Adaptive Re-Ranking

Abstract

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 regardless of query complexity, resulting in high latency and wasted computational resources on simple queries. We propose Adaptive Re-Ranking, an utility-based labeling framework for cost-aware routing and present empirical evidence (via oracle analysis and a trained baseline router) that per-query routing offers large potential gains but is non-trivial to learn from limited supervision. We train a routing classifier with 3 strategies: sparse retrieval (BM25), dense re-ranking (MiniLM-L6-v2), and heavy neural re-ranking (BGE-v2-m3). Compared to BGE our method achieves 1.15-53x lower median latency and 1.11-5.22x lower mean latency across all datasets we have tested, while delivering -17.5% to +4.0% nDCG@10, which is competitive in some datasets. Our findings show that routing queries based on our novel utility function offers a scalable solution for reducing computational costs and latency in a variety of IR systems.