Language models align with human judgments on key grammatical constructions

Do Large Language Models (LLMs) make human-like linguistic generalizations? Dentella et al. (5) (DGL) prompt several LLMs ("Is the following sentence grammatically correct in English?") to elicit grammaticality judgments of 80 English sentences, concluding that LLMs demonstrate a "yes-response bias" and a "failure to distinguish grammatical from ungrammatical sentences". We re-evaluate LLM performance using well-established practices and find that DGL's data in fact provide evidence for just how well LLMs capture human linguistic judgments. Children learn to produce well-formed sentences without necessarily being able to articulate the underlying grammatical rules, a distinction long noted in linguistics (e.g., 1; 6; 3). DGL blur this distinction: their task requires not just grammatical competence, but also knowing what "grammatically correct" means.