When Personalization Misleads: Understanding and Mitigating Hallucinations in Personalized LLMs
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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Zhongxiang Sun
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2026-01-19T09:22:05.638Z
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SunZX
Jeryi
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Jeryi
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Yi Zhan
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Chenglei Shen
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2026-01-19T14:39:47.954Z
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chenglei shen
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Weijie Yu
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2026-01-19T14:39:42.356Z
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Weijie Yu
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696dc5773f1837bfb8970a3f
Xiao Zhang
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Ming He
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Jun Xu
When Personalization Misleads: Understanding and Mitigating Hallucinations in Personalized LLMs
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.
Personalized large language models can generate false information aligned with user history instead of factual truth, but a new method called FPPS helps maintain both factual accuracy and personalized responses while preserving existing personalization effects.
personalized large language models factual reasoning personalization-induced hallucinations representational entanglement Factuality-Preserving Personalized Steering PFQABench inference-time approach factual accuracy personalized performance