Signatures of Steerability in Activation Space of Language Models
Steering language models using a set of contrastive representations has been a canonical and computationally efficient method for controlling model behavior. Despite this success in controlling certain model behaviors, the effectiveness of activation steering varies markedly across concepts; the generalization properties of steering vectors are often considered a function of the dataset used to construct them. We make this dataset-dependence claim more rigorous and show that simple separation metrics strongly correlate with the downstream steerability of language models across diverse settings, even after controlling for layers and dataset effects. Beyond prediction, we provide evidence f...
| Authors | Prajjwal Bhattarai, Tuka Alhanai |
|---|---|
| Primary category | cs.LG |
| Published | 12 September 2026 |
| Last updated at source | 12 September 2026 |
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