linear probing for syntax

**Linear probing for syntax** is the **probe methodology that uses linear classifiers to evaluate whether syntactic information is linearly accessible in hidden states** - it estimates how explicitly grammar-related structure is represented. **What Is Linear probing for syntax?** - **Definition**: Trains linear models on activations to predict syntactic labels such as dependency or POS classes. - **Rationale**: Linear probes emphasize readily available structure rather than complex nonlinear extraction. - **Layer Trends**: Syntax decodability often rises and shifts across middle and upper layers. - **Task Scope**: Can assess agreement, constituency signals, and grammatical-role separability. **Why Linear probing for syntax Matters** - **Linguistic Insight**: Provides interpretable measure of grammar encoding strength. - **Model Diagnostics**: Helps detect syntax weaknesses tied to generation errors. - **Comparability**: Linear probes enable consistent cross-model evaluation. - **Efficiency**: Low-complexity probes are fast and reproducible. - **Boundary**: Linear accessibility does not prove that model decisions rely on that signal. **How It Is Used in Practice** - **Balanced Datasets**: Use controlled syntax datasets with minimal lexical confounds. - **Layer Sweep**: Report performance by layer to capture representation progression. - **Intervention Pairing**: Validate syntax-use claims with targeted causal perturbations. Linear probing for syntax is **a focused method for measuring explicit grammatical structure in model states** - linear probing for syntax is valuable when interpreted as accessibility measurement rather than proof of causal mechanism.