Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement

TL;DR

Persona-DB introduces a novel framework for efficient LLM personalization by optimizing data representation and collaborative refinement, significantly improving response accuracy with less data and in cold-start scenarios.

Contribution

It proposes a hierarchical and collaborative data refinement approach to enhance retrieval efficiency and personalization in large language models.

Findings

Superior context efficiency with reduced retrieval size

Over 10% improvement in cold-start scenarios

Enhanced importance of collaborative knowledge with larger retrieval capacity

Abstract

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.