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

TL;DR

PersonaRAG is a new framework that improves retrieval-augmented generation by incorporating user-centric agents, enabling personalized and context-aware responses in real-time, thus addressing limitations of traditional RAG models.

Contribution

Introduces PersonaRAG, a novel approach integrating user-centric agents into RAG systems for personalized, adaptive information retrieval and generation.

Findings

Outperforms baseline models on question answering datasets.

Provides tailored answers based on real-time user data.

Demonstrates potential for user-adapted retrieval systems.

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Large Language Models (LLMs) struggle with generating reliable outputs due to outdated knowledge and hallucinations.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