TL;DR
This paper introduces RAGRouter-Bench, a comprehensive dataset and benchmark for evaluating adaptive retrieval-augmented generation routing, emphasizing context-aware paradigm selection to optimize effectiveness and efficiency.
Contribution
It presents the first systematic benchmark for adaptive RAG routing, integrating diverse query types, corpus indicators, and evaluation protocols for comprehensive analysis.
Findings
No single RAG paradigm is optimal for all query-corpus pairs.
Adaptive routing improves effectiveness-efficiency trade-offs over fixed paradigms.
Query-corpus compatibility is crucial for effective RAG system design.
Abstract
Retrieval-augmented generation (RAG) has evolved into a family of paradigms with distinct performance profiles and resource demands, turning paradigm selection into a multi-criteria, context-dependent decision problem. Nevertheless, existing studies largely focus on isolated method improvements or query-only benchmarking, without systematically examining how RAG paradigms behave across diverse query-corpus contexts and effectiveness-efficiency trade-offs. In this work, we introduce RAGRouter-Bench, the first dataset and benchmark for adaptive RAG routing. Grounded in query-corpus compatibility, the benchmark integrates three canonical query types, fine-grained corpus indicators capturing structural and semantic properties, and a unified protocol for evaluating both generation quality and resource consumption. Then, we implement standardized RAG paradigms with multiple backbone LLMs…
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