Large Language Models (LLMs) struggle with generating reliable outputs due to outdated knowledge and hallucinations. Retrieval-Augmented Generation (RAG) models address this by enhancing LLMs with external knowledge, but often fail to personalize the retrieval process. This paper introduces PersonaRAG, a novel framework incorporating user-centric agents to adapt retrieval and generation based on real-time user data and interactions. Evaluated across various question answering datasets, PersonaRAG demonstrates superiority over baseline models, providing tailored answers to user needs. The results suggest promising directions for user-adapted information retrieval systems.

翻译：大型语言模型（LLM）因知识过时和产生幻觉而难以生成可靠的输出。检索增强生成（RAG）模型通过引入外部知识来增强LLM，但通常无法实现检索过程的个性化。本文提出PersonaRAG，这是一种新颖的框架，通过引入以用户为中心的智能体，能够基于实时用户数据和交互来调整检索与生成过程。在多种问答数据集上的评估表明，PersonaRAG优于基线模型，能够根据用户需求提供定制化答案。该结果为用户自适应的信息检索系统指出了具有前景的发展方向。