Human-like Few-Shot Learning via Bayesian Reasoning over Natural
Language
- URL: http://arxiv.org/abs/2306.02797v3
- Date: Fri, 29 Sep 2023 16:45:41 GMT
- Title: Human-like Few-Shot Learning via Bayesian Reasoning over Natural
Language
- Authors: Kevin Ellis
- Abstract summary: Humans can efficiently learn a broad range of concepts.
We introduce a model of inductive learning that seeks to be human-like in that sense.
- Score: 7.11993673836973
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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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