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Adaptive Re-Ranking cuts IR latency by routing queries efficiently

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Adaptive Re-Ranking, a framework designed to optimize computational costs and latency in information retrieval systems. This method routes queries based on their complexity, employing different re-ranking models—from sparse retrieval (BM25) to heavy neural re-ranking (BGE-v2-m3)—to avoid unnecessary processing on simpler queries. The approach demonstrates significant reductions in median and mean latency, achieving competitive nDCG@10 scores across various datasets. AI

IMPACT Potential to significantly reduce latency and computational costs in search and retrieval systems.

RANK_REASON Academic paper detailing a new method for information retrieval. [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 →

Adaptive Re-Ranking cuts IR latency by routing queries efficiently

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Academic paper detailing a new method for information retrieval. [lever_c_demoted from research: ic=1 ai=1.0]
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98 days old
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · James Allan ·

    Adaptive Re-Ranking

    Modern Information Retrieval (IR) systems typically use a "retrieve-then-rerank" pipeline, where a computationally expensive, pre-determined cross-encoder re-ranks the top results from a fast initial retriever. While effective, this approach often applies heavy re-ranking models …

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