PROPER: A Progressive Learning Framework for Personalized Large Language Models with Group-Level Adaptation

• URL: http://arxiv.org/abs/2503.01303v1
• Date: Mon, 03 Mar 2025 08:40:50 GMT
• Title: PROPER: A Progressive Learning Framework for Personalized Large Language Models with Group-Level Adaptation
• Authors: Linhai Zhang, Jialong Wu, Deyu Zhou, Yulan He,
• Abstract summary: We propose PROgressive PERsonalization (PROPER), a novel learning framework inspired by meso-level theory in social science.<n>ProPER bridges population-level and user-level models by grouping users based on preferences and adapting LLMs in stages.<n> Experimental results show that PROPER significantly outperforms SOTA models across multiple tasks.
• Score: 32.53309583561644
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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