CoPL: Collaborative Preference Learning for Personalizing LLMs

• URL: http://arxiv.org/abs/2503.01658v1
• Date: Mon, 03 Mar 2025 15:32:02 GMT
• Title: CoPL: Collaborative Preference Learning for Personalizing LLMs
• Authors: Youngbin Choi, Seunghyuk Cho, Minjong Lee, MoonJeong Park, Yesong Ko, Jungseul Ok, Dongwoo Kim,
• Abstract summary: We propose a graph-based collaborative filtering framework that models user-response relationships to enhance preference estimation.<n>CoPL efficiently fine-tunes large language models (LLMs) while dynamically balancing shared and user-specific preferences.<n> Experiments on UltraFeedback-P demonstrate that CoPL outperforms existing personalized reward models, effectively capturing both common and controversial preferences.
• Score: 8.158048301024149
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Personalizing large language models (LLMs) is important for aligning outputs with diverse user preferences, yet existing methods struggle with flexibility and generalization. We propose CoPL (Collaborative Preference Learning), a graph-based collaborative filtering framework that models user-response relationships to enhance preference estimation, particularly in sparse annotation settings. By integrating a mixture of LoRA experts, CoPL efficiently fine-tunes LLMs while dynamically balancing shared and user-specific preferences. Additionally, an optimization-free adaptation strategy enables generalization to unseen users without fine-tuning. Experiments on UltraFeedback-P demonstrate that CoPL outperforms existing personalized reward models, effectively capturing both common and controversial preferences, making it a scalable solution for personalized LLM alignment.

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