arXiv:2510.16282v2 Announce Type: replace 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., Lo
The increasing complexity and user-base of LLMs necessitate more efficient and scalable personalization methods to overcome the computational and practical limitations of existing PEFT approaches.
This development offers a potential solution to the scalability bottleneck in personalized AI, enabling more efficient deployment and adaptation of LLMs for a wider range of users and applications.
The shift from 'One-PEFT-Per-User' to a hypernetwork-based adaptation fundamentally changes how personalized LLMs can be managed, making real-time updates and broad user support feasible.
- · AI platform providers
- · Cloud infrastructure providers
- · LLM developers
- · Individual users of AI
- · Companies relying on inefficient PEFT methods
- · AI personalization solutions with high computational overhead
Increased efficiency and reduced computational cost for personalized LLM deployments.
Faster iteration and more nuanced personalization in AI interactions, leading to higher user engagement.
Acceleration of sophisticated AI agent development that can fluidly adapt to individual user contexts and preferences.
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