面向主动信息获取的个性化图增强大语言模型
Personalized Graph-Empowered Large Language Model for Proactive Information Access
AI总结：
本文提出一种基于大语言模型和个性化知识图谱的主动信息获取框架，通过精炼决策过程提升用户对遗忘事件的识别能力。
摘要：
由于个体可能难以回忆所有生活细节且常混淆事件，建立一个帮助用户回忆遗忘经历的系统是必要的。尽管已有大量研究提出了记忆回忆系统，但这些系统主要依赖深度学习技术，需要大量训练且常因个人生活日志的有限可用性而面临数据稀缺问题。随着生活日志的增长，系统也必须快速适应新积累的数据。最近，大语言模型（LLMs）在各种任务中展示了显著的能力，使其在个性化应用中具有潜力。在本工作中，我们提出了一种框架，利用LLMs进行主动信息获取，通过精炼的决策过程整合个人知识图谱以增强对获取需求的检测。我们的框架具有高度灵活性，能够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，我们的方法有效识别了遗忘事件，支持用户更高效地回忆过去经历。
英文摘要：
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.
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。
