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) →
- Adaptive Re-Ranking
- alphaXiv
- arXiv
- BGE-v2-m3
- BM25
- DagsHub
- Emir Korukluoglu
- Hugging Face
- MiniLM-L6-v2
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