Large Language Models (LLMs) exhibit a robust mastery of syntax when processing and generating text.While this suggests internalized understanding of hierarchical syntax and dependency relations, the precise mechanism by which they represent syntactic structure is an open area within interpretability research.Probing provides one way to identify syntactic mechanisms linearly encoded in activations; however, no comprehensive study has yet established whether a model's probing accuracy reliably predicts its downstream syntactic performance.Adopting a "mechanisms vs. outcomes" framework, we evaluate 32 open-weight transformer models and find that syntactic features extracted via probing fail to predict outcomes of targeted syntax evaluations across English linguistic phenomena.Our results highlight a substantial disconnect between latent syntactic representations found via probing and observable syntactic behaviors in downstream tasks.

Eric Mitchell, Joseph Noh, Siyan Li, Will Armstrong, Ananth Agarwal, Patrick Liu, Chelsea Finn, Christopher Manning. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.