Personalized Graph-Empowered Large Language Model for Proactive Information Access

TL;DR

This paper introduces a personalized, graph-empowered large language model framework that proactively assists users in recalling forgotten personal experiences by integrating knowledge graphs with LLMs.

Contribution

It presents a novel framework combining LLMs and personal knowledge graphs for proactive memory recall, adaptable for continuous learning and improvement.

Findings

Effectively identifies forgotten events

Supports efficient personal memory recall

Flexible framework for ongoing enhancement

Abstract

Since individuals may struggle to recall all life details and often confuse events, establishing a system to assist users in recalling forgotten experiences is essential. While numerous studies have proposed memory recall systems, these primarily rely on deep learning techniques that require extensive training and often face data scarcity due to the limited availability of personal lifelogs. As lifelogs grow over time, systems must also adapt quickly to newly accumulated data. Recently, large language models (LLMs) have demonstrated remarkable capabilities across various tasks, making them promising for personalized applications. In this work, we present a framework that leverages LLMs for proactive information access, integrating personal knowledge graphs to enhance the detection of access needs through a refined decision-making process. Our framework offers high flexibility, enabling the replacement of base models and the modification of fact retrieval methods for continuous improvement. Experimental results demonstrate that our approach effectively identifies forgotten events, supporting users in recalling past experiences more efficiently.