PersonaRAG: Enhancing Retrieval-Augmented Generation Systems with User-Centric Agents
<|reference_start|>PersonaRAG: Enhancing Retrieval-Augmented Generation Systems with User-Centric Agents: Large Language Models (LLMs) struggle with generating reliable outputs due to outdated knowledge and hallucinations.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.<|reference_end|>
@article{zerhoudi2024personarag:,
    title={PersonaRAG: Enhancing Retrieval-Augmented Generation Systems with
  User-Centric Agents},
    author={Saber Zerhoudi, Michael Granitzer},
    journal={arXiv preprint arXiv:2407.09394},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.09394},
    primaryClass={cs.IR}
}