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Human-like Few-Shot Learning via Bayesian Reasoning over Natural Language

Kevin Ellis

Hall C2 (level 1 gate 9 south of food court)
[ ] [ Visit Oral 3A Neuro ]
Wed 13 Dec 8:30 a.m. — 8:45 a.m. PST


A core tension in models of concept learning is that the model must carefully balance the tractability of inference against the expressivity of the hypothesis class. Humans, however, can efficiently learn a broad range of concepts. We introduce a model of inductive learning that seeks to be human-like in that sense.It implements a Bayesian reasoning process where a language model first proposes candidate hypotheses expressed in natural language, which are then re-weighed by a prior and a likelihood.By estimating the prior from human data, we can predict human judgments on learning problems involving numbers and sets, spanning concepts that are generative, discriminative, propositional, and higher-order.

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