让大模型学会个性化理解用户偏好，从千人一面到人人不同。
From 1,000,000 Users to Every User: Scaling Up Personalized Preference for User-level Alignment
- 构建心理与行为维度的偏好空间，用人物画像推断用户需求。
- 创建超百万级个性化偏好数据集，准确率提升17.06%。
- 适合需要精准适配用户、重视个性化体验的研究者。
大语言模型传统上采用统一标准对齐，忽略用户间价值观与需求差异。本文提出可扩展的个性化对齐框架，建立涵盖心理与行为维度的系统性偏好空间，并设计多样化的角色表征以增强真实场景下的偏好推断能力。在此基础上，构建了包含超过130万条个性化偏好样本的大型数据集AlignX，提出两种互补对齐方法：基于上下文的对齐直接关联角色表征，偏好桥接对齐建模中间偏好分布。大量实验表明，该方法在四个基准测试中平均准确率提升17.06%，展现出对新偏好强适应性、小样本鲁棒性及精确偏好控制能力，验证了面向用户自适应的AI系统可行性。
原文摘要 · Abstract (English)
Large language models (LLMs) have traditionally been aligned through one-size-fits-all approaches that assume uniform human preferences, fundamentally overlooking the diversity in user values and needs. This paper introduces a comprehensive framework for scalable personalized alignment of LLMs. We establish a systematic preference space characterizing psychological and behavioral dimensions, alongside diverse persona representations for robust preference inference in real-world scenarios. Building upon this foundation, we introduce \textsc{AlignX}, a large-scale dataset of over 1.3 million personalized preference examples, and develop two complementary alignment approaches: \textit{in-context alignment} directly conditioning on persona representations and \textit{preference-bridged alignment} modeling intermediate preference distributions. Extensive experiments demonstrate substantial improvements over existing methods, with an average 17.06\% accuracy gain across four benchmarks while exhibiting a strong adaptation capability to novel preferences, robustness to limited user data, and precise preference controllability. These results validate our approach toward user-adaptive AI systems.
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