Controlling Chat Style in Language Models via Single-Direction Editing

Zhenyu Xu; Victor S. Sheng

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arXiv · 2603.03324

Controlling Chat Style in Language Models via Single-Direction Editing

Zhenyu Xu·Victor S. Sheng

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Abstract

Controlling stylistic attributes in large language models (LLMs) remains challenging, with existing approaches relying on either prompt engineering or post-training alignment. This paper investigates this challenge through the lens of representation engineering, testing the hypothesis that distinct stylistic attributes - from emotional tone to linguistic structure - are encoded as linear directions in the model's activation space. We provide strong empirical evidence for this hypothesis across a wide range of styles and, based on this finding, present a lightweight, training-free method for precise style control. Our approach supports linear style composition, enhances safety by ablating undesirable behaviors, and, as confirmed by experiments on over a dozen models, achieves high style adherence while preserving core capabilities at minimal computational cost.

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Zhenyu Xu, Victor S. Sheng. 2026-02-10. Controlling Chat Style in Language Models via Single-Direction Editing. https://arxiv.org/abs/2603.03324

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