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.
View on arXiv@article{guan2025_2503.17003,
title={ A Survey on Personalized Alignment -- The Missing Piece for Large Language Models in Real-World Applications },
author={ Jian Guan and Junfei Wu and Jia-Nan Li and Chuanqi Cheng and Wei Wu },
journal={arXiv preprint arXiv:2503.17003},
year={ 2025 }
}