Can Activation Steering Capture Multidimensional Authorship Style?

Title:Can Activation Steering Capture Multidimensional Authorship Style?

Abstract:Activation steering has shown promise for controlling LLM generation along well-defined attributes, but it remains unclear whether it can handle the multidimensional and hard-to-define nature of authorship style. We ask whether structured contrastive prompting along rhetorically-motivated dimensions can construct rich style representations directly in activation space, bypassing the need for natural language style descriptors or dedicated training. We find that the resulting directions share a common authorship backbone while conflicting on aspect-specific residuals that carry genuine stylistic signal, explaining why naive aggregation fails. We operationalize this in Aspect-Aware Activation Steering (A3S), a training-free framework that merges per-aspect contrastive directions with interference-aware aggregation and tunes steering strength per instance. A3S improves authorship style transfer where it is genuinely multi-aspect, outperforms a trained baseline in preference evaluations on out-of-domain benchmarks, and keeps target-exemplar overlap consistently low.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2609.04792 [cs.CL]

(or arXiv:2609.04792v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.04792 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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