用知识图谱增强大模型，主动帮人回忆遗忘的往事
Personalized Graph-Empowered Large Language Model for Proactive Information Access
- 用个人知识图谱优化大模型决策，识别用户需要回忆的信息
- 实验显示能有效发现遗忘事件，提升回忆效率
- 适合需要个性化记忆辅助的用户，如老年人或记忆力衰退者
个人可能难以回忆生活细节，常混淆事件，因此建立辅助回忆的系统至关重要。现有研究多依赖深度学习，但需大量训练数据，且因个人生活记录（lifelogs）稀缺而受限。随着生活记录持续积累，系统需快速适应新数据。近期大语言模型（LLMs）在多项任务中表现优异，为个性化应用带来希望。本文提出一种框架，利用LLM实现主动信息访问，通过个人知识图谱增强对信息需求的检测能力，优化决策流程。该框架具有高度灵活性，支持更换基础模型和调整事实检索方法，以持续改进。实验表明，该方法能有效识别遗忘事件，帮助用户更高效地回忆过往经历。
原文摘要 · Abstract (English)
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.
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