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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