User preferences are increasingly used to personalize Large Language Model (LLM) responses, yet how to reliably leverage preference signals for answer generation remains under-explored. In practice, preferences can be noisy, incomplete, or even misleading, which can degrade answer quality when applied naively. Motivated by the observation that stable personality traits shape everyday preferences, we study personality as a principled ''latent'' signal behind preference statements. Through extensive experiments, we find that conditioning on personality-aligned preferences substantially improves personalized question answering: selecting preferences consistent with a user's inferred personality increases answer-choice accuracy from 29.25% to 76%, compared to using randomly selected preferences. Based on these findings, we introduce PACIFIC (Preference Alignment Choices Inference for Five-factor Identity Characterization), a personality-labeled preference dataset containing 1200 preference statements spanning diverse domains (e.g., travel, movies, education), annotated with Big-Five (OCEAN) trait directions. Finally, we propose a framework that enables an LLM model to automatically retrieve personality-aligned preferences and incorporate them during answer generation.

翻译：用户偏好日益被用于个性化大型语言模型（LLM）的响应，然而如何可靠地利用偏好信号进行答案生成仍待深入探索。实践中，偏好可能包含噪声、不完整甚至具有误导性，若直接应用可能降低答案质量。受稳定人格特质塑造日常偏好这一观察的启发，我们将人格视为偏好陈述背后一种原则性的“潜在”信号。通过大量实验发现，基于人格对齐的偏好进行条件化能显著提升个性化问答性能：与使用随机选择的偏好相比，选择与用户推断人格一致的偏好可将答案选择准确率从29.25%提升至76%。基于这些发现，我们提出了PACIFIC（基于五因素身份表征的偏好对齐选择推断）——一个包含1200条跨领域（如旅行、电影、教育）偏好陈述的人格标注数据集，每条陈述均标注了大五人格（OCEAN）特质倾向。最后，我们提出一个框架，使LLM能够自动检索人格对齐的偏好，并在答案生成过程中将其纳入考量。