Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning
This addresses personalization and privacy issues in LLMs for users, though it is incremental as it builds on parameter-efficient fine-tuning methods.
The paper tackles the problem of personalizing large language models (LLMs) by introducing One PEFT Per User (OPPU), which uses personalized parameter-efficient fine-tuning modules to store user-specific behavior patterns, significantly outperforming existing prompt-based methods across seven tasks in the LaMP benchmark.
Personalization in large language models (LLMs) is increasingly important, aiming to align the LLMs' interactions, content, and recommendations with individual user preferences. Recent advances have highlighted effective prompt design by enriching user queries with non-parametric knowledge through behavior history retrieval and textual profiles. However, these methods faced limitations due to a lack of model ownership, resulting in constrained customization and privacy issues, and often failed to capture complex, dynamic user behavior patterns. To address these shortcomings, we introduce One PEFT Per User (OPPU), employing personalized parameter-efficient fine-tuning (PEFT) modules to store user-specific behavior patterns and preferences. By plugging in personal PEFT parameters, users can own and use their LLMs individually. OPPU integrates parametric user knowledge in the personal PEFT parameters with non-parametric knowledge from retrieval and profiles, adapting LLMs to user behavior shifts. Experimental results demonstrate that OPPU significantly outperforms existing prompt-based methods across seven diverse tasks in the LaMP benchmark. Further studies reveal OPPU's enhanced capabilities in handling user behavior shifts, modeling users at different activity levels, maintaining robustness across various user history formats, and displaying versatility with different PEFT methods.