Persona-DB：基于协同数据精炼的响应预测高效大语言模型个性化
Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement
AI总结：
针对LLM个性化中数据库表示优化不足的问题，提出Persona-DB框架，通过分层构建与协同精炼提升检索效率，在缩减检索规模下保持准确率，并在冷启动场景下实现超10%的性能提升。
摘要：
对大语言模型（LLMs）个性化交互日益增长的需求，呼唤能够准确且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，它能够容纳海量用户而无需承担微调带来的成本。然而，现有研究主要集中于增强检索阶段，对优化数据库表示的探索十分有限，而数据库表示对于个性化等任务是一个关键方面。在本研究中，我们从新角度审视该问题，重点关注在LLM定制化背景下，如何更好地表示数据以实现更具数据效率的检索。为应对这一挑战，我们引入了Persona-DB，这是一个简单而有效的框架，包含用于改善跨任务上下文泛化能力的分层构建过程，以及用于有效弥合用户间知识鸿沟的协同精炼机制。在响应预测的评估中，Persona-DB展现出卓越的上下文效率，在检索规模大幅缩减的情况下仍能保持准确率，这在历史记录庞大或上下文窗口有限的场景中是一项关键优势。我们的实验还表明，在用户数据极度稀疏的冷启动场景下，性能有超过10%的显著提升。此外，我们的分析揭示了随着检索容量的扩大，协同知识的重要性日益增加。
英文摘要：
The increasing demand for personalized interactions with large language models (LLMs) calls for 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 data-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 evaluation of response prediction, Persona-DB demonstrates superior context 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 10% 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.
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。
