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This research investigates how neural networks navigate the tension between dominant patterns and rare exceptions, mirroring U-shaped learning observed in human language acquisition. The study reveals that when exceptions are scarce, models tend to overgeneralize toward regularities before eventually recovering, highlighting a specific competitive dynamic during the learning process. These findings offer critical insights into generalization errors and the robustness of deep learning systems when handling mixed data distributions.
The Dynamics of Quasiregular Neural Learning
arXiv:2609.26018v1 Announce Type: new Abstract: Many learning problems combine a dominant regularity with systematic exceptions. Motivated by U-shaped learning in language acquisition, we study this interaction in controlled quasiregular regression problems where regular and exceptional solutions are explicitly known. Neural networks can partially acquire exceptions, subsequently regress toward the dominant regularity, and finally recover. This overregularization becomes substantially stronger when exceptions are rare, despite their early acquisition, but does not emerge equally across all regularities considered. Our results isolate a simple form of competition between regularities and exceptions during neural learning.
Abstract:Many learning problems combine a dominant regularity with systematic exceptions. Motivated by U-shaped learning in language acquisition, we study this interaction in controlled quasiregular regression problems where regular and exceptional solutions are explicitly known. Neural networks can partially acquire exceptions, subsequently regress toward the dominant regularity, and finally recover. This overregularization becomes substantially stronger when exceptions are rare, despite their early acquisition, but does not emerge equally across all regularities considered. Our results isolate a simple form of competition between regularities and exceptions during neural learning.
Machine Learning (cs.LG)
arXiv:2609.26018 [cs.LG]
(or arXiv:2609.26018v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.26018 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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