bert-interpretability-lower-syntactic-higher-semantic
IN premise — summaries/2026/08/24/wiki-BERT_language_model-chunk-2.md
Created 2026-08-24T17:11:06+00:00
BERT interpretability research (Clark et al. 2019, Khandelwal et al. 2018) found that lower layers encode local/syntactic features while higher layers encode semantic and task-specific features, and that attention weights do not reliably correspond to syntactic dependencies.
Summary
BERT's internal work is organized by depth: early layers deal with nearby words and grammar, while later layers build up meaning and task-specific understanding. This matters because looking at the model's attention patterns will not reliably show you which words it considers grammatically connected, so anyone trying to explain or audit BERT's decisions cannot lean on attention weights as a window into its structural reasoning.