@inproceedings{sun-etal-2026-personalization,
    title = "When Personalization Misleads: Understanding and Mitigating Hallucinations in Personalized {LLM}s",
    author = "Sun, Zhongxiang  and
      Zhan, Yi  and
      Shen, Chenglei  and
      Yu, Weijie  and
      Zhang, Xiao  and
      He, Ming  and
      Xu, Jun",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.395/",
    pages = "8041--8060",
    ISBN = "979-8-89176-395-1",
    abstract = "Personalized large language models (LLMs) adapt model behavior to individual users to enhance user satisfaction, yet personalization can inadvertently distort factual reasoning. We show that when personalized LLMs face factual queries, there exists a phenomenon where the model generates answers aligned with a user{'}s prior history rather than the objective truth, resulting in **personalization-induced hallucinations** that degrade factual reliability and may propagate incorrect beliefs, due to representational entanglement between personalization and factual representations. To address this issue, we propose **Factuality-Preserving Personalized Steering (FPPS)**, a lightweight inference-time approach that mitigates personalization-induced factual distortions while preserving personalized behavior. We further introduce **PFQABench**, the first benchmark designed to jointly evaluate factual and personalized question answering under personalization. Experiments across multiple LLM backbones and personalization methods show that FPPS substantially improves factual accuracy while maintaining personalized performance."
}