What counts as evidence for syntactic structure? In traditional generative grammar, systematic contrasts in grammaticality such as subject-auxiliary inversion and the licensing of parasitic gaps are taken as evidence for an internal, hierarchical grammar. In this paper, we test whether large language models (LLMs), trained only on surface forms, reproduce these contrasts in ways that imply an underlying structural representation. We focus on two classic constructions: subject-auxiliary inversion (testing recognition of the subject boundary) and parasitic gap licensing (testing abstract dependency structure). We evaluate models including GPT-4 and LLaMA-3 using prompts eliciting acceptability ratings. Results show that LLMs reliably distinguish between grammatical and ungrammatical variants in both constructions, and as such support that they are sensitive to structure and not just linear order. Structural generalizations, distinct from cognitive knowledge, emerge from predictive training on surface forms, suggesting functional sensitivity to syntax without explicit encoding.

翻译：什么证据可支撑句法结构的存在？在传统生成语法中，诸如主语-助动词倒装与寄生空位允准等语法性上的系统性对立，被视为内在层级化语法的证据。本文检验仅基于表层形式训练的大语言模型（LLMs）是否以暗示底层结构表征的方式复现这些对立。我们聚焦于两种经典构式：主语-助动词倒装（用于测试主语边界的识别）与寄生空位允准（用于测试抽象依存结构）。我们通过提示引导可接受度评分的方式评估包括GPT-4和LLaMA-3在内的模型。结果表明，LLMs在两种构式中均能可靠区分语法正确与不正确的变体，由此支持其对结构（而非仅线性顺序）具有敏感性。区别于认知知识的结构性概括，从表层形式的预测性训练中涌现，表明模型在无显式编码的情况下对句法具备功能性敏感。