Probing Classifiers › Summary

Probing classifiers are a methodological foundation for understanding what language models encode in their representations. By training a simple classifier on frozen model representations and measuring its accuracy on linguistic tasks, you can determine what information is linearly accessible at each layer.

The key ideas from this chapter:

• A probe is intentionally simple (usually logistic regression) so that its success reflects properties of the representations, not the probe's own computation.
• Probing tasks span syntactic (POS, dependency, tree depth), semantic (NER, SRL, coreference), and surface-level (position, length) properties. Good task design includes balanced classes, varied contexts, and controls for surface confounds.
• Probe accuracy alone is insufficient; selectivity (accuracy minus control accuracy) is the correct measure. A high selectivity score means the probe's success is specific to the real labels, not general memorization.
• Control tasks establish a baseline by replacing real labels with random ones, measuring how much of the probe.s success comes from linguistic signal versus memorization capacity.
• The minimum description length framework provides a more principled alternative to accuracy-based evaluation, measuring how much the representations compress the labels.
• Structural probing (Hewitt and Manning 2019) extends beyond classification to ask whether the metric geometry of representation space mirrors syntactic parse tree structure.
• Probing tells you about information accessibility, not causal relevance to model predictions. Methods like activation patching provide the causal analysis that probing cannot.
• BERT and similar models robustly encode POS in early-middle layers, syntactic structure in middle layers, and semantic properties in later layers, recapitulating the classical NLP pipeline in their layer structure.

In the next chapter, we extend probing to compare across layers systematically, building probing profiles that reveal where different types of linguistic knowledge are concentrated in the network.