Feature Interpretation › Key Parameters

When running your own feature interpretation pipeline, the key choices are:

• k (top examples): The number of top-activating examples to inspect per feature. Using 20 to 50 examples provides enough evidence to identify a dominant theme without overwhelming the annotator or the automated interpreter.
• Activation threshold: The minimum activation value to count a token as active. Setting this too low includes noise from near-zero activations; setting it too high misses weak but valid activations for rare concepts.
• Entropy threshold for monosemanticity: Values below 1.5 bits (log base 2) over a 10-category space correspond to one concept dominating 70% or more of the examples. Adjust this threshold based on the granularity of your category taxonomy.
• Attribution depth: How many layers back to trace when building feature circuits. Two to three layers captures the most direct causal structure; deeper tracing reveals more distant context but is computationally expensive and produces noisier attributions.
• PMI threshold for co-activation graphs: Filter edges below a normalized PMI of 0.5 to focus on the strongest co-activation relationships and avoid spurious correlations from common syntactic contexts.
• Steering coefficient: The scale $\alpha$ for feature steering experiments. Start small (0.1 to 1.0 times the typical active activation value) and increase until an effect is visible. Very large steering coefficients can move the residual stream far outside the training distribution and produce incoherent outputs.