Abstract
Personalization in LLMs often relies on costly human feedback or interaction logs, limiting scalability and neglecting deeper user attributes. To reduce the reliance on human annotations, we introduce GRAVITY (Generative Response with Aligned Values, Interests, and Traits of You), a framework for generating synthetic, profile-grounded preference data that captures users' interests, values, beliefs, and personality traits. By integrating demographic, cultural, and psychological frameworks -- including Hofstede's cultural dimensions, Schwartz's basic values, the World Values Survey, and Big Five OCEAN traits -- GRAVITY synthesizes preference pairs to guide personalized content generation. We evaluate GRAVITY on book descriptions for 400 Amazon users, comparing it to prompt-based conditioning, standard fine-tuning, and naive synthetic pair generation. Profile-grounded synthetic data consistently improves generation, especially across multiple cultures (USA, Brazil, Japan, India), achieving over 4% higher preference gains across baselines, with user studies showing that GRAVITY outputs are preferred over 86% of the time. Our results show that scenario-grounded synthetic data can capture richer user variation, reduce reliance on costly annotation, and produce more engaging, user-centered content, offering a scalable path for LLM personalization.
Abstract (translated)
在大型语言模型（LLMs）中，个性化通常依赖于昂贵的人类反馈或交互日志，这限制了其可扩展性，并忽视了更深层次的用户属性。为了减少对人工注释的依赖，我们引入了一种名为GRAVITY（与您的价值观、兴趣和特质相匹配的生成式响应）的框架，用于生成基于个人资料的合成偏好数据，该数据能够捕捉用户的兴趣、价值观念、信仰和个人特质。通过整合人口统计学、文化和心理框架——包括霍夫斯泰德的文化维度理论、施瓦茨的基本价值观、世界价值观调查以及五大人格特质（开放性、尽责性、外向性、宜人性和神经质）——GRAVITY综合生成偏好对，以指导个性化内容的创作。我们在400名亚马逊用户中的书籍描述上评估了GRAVITY，并将其与基于提示的方法调优、标准微调以及简单的合成对生成方法进行了比较。结果表明，基于个人资料的合成数据在提升生成效果方面表现突出，尤其是在多文化背景（美国、巴西、日本和印度）下，相对于基准线，偏好增益提高了超过4%；用户研究显示，在86%的时间内，GRAVITY生成的内容更受欢迎。我们的结果显示，场景相关的合成数据能够捕捉到更为丰富的用户差异性，减少对昂贵注释的依赖，并且能产出更多引人入胜、以用户为中心的内容，为大型语言模型的个性化提供了一条可扩展的道路。
URL
https://arxiv.org/abs/2510.11952
