongside, same era.
The computational roots of positivity and confirmation biases in reinforcement learning
Palminteri, S. and Lebreton, M · 2022
Cited alongside, same era.
The globalizability of temporal discounting
Ruggeri, K., Panin, A., Vdovic, M., Većkalov, B., Abdul-Salaam, N., Achterberg, J., Akil, C., Amatya, J., Amatya, K., Andersen, T. L., et al · 2022
Cited alongside, same era.
Emergent abilities of large language models
Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., Metzler, D., et al · 2022
Cited alongside, same era.
Playing repeated games with large language models, 2023
Akata, E., Schulz, L., Coda-Forno, J., Oh, S. J., Bethge, M., and Schulz, E · 2023
Cited alongside, same era.
OpenAI · 2023
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In-context impersonation reveals large language models’ strengths and biases
Salewski, L., Alaniz, S., Rio-Torto, I., Schulz, E., and Akata, Z · 2023
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Are emergent abilities of large language models a mirage?
Schaeffer, R., Miranda, B., and Koyejo, S · 2023
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Dynamic computational phenotyping of human cognition
Schurr, R., Reznik, D., Hillman, H., Bhui, R., and Gershman, S. J · 2023
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models, 2023
Srivastava, A. and authors · 2023
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Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al · 2023
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Large language models fail on trivial alterations to theory-of-mind tasks
Ullman, T · 2023
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Chain-of-thought prompting elicits reasoning in large language models, 2023
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., and Zhou, D · 2023
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Studying and improving reasoning in humans and machines
Yax, N., Anlló, H., and Palminteri, S · 2023
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Claude 2
Anthropic · 2024
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Visual cognition in multimodal large language models, 2024
Buschoff, L. M. S., Akata, E., Bethge, M., and Schulz, E · 2024
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Meta-in-context learning in large language models
Coda-Forno, J., Binz, M., Akata, Z., Botvinick, M., Wang, J., and Schulz, E · 2024
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Towards a transparent ai future: The call for less regulatory hurdles on open-source ai in europe
LAION · 2024
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The waluigi effect (mega-post)
Nardo, C · 2024
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