Linguistic Productivity in Large Language Models: Models Coerce, but do not Preempt

The paper investigates how large language models (LLMs) handle two usage‑based grammatical strategies: coercion, where a mismatched word is reinterpreted to fit its context, and preemption, where a plausible structure is avoided because it never appears in expected contexts. Experiments across models of varying sizes show that larger LLMs successfully recognize and produce constructional productivity in coercive situations, even with novel words, but they fail to exhibit preemptive behavior, lacking the negative evidence that would guide avoidance of unconventional but semantically viable constructions. The authors argue that coercion is detectable due to its surprising, semantically motivated nature, whereas preemption lacks such a signature, leading to an asymmetry where models coerce but do not preempt.