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In-context learning in natural and artificial intelligence

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Jagadish, A., Dasgupta, I., Lerousseau, J., & Binz, M. (2024). In-context learning in natural and artificial intelligence.

Cite as: https://hdl.handle.net/21.11116/0000-000F-5FE6-4
In-context learning refers to the ability of a neural network to learn from information presented in its context. While traditional learning in neural networks requires adjusting network weights for every new task, in-context learning operates purely by updating internal activations without needing any updates to network weights. The emergence of this ability in large language models has led to a paradigm shift in machine learning and has forced researchers to reconceptualize how they think about learning in neural networks. Looking beyond language models, we can find in-context learning in many computational models relevant to cognitive science, including those that emerge from meta-learning.