Distinguishing rule- and exemplar-based generalization in learning systems

• URL: http://arxiv.org/abs/2110.04328v1
• Date: Fri, 8 Oct 2021 18:37:59 GMT
• Title: Distinguishing rule- and exemplar-based generalization in learning systems
• Authors: Ishita Dasgupta, Erin Grant, Thomas L. Griffiths
• Abstract summary: We investigate two distinct inductive biases: feature-level bias and exemplar-vs-rule bias. We find that most standard neural network models have a propensity towards exemplar-based extrapolation. We discuss the implications of these findings for research on data augmentation, fairness, and systematic generalization.
• Score: 10.396761067379195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the increasing scale of datasets in machine learning, generalization to unseen regions of the data distribution remains crucial. Such extrapolation is by definition underdetermined and is dictated by a learner's inductive biases. Machine learning systems often do not share the same inductive biases as humans and, as a result, extrapolate in ways that are inconsistent with our expectations. We investigate two distinct such inductive biases: feature-level bias (differences in which features are more readily learned) and exemplar-vs-rule bias (differences in how these learned features are used for generalization). Exemplar- vs. rule-based generalization has been studied extensively in cognitive psychology, and, in this work, we present a protocol inspired by these experimental approaches for directly probing this trade-off in learning systems. The measures we propose characterize changes in extrapolation behavior when feature coverage is manipulated in a combinatorial setting. We present empirical results across a range of models and across both expository and real-world image and language domains. We demonstrate that measuring the exemplar-rule trade-off while controlling for feature-level bias provides a more complete picture of extrapolation behavior than existing formalisms. We find that most standard neural network models have a propensity towards exemplar-based extrapolation and discuss the implications of these findings for research on data augmentation, fairness, and systematic generalization.

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