Equation 4 · Probing, Sparse Autoencoders, Patching, and Steering: The Main Interpretability Methods, Compared
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where is the frozen activation the network produces at layer for input x , and w, b are the probe’s own parameters, trained on labelled examples the network never saw during its own training. Nothing in that objective touches the network’s weights or its downstream computation. A probe reports only whether some linear function of this one activation predicts the label well.
Sources cited in the article section
- [1] Understanding Intermediate Layers Using Linear Classifier Probes ↗
- [2] What You Can Cram into a Single Vector: Probing Sentence Embeddings for Linguistic Properties ↗
- [3] Designing and Interpreting Probes with Control Tasks ↗
- [4] Probing Classifiers: Promises, Shortcomings, and Advances ↗
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