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RAG systems adapt retrieval depth via query clustering for improved accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel framework for adaptive retrieval depth in retrieval-augmented generation (RAG) systems. This approach addresses the limitations of fixed top-k document retrieval by dynamically adjusting the number of documents based on query complexity. By clustering queries offline and assigning a recommended retrieval depth to each cluster, the system can improve accuracy and reduce computational costs at runtime. Initial testing showed a 36% increase in F1 score and a 14% reduction in token usage for low-complexity queries without sacrificing accuracy. AI

IMPACT Improves RAG system efficiency and accuracy by dynamically adjusting document retrieval based on query complexity.

RANK_REASON Academic paper detailing a new method for improving RAG systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

RAG systems adapt retrieval depth via query clustering for improved accuracy

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Academic paper detailing a new method for improving RAG systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Haixun Wang ·

    Pre-retrieval Query Clustering for Adaptive Top-k Document Retrieval in RAG Systems

    RAG systems commonly retrieve a fixed number of documents (top-k) to ground generation, but this static approach is brittle: simple queries suffer over-retrieval (adding noise and cost) while complex queries are under-retrieved, causing recall failures that cascade into incorrect…

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