“PersonaRAG: Enhancing RAG Systems with User-Centric Agents”
In the rapidly evolving field of natural language processing (NLP), integrating external knowledge bases through Retrieval-Augmented Generation (RAG) systems has made significant progress. However, traditional RAG systems often fail to incorporate user context or personalized information retrieval strategies, resulting in a gap between general effectiveness and customized user experiences. To address this issue, researchers at … Read more