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This paper disentangles statistical preemption from entrenchment in language models' avoidance of overgeneralizations through controlled experiments, finding that LMs exhibit abstract preemption rather than verb-specific preemption, with implications for human language learning.
This paper investigates whether Large Language Models exhibit the same usage-based linguistic productivity constraints (entrenchment and preemption) as humans, finding that models can reproduce coercion but fail to apply statistical preemption to avoid overgeneralization.