UniversalRAG: Retrieval-Augmented Generation over Multiple Corpora with Diverse Modalities and Granularities
Woongyeong Yeo, Kangsan Kim, Soyeong Jeong, Jinheon Baek, Sung Ju Hwang
2025-04-30
Summary
This paper talks about a new AI system called UniversalRAG that helps chatbots answer questions more accurately by checking multiple types of information sources like text, images, and videos.
What's the problem?
Current AI systems often use only text to answer questions, but real-world questions might need visuals or videos. Mixing all data types into one system causes confusion, making it harder to find the right info.
What's the solution?
UniversalRAG uses a smart 'router' to pick the best data type (text/image/video) and detail level for each question. It checks specialized databases for each type separately, avoiding mix-ups.
Why it matters?
It matters because it lets AI handle complex, real-life questions that require different media types, making answers more trustworthy and useful for things like homework help or fact-checking.
Abstract
UniversalRAG enhances Retrieval-Augmented Generation by dynamically selecting and retrieving knowledge from various heterogeneous and modality-specific sources to improve factual accuracy across different types of queries.