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[2602.21553] Revisiting RAG Retrievers: An Information Theoretic Benchmark
Summary
This paper presents MIGRASCOPE, a new framework for evaluating RAG retrievers, emphasizing the need for systematic benchmarks and metrics to enhance retrieval quality in AI systems.
Why It Matters
As RAG systems become increasingly critical in AI, understanding the nuances of retriever performance is essential. This research provides a structured approach to evaluate and improve retriever selection, which can lead to more effective AI applications.
Key Takeaways
- Introduces MIGRASCOPE for evaluating RAG retrievers based on information theory.
- Highlights the limitations of current benchmarks in assessing retriever performance.
- Demonstrates that ensembles of retrievers can outperform individual models.
- Provides actionable insights for designing robust RAG systems.
- Encourages a deeper understanding of retrieval mechanisms in AI.
Computer Science > Information Retrieval arXiv:2602.21553 (cs) [Submitted on 25 Feb 2026] Title:Revisiting RAG Retrievers: An Information Theoretic Benchmark Authors:Wenqing Zheng, Dmitri Kalaev, Noah Fatsi, Daniel Barcklow, Owen Reinert, Igor Melnyk, Senthil Kumar, C. Bayan Bruss View a PDF of the paper titled Revisiting RAG Retrievers: An Information Theoretic Benchmark, by Wenqing Zheng and 7 other authors View PDF HTML (experimental) Abstract:Retrieval-Augmented Generation (RAG) systems rely critically on the retriever module to surface relevant context for large language models. Although numerous retrievers have recently been proposed, each built on different ranking principles such as lexical matching, dense embeddings, or graph citations, there remains a lack of systematic understanding of how these mechanisms differ and overlap. Existing benchmarks primarily compare entire RAG pipelines or introduce new datasets, providing little guidance on selecting or combining retrievers themselves. Those that do compare retrievers directly use a limited set of evaluation tools which fail to capture complementary and overlapping strengths. This work presents MIGRASCOPE, a Mutual Information based RAG Retriever Analysis Scope. We revisit state-of-the-art retrievers and introduce principled metrics grounded in information and statistical estimation theory to quantify retrieval quality, redundancy, synergy, and marginal contribution. We further show that if chosen carefully, an en...