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method:term-importance-analysis-via-ablationTerm Importance Analysis via Ablation
An algorithm that determines the marginal effect of n-th order path terms by running the model multiple times with frozen attention patterns and progressively replacing activations
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Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- Robustness check ablating over five steering coefficient values to test whether SP/AS inversion is an artifact of steering strength
- Intervention type that sets activations to zero, used for interpretability analysis
- Three-setting ablation (Before Training, Without CL, With CL) to isolate the contribution of contrastive learning
- Intervention method that removes a direction from residual stream activations to disrupt corresponding behavior
- Gradient-based attribution approximates ablation impact, enabling fast search for causally important features.
- Classical techniques to interrogate regulative capacity of embryos and neural crest by tissue removal or transplantation.
- Sequential zeroing out of high-contribution heads to verify their functional specialization for persona control
- Property requiring that ablating a truth direction shifts model output from truthful to false without other side effects