@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T When Personalization Misleads: Understanding and Mitigating Hallucinations in Personalized LLMs
%A Sun, Zhongxiang
%A Zhan, Yi
%A Shen, Chenglei
%A Yu, Weijie
%A Zhang, Xiao
%A He, Ming
%A Xu, Jun
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F sun-etal-2026-personalization
%X 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.
%U https://aclanthology.org/2026.findings-acl.395/
%P 8041-8060
Markdown (Informal)
[When Personalization Misleads: Understanding and Mitigating Hallucinations in Personalized LLMs](https://aclanthology.org/2026.findings-acl.395/) (Sun et al., Findings 2026)
ACL
- Zhongxiang Sun, Yi Zhan, Chenglei Shen, Weijie Yu, Xiao Zhang, Ming He, and Jun Xu. 2026. When Personalization Misleads: Understanding and Mitigating Hallucinations in Personalized LLMs. In Findings of the Association for Computational Linguistics: ACL 2026, pages 8041–8060, San Diego, California, United States. Association for Computational Linguistics.