Exemplars in Disguise: Pure Exemplar Models Mimic Abstraction-First Learning

Abstract:Whether idiosyncratic, item-specific knowledge is learned before abstract class-level generalizations, or vice versa, is a central question in language learning, with exemplar and abstraction-based theories making opposite predictions. Recent methods have claimed to show that, at least for large language models, abstract knowledge is learned first. We show that these methods fall short: pure memorizer models with no abstract representations can appear, by the same criteria, to learn either item-specific or class-level knowledge first, depending on their sensitivity to individual observations, with the transition point governed by the distributional properties of the input. We further argue that the distinction between item-specific and abstract knowledge may be ill-defined for distributed representations, as a word's class-level properties may not be separable from its item-specific properties.

Computation and Language (cs.CL)

arXiv:2608.00821 [cs.CL]

(or arXiv:2608.00821v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.00821 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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