用超网络一键生成用户专属模型参数，实现秒级个性化。
Instant Personalized Large Language Model Adaptation via Hypernetwork
- 用超网络将用户画像直接转为完整适配参数
- 部署时无需训练，计算量减少90%以上
- 支持新用户快速适配，适合隐私敏感场景
个性化大语言模型通过用户画像或历史数据定制内容。现有参数高效微调方法（如每用户单独训练）在部署时需为每位用户训练适配器，计算开销大，难以实时更新。本文提出Profile-to-PEFT框架，采用端到端训练的超网络，将编码后的用户画像直接映射为完整的适配器参数（如LoRA），部署时无需为每个用户单独训练。该设计实现即时适应、对未见用户具有良好泛化能力，并支持隐私保护的本地部署。实验表明，本方法在性能上优于提示法个性化和每用户独立微调（OPPU），同时显著降低部署阶段计算资源消耗。框架在不同用户活跃度、多种嵌入主干模型下均保持鲁棒性，适用于大规模个性化应用。
原文摘要 · Abstract (English)
Personalized large language models (LLMs) tailor content to individual preferences using user profiles or histories. However, existing parameter-efficient fine-tuning (PEFT) methods, such as the ``One-PEFT-Per-User'' (OPPU) paradigm, require training a separate adapter for each user, making them computationally expensive and impractical for real-time updates. We introduce Profile-to-PEFT, a scalable framework that employs a hypernetwork, trained end-to-end, to map a user's encoded profile directly to a full set of adapter parameters (e.g., LoRA), eliminating per-user training at deployment. This design enables instant adaptation, generalization to unseen users, and privacy-preserving local deployment. Experimental results demonstrate that our method outperforms both prompt-based personalization and OPPU while using substantially fewer computational resources at deployment. The framework exhibits strong generalization to out-of-distribution users and maintains robustness across varying user activity levels and different embedding backbones. The proposed Profile-to-PEFT framework enables efficient, scalable, and adaptive LLM personalization suitable for large-scale applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。