tuned lens

**Tuned lens** is the **calibrated extension of logit lens that learns layer-specific affine translators before unembedding intermediate states** - it improves interpretability of intermediate predictions by correcting representation mismatch. **What Is Tuned lens?** - **Definition**: Learns lightweight transforms that map each layer activation into output-aligned space. - **Advantage**: Reduces systematic distortion present in naive direct unembedding projections. - **Output**: Produces more faithful layer-by-layer token distribution estimates. - **Training**: Lens parameters are fit post hoc without changing base model weights. **Why Tuned lens Matters** - **Interpretation Quality**: Gives clearer picture of computation progress across depth. - **Debug Precision**: Improves confidence when diagnosing layer-localized failures. - **Research Utility**: Supports stronger comparisons across prompts and model checkpoints. - **Method Progress**: Addresses major limitation of baseline logit-lens analysis. - **Operational Use**: Useful for monitoring internal state quality during model development. **How It Is Used in Practice** - **Calibration Data**: Fit tuned lenses on representative corpora aligned with deployment domains. - **Evaluation**: Check lens fidelity against true final-output behavior on held-out prompts. - **Pipeline Integration**: Use tuned-lens outputs as diagnostics alongside causal interpretability tools. Tuned lens is **a calibrated intermediate-state decoding method for transformer analysis** - tuned lens provides better intermediate prediction interpretability when trained and validated for the target model domain.