diagnostic classifiers

**Diagnostic classifiers** is the **lightweight supervised models used to test whether targeted information can be extracted from neural representations** - they serve as diagnostics for internal encoding quality and layer-wise information flow. **What Is Diagnostic classifiers?** - **Definition**: Classifier is trained on frozen activations to predict predefined diagnostic labels. - **Design**: Typically uses constrained model capacity to avoid overfitting artifacts. - **Use**: Applied to syntax, semantics, factual cues, or control-signal detection. - **Outcome**: Performance indicates representational availability of target information. **Why Diagnostic classifiers Matters** - **Monitoring**: Tracks representational shifts during model scaling or fine-tuning. - **Failure Localization**: Identifies layers where critical information degrades. - **Research Utility**: Supports controlled hypotheses about internal feature encoding. - **Benchmarking**: Provides compact comparable metrics across model variants. - **Caveat**: Diagnostic success does not imply model actually uses that signal for outputs. **How It Is Used in Practice** - **Control Tasks**: Include random-label and lexical-baseline controls to detect probe leakage. - **Capacity Reporting**: Document classifier complexity and regularization settings clearly. - **Causal Extension**: Use interventions to test whether diagnosed features are functionally required. Diagnostic classifiers is **a practical representational health-check tool in interpretability workflows** - diagnostic classifiers are most reliable when paired with controls and causal follow-up experiments.