Crafting Personalized Agents through Retrieval-Augmented Generation on Editable Memory Graphs
<|reference_start|>Crafting Personalized Agents through Retrieval-Augmented Generation on Editable Memory Graphs: In the age of mobile internet, user data, often referred to as memories, is continuously generated on personal devices. Effectively managing and utilizing this data to deliver services to users is a compelling research topic. In this paper, we introduce a novel task of crafting personalized agents powered by large language models (LLMs), which utilize a user's smartphone memories to enhance downstream applications with advanced LLM capabilities. To achieve this goal, we introduce EMG-RAG, a solution that combines Retrieval-Augmented Generation (RAG) techniques with an Editable Memory Graph (EMG). This approach is further optimized using Reinforcement Learning to address three distinct challenges: data collection, editability, and selectability. Extensive experiments on a real-world dataset validate the effectiveness of EMG-RAG, achieving an improvement of approximately 10% over the best existing approach. Additionally, the personalized agents have been transferred into a real smartphone AI assistant, which leads to enhanced usability.<|reference_end|>
@article{wang2024crafting,
    title={Crafting Personalized Agents through Retrieval-Augmented Generation on
  Editable Memory Graphs},
    author={Zheng Wang, Zhongyang Li, Zeren Jiang, Dandan Tu, Wei Shi},
    journal={arXiv preprint arXiv:2409.19401},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19401},
    primaryClass={cs.CL cs.IR}
}