Personalizing Large Language Models (LLMs) has become a critical step in facilitating their widespread application to enhance individual life experiences. In pursuit of personalization, distilling key preference information from an individual's historical data as instructional preference context to customize LLM generation has emerged as a promising direction. However, these methods face a fundamental limitation by overlooking the inter-user comparative analysis, which is essential for identifying the inter-user differences that truly shape preferences. To address this limitation, we propose Difference-aware Personalization Learning (DPL), a novel approach that emphasizes extracting inter-user differences to enhance LLM personalization. DPL strategically selects representative users for comparison and establishes a structured standard to extract meaningful, task-relevant differences for customizing LLM generation. Extensive experiments on real-world datasets demonstrate that DPL significantly enhances LLM personalization. We release our code atthis https URL.
View on arXiv@article{qiu2025_2503.02450,
title={ Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization },
author={ Yilun Qiu and Xiaoyan Zhao and Yang Zhang and Yimeng Bai and Wenjie Wang and Hong Cheng and Fuli Feng and Tat-Seng Chua },
journal={arXiv preprint arXiv:2503.02450},
year={ 2025 }
}