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

• URL: http://arxiv.org/abs/2407.09394v1
• Date: Fri, 12 Jul 2024 16:18:00 GMT
• Title: PersonaRAG: Enhancing Retrieval-Augmented Generation Systems with User-Centric Agents
• Authors: Saber Zerhoudi, Michael Granitzer,
• Abstract summary: This paper introduces PersonaRAG, a novel framework incorporating user-centric agents to adapt retrieval and generation based on real-time user data and interactions. The results suggest promising directions for user-adapted information retrieval systems.
• Score: 0.9135658693137204
• License:
• Abstract: Large Language Models (LLMs) struggle with generating reliable outputs due to outdated knowledge and hallucinations.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.

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