PersonaRAG: Enhancing Retrieval-Augmented Generation Systems with User-Centric Agents

Understand

Large Language Models (LLMs) struggle with generating reliable outputs due to outdated knowledge and hallucinations.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.