PROPER: A Progressive Learning Framework for Personalized Large Language Models with Group-Level Adaptation
This addresses the problem of personalizing AI outputs for users with limited data, though it is incremental as it builds on existing parameter-efficient fine-tuning methods.
The paper tackles the challenge of personalizing large language models with sparse user data by proposing PROPER, a progressive learning framework that groups users and adapts models in stages, which significantly outperforms state-of-the-art models across multiple tasks.
Personalized large language models (LLMs) aim to tailor their outputs to user preferences. Recent advances in parameter-efficient fine-tuning (PEFT) methods have highlighted the effectiveness of adapting population-level LLMs to personalized LLMs by fine-tuning user-specific parameters with user history. However, user data is typically sparse, making it challenging to adapt LLMs to specific user patterns. To address this challenge, we propose PROgressive PERsonalization (PROPER), a novel progressive learning framework inspired by meso-level theory in social science. PROPER bridges population-level and user-level models by grouping users based on preferences and adapting LLMs in stages. It combines a Mixture-of-Experts (MoE) structure with Low Ranked Adaptation (LoRA), using a user-aware router to assign users to appropriate groups automatically. Additionally, a LoRA-aware router is proposed to facilitate the integration of individual user LoRAs with group-level LoRAs. Experimental results show that PROPER significantly outperforms SOTA models across multiple tasks, demonstrating the effectiveness of our approach.