用离散代码本构建用户表征，实现高效个性化生成
CURP: Codebook-based Continuous User Representation for Personalized Generation with LLMs
- 通过双向编码器与代码本提取多维用户特征
- 仅需约2000万参数（总模型的0.2%）即达成优秀效果
- 适合需要快速部署、可解释性强的个性化应用
用户建模通过偏好和行为模式刻画个体，以实现大语言模型（LLMs）下的个性化生成。然而，现有方法在个性化质量与计算效率、数据效率之间难以平衡。本文提出新型框架CURP，采用双向用户编码器与离散原型代码本，提取多维度用户特征。该设计支持即插即用式个性化，仅需约2000万参数（约占总模型大小的0.2%）。在多种生成任务上的大量实验表明，CURP在性能和泛化能力上优于强基线，同时具备更好的可解释性与可扩展性。代码已公开于https://github.com/RaidonWong/CURP_code。
原文摘要 · Abstract (English)
User modeling characterizes individuals through their preferences and behavioral patterns to enable personalized simulation and generation with Large Language Models (LLMs) in contemporary approaches. However, existing methods, whether prompt-based or training-based methods, face challenges in balancing personalization quality against computational and data efficiency. We propose a novel framework CURP, which employs a bidirectional user encoder and a discrete prototype codebook to extract multi-dimensional user traits. This design enables plug-and-play personalization with a small number of trainable parameters (about 20M parameters, about 0.2\% of the total model size). Through extensive experiments on variant generation tasks, we show that CURP achieves superior performance and generalization compared to strong baselines, while offering better interpretability and scalability. The code are available at https://github.com/RaidonWong/CURP_code
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