probing classifiers

**Probing classifiers** is the **auxiliary models trained on hidden states to test whether specific information is linearly or nonlinearly decodable** - they measure representational content without altering base model weights. **What Is Probing classifiers?** - **Definition**: A probe maps internal activations to labels such as POS tags, entities, or factual attributes. - **Layer Analysis**: Performance across layers indicates where information becomes explicitly encoded. - **Complexity Choice**: Probe capacity must be controlled to avoid extracting spurious signal. - **Interpretation**: Decodability implies information presence, not necessarily causal usage. **Why Probing classifiers Matters** - **Representation Mapping**: Provides quick quantitative view of what each layer contains. - **Model Comparison**: Supports systematic comparison between architectures and checkpoints. - **Debugging**: Identifies layers where expected signals are weak or corrupted. - **Benchmarking**: Widely used in interpretability and linguistic analysis literature. - **Limitations**: Strong probe accuracy can overstate functional importance without interventions. **How It Is Used in Practice** - **Capacity Control**: Use simple probes first and report baseline comparisons. - **Data Hygiene**: Avoid label leakage and prompt-template shortcuts in probe datasets. - **Causal Link**: Combine probing results with ablation or patching to test functional role. Probing classifiers is **a standard quantitative instrument for representational analysis** - probing classifiers are most informative when decodability findings are paired with causal evidence.