UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities
Researchers have developed UniversalRAG, a novel framework for Retrieval-Augmented Generation that can process and integrate information from diverse data types and granularities. Unlike previous RAG systems limited to text or single modalities, UniversalRAG employs modality-aware routing to select the most appropriate corpus for retrieval and organizes data into multiple granularity levels. This approach aims to overcome the modality gap and improve retrieval accuracy for complex, multi-faceted queries. AI
IMPACT This framework could enhance LLM capabilities by enabling more comprehensive and accurate information retrieval across diverse data sources.