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Large language models have shown tremendous performance in a variety of tasks.
Learning to learn using gradient descent
Sepp Hochreiter, A Steven Younger, and Peter R Conwell · 2001
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Learning to reinforcement learn
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Meta-learning with memory-augmented neural networks
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Simple regression models
Jan Malte Lichtenberg and Özgür Simsek · 2017
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Deconstructing the human algorithms for exploration
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Meta-learning of sequential strategies, 2019
Pedro A. Ortega, Jane X. Wang, Mark Rowland, Tim Genewein, Zeb Kurth-Nelson, Razvan Pascanu, Nicolas Heess, Joel Veness, Alex Pritzel, Pablo Sprechmann, Siddhant M. Jayakumar, Tom McGrath, Kevin Miller, Mohammad Azar, Ian Osband, Neil Rabinowitz, András György, Silvia Chiappa, Simon Osindero, Yee Whye Teh, Hado van Hasselt, Nando de Freitas, Matthew Botvinick, and Shane Legg · 2019
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Language models are few-shot learners, 2020
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level
Iddo Drori, Sarah Zhang, Reece Shuttleworth, Leonard Tang, Albert Lu, Elizabeth Ke, Kevin Liu, Linda Chen, Sunny Tran, Newman Cheng, et al · 2022
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Emergent analogical reasoning in large language models
Taylor Webb, Keith J Holyoak, and Hongjing Lu · 2022
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Data distributional properties drive emergent in-context learning in transformers, 2022
Stephanie C. Y. Chan, Adam Santoro, Andrew K. Lampinen, Jane X. Wang, Aaditya Singh, Pierre H. Richemond, Jay McClelland, and Felix Hill · 2022
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Rethinking the role of demonstrations: What makes in-context learning work?, 2022
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Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, and Max Vladymyrov · 2022
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What learning algorithm is in-context learning? investigations with linear models
Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, and Denny Zhou · 2022
Chatgpt for good? on opportunities and challenges of large language models for education
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Tabllm: Few-shot classification of tabular data with large language models, 2023
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Andrew Kyle Lampinen · 2022
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Modeling human exploration through resource-rational reinforcement learning
Marcel Binz and Eric Schulz · 2022
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Gpts are gpts: An early look at the labor market impact potential of large language models
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Who models the models that model models? an exploration of gpt-3’s in-context model fitting ability
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What can transformers learn in-context? a case study of simple function classes, 2023
Shivam Garg, Dimitris Tsipras, Percy Liang, and Gregory Valiant · 2023
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Inducing anxiety in large language models increases exploration and bias
Julian Coda-Forno, Kristin Witte, Akshay K Jagadish, Marcel Binz, Zeynep Akata, and Eric Schulz · 2023
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Thilo Hagendorff · 2023
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https://platform.openai.com
Openai api · 2023
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