PACIFIC：LLM能否辨别影响您偏好的特质？评估LLM中由性格驱动的偏好对齐
PACIFIC: Can LLMs Discern the Psychometric Traits Influencing Your Preferences? Personality-Driven Preference Alignment in LLMs
AI总结：
研究通过实验发现，基于用户性格特征的偏好对齐可显著提升个性化问答准确性，提出PACIFIC数据集及自动检索框架，用于改进LLM回答生成。
摘要：
用户偏好日益用于个性化大型语言模型（LLM）响应，但如何可靠地利用偏好信号进行回答生成仍不明确。鉴于稳定的人格特质塑造日常偏好，本文研究将人格作为潜在信号来解释偏好陈述。通过大量实验发现，基于人格一致的偏好可使个性化问答准确率从29.25%提升至76%。基于此，我们引入包含1200个涵盖不同领域的偏好陈述的PACIFIC数据集，标注有大五人格（OCEAN）特质方向。最后，我们提出一个框架，使LLM模型能够自动检索人格一致的偏好并在回答生成中加以利用。
英文摘要：
User preferences are increasingly used to personalize Large Language Model (LLM) responses, yet reliably leveraging preference signals 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 introduce PACIFIC (Preference Alignment for Choices Inference via Five-factor Identity Characterization), a personality-driven preference alignment framework that uses Big-Five (OCEAN) traits as a principled "latent" signal for organizing and reasoning over user preference history. To systematically evaluate this framework, we construct a psychometrics-based dataset containing 1,200 preference-query pairs spanning diverse domains (e.g., travel, movies, and education), with comprehensive coverage of high and low Big-Five trait directions. Extensive experiments show that trait-aligned preferences substantially improve personalized QA: given clean, trait-aligned context, LLMs reach near-ceiling accuracy (up to 99%), confirming that reasoning capability is not the bottleneck. The challenge is that real histories are mixed-trait and unlabeled. We show the true bottleneck is retrieval: a persona-aware contrastive retriever (PiRAG) raises label-free accuracy from 30% to 43% over standard semantic retrieval, without any trait annotations at inference.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
