Do Language Models Know What Not to Say? Causal Evidence for Statistical Preemption in LLMs
For linguists and cognitive scientists, this provides causal evidence that neural language models learn negative linguistic knowledge via distributional competition, supporting Construction Grammar theory.
The paper shows that large language models acquire knowledge of unacceptable linguistic forms through statistical preemption, where exposure to conventional forms blocks unattested alternatives, with surprisal patterns correlating strongly with human judgments (r=0.79) across 120 verb-construction pairings.
How do learners acquire knowledge of what is unacceptable without negative evidence? Construction Grammar proposes statistical preemption: exposure to a conventional form (e.g., "donated the books to the library") preempts structurally possible but unattested alternatives ("*donated the library the books"). We present a computational study that, for the first time, directly dissociates statistical preemption from the competing entrenchment hypothesis in large language models within a single converging design. Across four experiments spanning 120 English verb-construction pairings (dative, causative, locative), we show that (1) LLM surprisal patterns correlate strongly with human acceptability judgments ($r = 0.79$), validated against three independent behavioral datasets; (2) these patterns are driven by competing-form frequency rather than overall verb frequency, confirmed by non-circular partial correlations; (3) preemption sensitivity scales as a power law with model size; and (4) a controlled fine-tuning intervention causally demonstrates that manipulating competing-form frequencies shifts preemption behavior in the predicted direction, with reverse-direction controls ruling out frequency-sensitivity confounds. These results provide converging evidence that neural language models acquire negative linguistic knowledge through distributional competition, the core mechanism posited by Construction Grammar.