RAGRoute introduces a neural router for federated RAG that dynamically selects relevant sources, reducing communication by up to 80.65% and latency by 52.50% while preserving accuracy on three benchmarks.
A collaborative multi-agent approach to retrieval-augmented generation across diverse data
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ALDEN boosts private data extraction rates from RAG systems by combining active learning for query diversification with dynamic estimation of the underlying knowledge-base topic distribution.
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Efficient Federated Search for Retrieval-Augmented Generation using Lightweight Routing
RAGRoute introduces a neural router for federated RAG that dynamically selects relevant sources, reducing communication by up to 80.65% and latency by 52.50% while preserving accuracy on three benchmarks.
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ALDEN: Boosting Private Data Extraction from Retrieval-Augmented Generation Systems via Active Learning and Distribution Estimation
ALDEN boosts private data extraction rates from RAG systems by combining active learning for query diversification with dynamic estimation of the underlying knowledge-base topic distribution.