Instant Personalized Large Language Model Adaptation via Hypernetwork

• URL: http://arxiv.org/abs/2510.16282v1
• Date: Sat, 18 Oct 2025 00:41:25 GMT
• Title: Instant Personalized Large Language Model Adaptation via Hypernetwork
• Authors: Zhaoxuan Tan, Zixuan Zhang, Haoyang Wen, Zheng Li, Rongzhi Zhang, Pei Chen, Fengran Mo, Zheyuan Liu, Qingkai Zeng, Qingyu Yin, Meng Jiang,
• Abstract summary: Profile-to-PEFT is 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.<n>We show that our method outperforms both prompt-based personalization and OPPU while using substantially fewer computational resources at deployment.
• Score: 56.512539596908745
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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