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        <title>Can Activation Steering Capture Multidimensional Authorship Style?</title>
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        <namePart type="given">Hieu</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Can Activation Steering Capture Multidimensional Authorship Style?
%A Tran, Hieu
%A Bao, Calvin
%A Carpuat, Marine
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F tran-etal-2026-activation
%X 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.
%U https://aclanthology.org/2026.emnlp-main.2669/
%P 60731-60754

Markdown (Informal)

[Can Activation Steering Capture Multidimensional Authorship Style?](https://aclanthology.org/2026.emnlp-main.2669/) (Tran et al., EMNLP 2026)

• Can Activation Steering Capture Multidimensional Authorship Style? (Tran et al., EMNLP 2026)

ACL

• Hieu Tran, Calvin Bao, and Marine Carpuat. 2026. Can Activation Steering Capture Multidimensional Authorship Style?. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 60731–60754, Budapest, Hungary. Association for Computational Linguistics.