@inproceedings{guan-etal-2025-survey,
title = "A Survey on Personalized {A}lignment{---}{T}he Missing Piece for Large Language Models in Real-World Applications",
author = "Guan, Jian and
Wu, Junfei and
Li, Jia-Nan and
Cheng, Chuanqi and
Wu, Wei",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.277/",
doi = "10.18653/v1/2025.findings-acl.277",
pages = "5313--5333",
ISBN = "979-8-89176-256-5",
abstract = "Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their transition to real-world applications reveals a critical limitation: the inability to adapt to individual preferences while maintaining alignment with universal human values. Current alignment techniques adopt a one-size-fits-all approach that fails to accommodate users' diverse backgrounds and needs. This paper presents the first comprehensive survey of personalized alignment{---}a paradigm that enables LLMs to adapt their behavior within ethical boundaries based on individual preferences. We propose a unified framework comprising preference memory management, personalized generation, and feedback-based alignment, systematically analyzing implementation approaches and evaluating their effectiveness across various scenarios. By examining current techniques, potential risks, and future challenges, this survey provides a structured foundation for developing more adaptable and ethically-aligned LLMs."
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%0 Conference Proceedings
%T A Survey on Personalized Alignment—The Missing Piece for Large Language Models in Real-World Applications
%A Guan, Jian
%A Wu, Junfei
%A Li, Jia-Nan
%A Cheng, Chuanqi
%A Wu, Wei
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F guan-etal-2025-survey
%X Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their transition to real-world applications reveals a critical limitation: the inability to adapt to individual preferences while maintaining alignment with universal human values. Current alignment techniques adopt a one-size-fits-all approach that fails to accommodate users’ diverse backgrounds and needs. This paper presents the first comprehensive survey of personalized alignment—a paradigm that enables LLMs to adapt their behavior within ethical boundaries based on individual preferences. We propose a unified framework comprising preference memory management, personalized generation, and feedback-based alignment, systematically analyzing implementation approaches and evaluating their effectiveness across various scenarios. By examining current techniques, potential risks, and future challenges, this survey provides a structured foundation for developing more adaptable and ethically-aligned LLMs.
%R 10.18653/v1/2025.findings-acl.277
%U https://aclanthology.org/2025.findings-acl.277/
%U https://doi.org/10.18653/v1/2025.findings-acl.277
%P 5313-5333
Markdown (Informal)
[A Survey on Personalized Alignment—The Missing Piece for Large Language Models in Real-World Applications](https://aclanthology.org/2025.findings-acl.277/) (Guan et al., Findings 2025)
ACL