@inproceedings{qiu-etal-2025-measuring,
    title = "Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing {LLM} Personalization",
    author = "Qiu, Yilun  and
      Zhao, Xiaoyan  and
      Zhang, Yang  and
      Bai, Yimeng  and
      Wang, Wenjie  and
      Cheng, Hong  and
      Feng, Fuli  and
      Chua, Tat-Seng",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/transition-to-people-yaml/2025.findings-acl.1095/",
    doi = "10.18653/v1/2025.findings-acl.1095",
    pages = "21258--21277",
    ISBN = "979-8-89176-256-5",
    abstract = "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 at \url{https://github.com/SnowCharmQ/DPL}."
}