What Do Sparse Autoencoder Feature Discovery Scores Identify? From Activations to Interventions

Title:What Do Sparse Autoencoder Feature Discovery Scores Identify? From Activations to Interventions

Abstract:Sparse autoencoder (SAE) features are typically discovered from how their activations co-vary with behaviour, yet they are often used through interventions such as steering or ablation. We take intervention attribution, the first-order effect of a specified intervention on the model output, as our estimand. For a broad class of response functions, we show that observational feature-discovery evidence generally does not identify this attribution, and derive an exact gradient-based decomposition of the gap between linear-probe predictions and intervention attribution. Across four model-SAE pairs, median probe-to-attribution magnitude ratios range from tens to hundreds, while attributions closely track small-step steering. For single-latent ablations with nonzero effects, gradient predictions match the realized sign in 91-97% of cases, compared with 17-35% for probe-implied changes. Attribution-selected sets also produce larger absolute mean ablation effects on held-out prompts than sets selected by correlation, rank tests or probe coefficients. Together, these results establish intervention attribution as a principled, gradient-estimable target for selecting SAE features for intervention.

Machine Learning (cs.LG); Computation and Language (cs.CL); Machine Learning (stat.ML)

arXiv:2610.15850 [cs.LG]

(or arXiv:2610.15850v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.15850 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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