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.

翻译：由于个体可能难以回忆所有生活细节且经常混淆事件，建立一个协助用户回忆遗忘经历的系统至关重要。尽管已有大量研究提出了记忆召回系统，但这些系统主要依赖于深度学习技术，需要大量训练数据，且常因个人生活日志的有限可用性而面临数据稀缺问题。随着生活日志随时间不断增长，系统还必须快速适应新积累的数据。近年来，大型语言模型（LLMs）在各种任务中展现出卓越能力，使其在个性化应用领域极具前景。本研究提出一个利用LLMs实现前瞻性信息访问的框架，通过整合个人知识图谱并借助精细化的决策过程来增强访问需求的检测能力。该框架具有高度灵活性，允许替换基础模型并修改事实检索方法以实现持续改进。实验结果表明，我们的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。