@inproceedings{liu-etal-2025-llms,
title = "{LLM}s + Persona-Plug = Personalized {LLM}s",
author = "Liu, Jiongnan and
Zhu, Yutao and
Wang, Shuting and
Wei, Xiaochi and
Min, Erxue and
Lu, Yu and
Wang, Shuaiqiang and
Yin, Dawei and
Dou, Zhicheng",
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.461/",
doi = "10.18653/v1/2025.acl-long.461",
pages = "9373--9385",
ISBN = "979-8-89176-251-0",
abstract = "Personalization plays a critical role in numerous language tasks and applications, since users with the same requirements may prefer diverse outputs based on their interests. This has led to the development of various personalized approaches aimed at adapting large language models (LLMs) to generate customized outputs aligned with user preferences. Some of them involve fine-tuning a unique personalized LLM for each user, which is too expensive for widespread application. Alternative approaches introduce personalization information in a plug-and-play manner by retrieving the user{'}s relevant historical texts as demonstrations. However, this retrieval-based strategy may break the continuity of the user history and fail to capture the user{'}s overall styles and patterns, hence leading to sub-optimal performance. To address these challenges, we propose a novel personalized LLM model, PPlug. It constructs a user-specific embedding for each individual by modeling all her historical contexts through a lightweight plug-in user embedder module. By attaching this embedding to the task input, LLMs can better understand and capture user habits and preferences, thereby producing more personalized outputs without tuning their parameters. Extensive experiments on various tasks in the language model personalization (LaMP) benchmark demonstrate that the proposed model significantly outperforms existing personalized LLM approaches."
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<abstract>Personalization plays a critical role in numerous language tasks and applications, since users with the same requirements may prefer diverse outputs based on their interests. This has led to the development of various personalized approaches aimed at adapting large language models (LLMs) to generate customized outputs aligned with user preferences. Some of them involve fine-tuning a unique personalized LLM for each user, which is too expensive for widespread application. Alternative approaches introduce personalization information in a plug-and-play manner by retrieving the user’s relevant historical texts as demonstrations. However, this retrieval-based strategy may break the continuity of the user history and fail to capture the user’s overall styles and patterns, hence leading to sub-optimal performance. To address these challenges, we propose a novel personalized LLM model, PPlug. It constructs a user-specific embedding for each individual by modeling all her historical contexts through a lightweight plug-in user embedder module. By attaching this embedding to the task input, LLMs can better understand and capture user habits and preferences, thereby producing more personalized outputs without tuning their parameters. Extensive experiments on various tasks in the language model personalization (LaMP) benchmark demonstrate that the proposed model significantly outperforms existing personalized LLM approaches.</abstract>
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%0 Conference Proceedings
%T LLMs + Persona-Plug = Personalized LLMs
%A Liu, Jiongnan
%A Zhu, Yutao
%A Wang, Shuting
%A Wei, Xiaochi
%A Min, Erxue
%A Lu, Yu
%A Wang, Shuaiqiang
%A Yin, Dawei
%A Dou, Zhicheng
%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 liu-etal-2025-llms
%X Personalization plays a critical role in numerous language tasks and applications, since users with the same requirements may prefer diverse outputs based on their interests. This has led to the development of various personalized approaches aimed at adapting large language models (LLMs) to generate customized outputs aligned with user preferences. Some of them involve fine-tuning a unique personalized LLM for each user, which is too expensive for widespread application. Alternative approaches introduce personalization information in a plug-and-play manner by retrieving the user’s relevant historical texts as demonstrations. However, this retrieval-based strategy may break the continuity of the user history and fail to capture the user’s overall styles and patterns, hence leading to sub-optimal performance. To address these challenges, we propose a novel personalized LLM model, PPlug. It constructs a user-specific embedding for each individual by modeling all her historical contexts through a lightweight plug-in user embedder module. By attaching this embedding to the task input, LLMs can better understand and capture user habits and preferences, thereby producing more personalized outputs without tuning their parameters. Extensive experiments on various tasks in the language model personalization (LaMP) benchmark demonstrate that the proposed model significantly outperforms existing personalized LLM approaches.
%R 10.18653/v1/2025.acl-long.461
%U https://aclanthology.org/2025.acl-long.461/
%U https://doi.org/10.18653/v1/2025.acl-long.461
%P 9373-9385
Markdown (Informal)
[LLMs + Persona-Plug = Personalized LLMs](https://aclanthology.org/2025.acl-long.461/) (Liu et al., ACL 2025)
ACL
- Jiongnan Liu, Yutao Zhu, Shuting Wang, Xiaochi Wei, Erxue Min, Yu Lu, Shuaiqiang Wang, Dawei Yin, and Zhicheng Dou. 2025. LLMs + Persona-Plug = Personalized LLMs. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9373–9385, Vienna, Austria. Association for Computational Linguistics.