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.

翻译：大语言模型（LLMs）已展现出卓越的能力，但其在现实应用中的落地却暴露出一个关键局限：难以在保持与普世人类价值观对齐的同时适应个体偏好。当前的对齐技术采用"一刀切"的方法，无法适应用户多样化的背景与需求。本文首次对个性化对齐——一种使大语言模型能够在伦理边界内根据个体偏好调整其行为的范式——进行了全面综述。我们提出了一个包含偏好记忆管理、个性化生成和基于反馈的对齐的统一框架，系统分析了各类实现方法，并评估了它们在不同场景下的有效性。通过审视现有技术、潜在风险与未来挑战，本综述为开发更具适应性和伦理对齐性的大语言模型提供了结构化基础。