Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning

Editorial record

Plain-language summary

One PEFT Per User (OPPU) gives each user their own parameter-efficient fine-tuning module that stores user-specific behavior patterns and preferences parametrically, enabling model ownership and enhanced customization. By fine-tuning personal PEFT parameters on user history and integrating them with retrieval and profile augmentation, OPPU significantly outperforms existing prompt-based methods across seven LaMP benchmark tasks and demonstrates superior adaptation to user behavior shifts where history is less relevant.