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In-context learning agents are asymmetric belief updaters
We study the in-context learning dynamics of large language models (LLMs) using three instrumental learning tasks adapted from cognitive psychology.
Human-like Category Learning by Injecting Ecological Priors from Large Language Models into Neural Networks
We tackle the second challenge by deriving rational agents adapted to these tasks using the framework of meta-learning, leading to a class of models called ecologically rational meta-learned inference (ERMI).
Inducing anxiety in large language models increases exploration and bias
We focus on the Generative Pre-Trained Transformer 3. 5 and subject it to tasks commonly studied in psychiatry.
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