當個人化誤導：理解與減輕個人化大型語言模型中的幻覺現象

摘要

個人化大型語言模型（LLMs）通過適應用戶個性特徵來提升使用者滿意度，然而這種個人化機制可能無意間扭曲事實推理。我們發現，當個人化LLMs處理事實性查詢時，會出現模型生成答案與用戶歷史偏好而非客觀事實相符的現象，導致「個人化誘導幻覺」——這種因個人化表徵與事實表徵的糾纏所產生的效應，不僅削弱模型的事實可靠性，更可能傳播錯誤認知。為解決此問題，我們提出「事實性保持的個人化導向」（FPPS），一種輕量級的推理時介入方法，能在維持個人化行為的同時有效抑制個人化引發的事實扭曲。我們進一步建立PFQABench，首個專為評估個人化情境下事實問答與個性化問答綜合表現的基準測試。跨多種LLM基礎模型與個人化方法的實驗表明，FPPS在保持個人化性能的同時，能顯著提升事實準確性。

English

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.

當個人化誤導：理解與減輕個人化大型語言模型中的幻覺現象

When Personalization Misleads: Understanding and Mitigating Hallucinations in Personalized LLMs

摘要

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