Crafting Personalized Agents through Retrieval-Augmented Generation on Editable Memory Graphs

• URL: http://arxiv.org/abs/2409.19401v1
• Date: Sat, 28 Sep 2024 16:22:53 GMT
• Title: Crafting Personalized Agents through Retrieval-Augmented Generation on Editable Memory Graphs
• Authors: Zheng Wang, Zhongyang Li, Zeren Jiang, Dandan Tu, Wei Shi,
• Abstract summary: We introduce a novel task of crafting personalized agents powered by large language models (LLMs) We introduce EMG-RAG, a solution that combines Retrieval-Augmented Generation (RAG) techniques with an Editable Memory Graph (EMG) Experiments on a real-world dataset validate the effectiveness of EMG-RAG, achieving an improvement of approximately 10% over the best existing approach.
• Score: 11.182641942286883
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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