The increasing demand for personalized interactions with large language models (LLMs) calls for the development of methodologies capable of accurately and efficiently identifying user opinions and preferences. Retrieval augmentation emerges as an effective strategy, as it can accommodate a vast number of users without the costs from fine-tuning. Existing research, however, has largely focused on enhancing the retrieval stage and devoted limited exploration toward optimizing the representation of the database, a crucial aspect for tasks such as personalization. In this work, we examine the problem from a novel angle, focusing on how data can be better represented for more efficient retrieval in the context of LLM customization. To tackle this challenge, we introduce Persona-DB, a simple yet effective framework consisting of a hierarchical construction process to improve generalization across task contexts and collaborative refinement to effectively bridge knowledge gaps among users. In the task of response forecasting, Persona-DB demonstrates superior efficiency in maintaining accuracy with a significantly reduced retrieval size, a critical advantage in scenarios with extensive histories or limited context windows. Our experiments also indicate a marked improvement of over 15% under cold-start scenarios, when users have extremely sparse data. Furthermore, our analysis reveals the increasing importance of collaborative knowledge as the retrieval capacity expands.

翻译：随着对与大语言模型进行个性化交互的需求日益增长，亟需开发能够准确高效识别用户观点与偏好的方法论。检索增强作为一种有效策略应运而生，可在无需微调成本的前提下容纳海量用户。然而现有研究主要聚焦于优化检索阶段，对数据库表征这一个性化任务关键环节的探索十分有限。本研究从全新视角审视该问题，聚焦于在大语言模型定制场景中，如何通过更优的数据表征实现更高效的检索。为此我们提出Persona-DB框架，该框架简洁高效，包含分层构建流程以提升跨任务场景的泛化能力，以及协作精炼机制以有效弥合用户间的知识鸿沟。在响应预测任务中，Persona-DB在显著缩减检索规模的同时保持准确率，这一优势在存在大量历史记录或有限上下文窗口的场景中尤为关键。实验表明，在用户数据极度稀疏的冷启动场景下，该框架实现了超过15%的性能提升。此外，分析结果揭示协作知识在检索容量扩展时的重要性日益凸显。