Feature Interpretation › Summary

Feature interpretation turns the abstract output of a sparse autoencoder into human-readable understanding. The key steps are:

• Activation pattern analysis: identify which inputs activate each feature, compute frequency statistics, and study activation histograms to distinguish monosemantic features (narrow, peaked distributions, low entropy) from polysemantic ones (broad distributions, high entropy).
• Feature naming: assign human-readable labels through careful manual inspection for small dictionaries, or through automated interpretation pipelines for large SAEs. Calibrate descriptions using held-out simulation to ensure they are predictive rather than merely descriptive.
• Monosemanticity measurement: use the Shannon entropy of the top-activating examples' concept distribution to quantify how cleanly a feature maps to a single concept, using thresholds of 1.5 bits (near-monosemantic) and 2.5 bits (polysemantic) as practical guides.
• Feature geometry: study how feature vectors cluster in activation space, identifying feature families (semantically related groups), linear relational structure (analogies between feature directions), and the effects of dictionary size on feature splitting and absorption.
• Causal validation: go beyond correlation by using feature steering (amplifying a feature and checking that the expected behavior appears) and feature ablation (suppressing a feature and checking that the behavior degrades). Together these test whether a feature is sufficient and necessary for a behavior.
• Feature circuits: trace causal connections between features across layers using attribution methods, revealing the functional pathways through which information flows from input tokens to output logits.

The limitations of current feature interpretation methods are substantial: descriptions are hard to validate rigorously, complete circuit analysis is computationally expensive, and the SAE training process itself introduces biases that compromise monosemanticity. Despite these challenges, feature interpretation has already produced specific findings into how language models encode linguistic structure, world knowledge, and reasoning patterns.

The next chapter on Mechanistic Interpretability builds on these foundations in a more targeted way. Where feature interpretation asks "what does each feature represent?", mechanistic interpretability asks "how do groups of features implement specific algorithms?" We will see how circuits involving attention heads and MLP layers can be reverse-engineered to reveal that transformers implement surprisingly structured computations, from simple lookup tables to multi-step reasoning chains, using the same building blocks we have studied throughout this handbook.