Researchers have introduced Stellar, a new framework designed to make multimodal document retrieval more scalable for Natural Language Query (NLQ) systems. Current methods often use multiple token-level embeddings, which leads to high memory usage and hinders real-world deployment. Stellar addresses this by storing token-level document embeddings on disk and only loading a subset into memory for interaction. It achieves this through a two-component system: Lexical Representation-based Filtering (LRF) for efficient candidate set reduction and Efficient Disk-backed Late Interaction (DLI) for optimized on-disk storage and dynamic loading of embeddings. Experiments show Stellar significantly reduces memory overhead and query latency without sacrificing retrieval effectiveness. AI
IMPACT This framework could enable more efficient and scalable deployment of RAG systems, improving their performance in real-world applications.
RANK_REASON The cluster contains an academic paper detailing a new technical framework for information retrieval. [lever_c_demoted from research: ic=1 ai=1.0]
Read on arXiv cs.IR (Information Retrieval) →
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