@inproceedings{qiu-etal-2025-latent,
    title = "Latent Inter-User Difference Modeling for {LLM} Personalization",
    author = "Qiu, Yilun  and
      Shi, Tianhao  and
      Zhao, Xiaoyan  and
      Zhu, Fengbin  and
      Zhang, Yang  and
      Feng, Fuli",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.536/",
    pages = "10610--10628",
    ISBN = "979-8-89176-332-6",
    abstract = "Large language models (LLMs) are increasingly integrated into users' daily lives, leading to a growing demand for personalized outputs.Previous work focuses on leveraging a user{'}s own history, overlooking inter-user differences that are crucial for effective personalization.While recent work has attempted to model such differences, the reliance on language-based prompts often hampers the effective extraction of meaningful distinctions.To address these issues, we propose Difference-aware Embedding-based Personalization (DEP), a framework that models inter-user differences in the latent space instead of relying on language prompts. DEP constructs soft prompts by contrasting a user{'}s embedding with those of peers who engaged with similar content, highlighting relative behavioral signals.A sparse autoencoder then filters and compresses both user-specific and difference-aware embeddings, preserving only task-relevant features before injecting them into a frozen LLM.Experiments on personalized review generation show that DEP consistently outperforms baseline methods across multiple metrics.Our code is available at https://github.com/SnowCharmQ/DEP."
}