Instant Personalized Large Language Model Adaptation via Hypernetwork

TL;DR

This work tackles the challenge of instant, scalable personalization for large language models by replacing per-user fine-tuning with Profile-to-PEFT (P2P), a hypernetwork that maps encoded user profiles to a full set of LoRA adapters in a single forward pass. Trained end-to-end on a diverse user population, P2P generalizes to unseen users and supports deployment without sharing raw user history, enabling privacy-preserving, real-time customization. Empirical results on LaMP, LongLaMP, Personal Reddit, and Empathetic Conversation show P2P outperforms prompt-based and One-PEFT-Per-User baselines, with strong Open-Ended generation performance and markedly faster deployment (33x faster than fastest OPPU). The approach demonstrates robustness to embedding choices and user activity levels, and highlights the practical viability of instant LLM personalization at industrial scale while acknowledging ethical considerations around bias, privacy, and accessibility.

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.

Figures (6)

• Figure 1: The "One-PEFT-Per-User" method uses computationally intensive fine-tuning to create personalized parameters. In contrast, our proposed Profile-to-PEFT uses a hypernetwork to directly generate parameters from user history or profile in a single inference pass.
• Figure 2: Overview of the Profile-to-PEFT architecture, where user history, depth, module embeddings are fed into the hypernetwork to obtain personalized LoRA. P2P is optimized in a end-to-end training manner.
• Figure 3: Performance of P2P as a function of training user diversity (top row) and quantity (bottom row). While greater diversity boosts performance, increasing the number of users yields no significant gains.
• Figure 4: Cumulative time for personalized parameter generation vs. user count. Our Profile-to-PEFT exhibits near-constant, minimal cost, showing superior scalability over OPPU variants' linear growth.
• Figure 5: Robustness of P2P to varying user activity levels. The model maintains strong performance across different history lengths, outperforming baselines for both sparse and dense user data.