Modeling Human-like Concept Learning with Bayesian Inference over Natural Language
CoRR(2023)
摘要
We model learning of abstract symbolic concepts by performing Bayesian inference over utterances in natural language. For efficient inference, we use a large language model as a proposal distribution. We fit a prior to human data to better model human learners, and evaluate on both generative and logical concepts.
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