LLM Optimization Unlocks Real-Time Pairwise Reranking

arXiv — cs.CL•Wednesday, November 12, 2025 at 5:00:00 AM

A recent study published on arXiv presents significant advancements in the efficiency of pairwise reranking in information retrieval systems, particularly for Retrieval-Augmented Generation (RAG). The research demonstrates that through the application of optimization methods, the latency for processing queries can be reduced by up to 166 times, from 61.36 seconds to just 0.37 seconds, while maintaining performance levels with only an insignificant drop in Recall@k. This work underscores the critical role of Large Language Models (LLMs) in reranking tasks and highlights the effectiveness of Pairwise Reranking Prompting (PRP) as a practical solution. The findings emphasize the importance of design choices that can enhance the usability and effectiveness of these systems, making real-time applications more feasible.

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