@inproceedings{zhang-etal-2025-personalized,
title = "Personalized Text Generation with Contrastive Activation Steering",
author = "Zhang, Jinghao and
Liu, Yuting and
Wang, Wenjie and
Liu, Qiang and
Wu, Shu and
Wang, Liang and
Chua, Tat-Seng",
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.353/",
doi = "10.18653/v1/2025.acl-long.353",
pages = "7128--7141",
ISBN = "979-8-89176-251-0",
abstract = "Personalized text generation aims to infer users' writing style preferences from their historical texts and generate outputs that faithfully reflect these stylistic characteristics. Existing solutions primarily adopt two paradigms: retrieval-augmented generation (RAG) and parameter-efficient fine-tuning (PEFT). While these approaches have advanced the field, they suffer from two critical limitations: (1) the entanglement of content semantics and stylistic patterns in historical texts impedes accurate modeling of user-specific writing preferences; and (2) scalability challenges arising from both RAG{'}s inference latency by retrieval operations and PEFT{'}s parameter storage requirements for per user model. To overcome these limitations, we propose StyleVector, a training-free framework that disentangles and represents personalized writing style as a vector in LLM{'}s activation space, enabling style-steered generation during inference without requiring costly retrieval or parameter storage. Comprehensive experiments demonstrate that our framework achieves a significant 8{\%} relative improvement in personalized generation while reducing storage requirements by 1700 $\times$ over PEFT method."
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<abstract>Personalized text generation aims to infer users’ writing style preferences from their historical texts and generate outputs that faithfully reflect these stylistic characteristics. Existing solutions primarily adopt two paradigms: retrieval-augmented generation (RAG) and parameter-efficient fine-tuning (PEFT). While these approaches have advanced the field, they suffer from two critical limitations: (1) the entanglement of content semantics and stylistic patterns in historical texts impedes accurate modeling of user-specific writing preferences; and (2) scalability challenges arising from both RAG’s inference latency by retrieval operations and PEFT’s parameter storage requirements for per user model. To overcome these limitations, we propose StyleVector, a training-free framework that disentangles and represents personalized writing style as a vector in LLM’s activation space, enabling style-steered generation during inference without requiring costly retrieval or parameter storage. Comprehensive experiments demonstrate that our framework achieves a significant 8% relative improvement in personalized generation while reducing storage requirements by 1700 \times over PEFT method.</abstract>
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%0 Conference Proceedings
%T Personalized Text Generation with Contrastive Activation Steering
%A Zhang, Jinghao
%A Liu, Yuting
%A Wang, Wenjie
%A Liu, Qiang
%A Wu, Shu
%A Wang, Liang
%A Chua, Tat-Seng
%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 zhang-etal-2025-personalized
%X Personalized text generation aims to infer users’ writing style preferences from their historical texts and generate outputs that faithfully reflect these stylistic characteristics. Existing solutions primarily adopt two paradigms: retrieval-augmented generation (RAG) and parameter-efficient fine-tuning (PEFT). While these approaches have advanced the field, they suffer from two critical limitations: (1) the entanglement of content semantics and stylistic patterns in historical texts impedes accurate modeling of user-specific writing preferences; and (2) scalability challenges arising from both RAG’s inference latency by retrieval operations and PEFT’s parameter storage requirements for per user model. To overcome these limitations, we propose StyleVector, a training-free framework that disentangles and represents personalized writing style as a vector in LLM’s activation space, enabling style-steered generation during inference without requiring costly retrieval or parameter storage. Comprehensive experiments demonstrate that our framework achieves a significant 8% relative improvement in personalized generation while reducing storage requirements by 1700 \times over PEFT method.
%R 10.18653/v1/2025.acl-long.353
%U https://aclanthology.org/2025.acl-long.353/
%U https://doi.org/10.18653/v1/2025.acl-long.353
%P 7128-7141
Markdown (Informal)
[Personalized Text Generation with Contrastive Activation Steering](https://aclanthology.org/2025.acl-long.353/) (Zhang et al., ACL 2025)
ACL
- Jinghao Zhang, Yuting Liu, Wenjie Wang, Qiang Liu, Shu Wu, Liang Wang, and Tat-Seng Chua. 2025. Personalized Text Generation with Contrastive Activation Steering. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7128–7141, Vienna, Austria. Association for Computational Linguistics.