# DRESSing Up LLM: Efficient Stylized Question-Answering via Style Subspace Editing

arXiv: https://arxiv.org/abs/2501.14371 · HTML: https://mlsanity.com/paper/2501.14371/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2025.

## Built on by (most cited first, 10 of 10)

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- [Search for Truth from Reasoning: A Dynamic Representation Editing Framework for Steering LLM Trajectories](https://mlsanity.com/paper/2606.28589/) (2026)
- [An Empirical Study of Sample Selection Strategies for Large Language Model Repair](https://mlsanity.com/paper/2510.20428/) (2025)
- [The Blessing of Dimensionality: How Near-Orthogonality in High-Dimensional Spaces Explains Temporal Portability](https://mlsanity.com/paper/2607.20301/) (2026)
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## Abstract (arXiv)

We introduce DRESS, a novel approach for generating stylized large language model (LLM) responses through representation editing. Existing methods like prompting and fine-tuning are either insufficient for complex style adaptation or computationally expensive, particularly in tasks like NPC creation or character role-playing. Our approach leverages the over-parameterized nature of LLMs to disentangle a style-relevant subspace within the model's representation space to conduct representation editing, ensuring a minimal impact on the original semantics. By applying adaptive editing strengths, we dynamically adjust the steering vectors in the style subspace to maintain both stylistic fidelity and semantic integrity. We develop two stylized QA benchmark datasets to validate the effectiveness of DRESS, and the results demonstrate significant improvements compared to baseline methods such as prompting and ITI. In short, DRESS is a lightweight, train-free solution for enhancing LLMs with flexible and effective style control, making it particularly useful for developing stylized conversational agents. Codes and benchmark datasets are available at https://github.com/ArthurLeoM/DRESS-LLM.