@inproceedings{wang-etal-2025-controllable,
title = "Controllable Style Arithmetic with Language Models",
author = "Wang, Weiqi and
Zhou, Wengang and
Zhang, Zongmeng and
Zhao, Jie and
Li, Houqiang",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.767/",
doi = "10.18653/v1/2025.acl-long.767",
pages = "15750--15799",
ISBN = "979-8-89176-251-0",
abstract = "Language models have shown remarkable capabilities in text generation, but precisely controlling their linguistic style remains challenging. Existing methods either lack fine-grained control, require extensive computation, or introduce significant latency. We propose Style Arithmetic (SA), a novel parameter-space approach that first extracts style-specific representations by analyzing parameter differences between models trained on contrasting styles, then incorporates these representations into a base model with precise control over style intensity. Our experiments show that SA achieves three key capabilities: controllability for precise adjustment of styles, transferability for effective style transfer across tasks, and composability for simultaneous control of multiple style dimensions. Compared to alternative methods, SA offers superior effectiveness while achieving optimal computational efficiency. Our approach opens new possibilities for flexible and efficient style control in language models."
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<abstract>Language models have shown remarkable capabilities in text generation, but precisely controlling their linguistic style remains challenging. Existing methods either lack fine-grained control, require extensive computation, or introduce significant latency. We propose Style Arithmetic (SA), a novel parameter-space approach that first extracts style-specific representations by analyzing parameter differences between models trained on contrasting styles, then incorporates these representations into a base model with precise control over style intensity. Our experiments show that SA achieves three key capabilities: controllability for precise adjustment of styles, transferability for effective style transfer across tasks, and composability for simultaneous control of multiple style dimensions. Compared to alternative methods, SA offers superior effectiveness while achieving optimal computational efficiency. Our approach opens new possibilities for flexible and efficient style control in language models.</abstract>
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%0 Conference Proceedings
%T Controllable Style Arithmetic with Language Models
%A Wang, Weiqi
%A Zhou, Wengang
%A Zhang, Zongmeng
%A Zhao, Jie
%A Li, Houqiang
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F wang-etal-2025-controllable
%X Language models have shown remarkable capabilities in text generation, but precisely controlling their linguistic style remains challenging. Existing methods either lack fine-grained control, require extensive computation, or introduce significant latency. We propose Style Arithmetic (SA), a novel parameter-space approach that first extracts style-specific representations by analyzing parameter differences between models trained on contrasting styles, then incorporates these representations into a base model with precise control over style intensity. Our experiments show that SA achieves three key capabilities: controllability for precise adjustment of styles, transferability for effective style transfer across tasks, and composability for simultaneous control of multiple style dimensions. Compared to alternative methods, SA offers superior effectiveness while achieving optimal computational efficiency. Our approach opens new possibilities for flexible and efficient style control in language models.
%R 10.18653/v1/2025.acl-long.767
%U https://aclanthology.org/2025.acl-long.767/
%U https://doi.org/10.18653/v1/2025.acl-long.767
%P 15750-15799
Markdown (Informal)
[Controllable Style Arithmetic with Language Models](https://aclanthology.org/2025.acl-long.767/) (Wang et al., ACL 2025)
ACL
- Weiqi Wang, Wengang Zhou, Zongmeng Zhang, Jie Zhao, and Houqiang Li. 2025. Controllable Style Arithmetic with Language Models. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15750–15799, Vienna, Austria. Association for Computational Linguistics.