EcoRank: Budget-Constrained Text Re-ranking Using Large Language Models
This addresses the cost problem for users of LLM-based re-ranking systems, offering an incremental improvement in efficiency.
The paper tackles the high cost of using Large Language Models (LLMs) for text re-ranking by proposing EcoRank, a two-layered pipeline that optimizes budget allocation across prompt strategies and LLM APIs, achieving superior performance on four QA and passage reranking datasets compared to other budget-aware methods.
Large Language Models (LLMs) have achieved state-of-the-art performance in text re-ranking. This process includes queries and candidate passages in the prompts, utilizing pointwise, listwise, and pairwise prompting strategies. A limitation of these ranking strategies with LLMs is their cost: the process can become expensive due to API charges, which are based on the number of input and output tokens. We study how to maximize the re-ranking performance given a budget, by navigating the vast search spaces of prompt choices, LLM APIs, and budget splits. We propose a suite of budget-constrained methods to perform text re-ranking using a set of LLM APIs. Our most efficient method, called EcoRank, is a two-layered pipeline that jointly optimizes decisions regarding budget allocation across prompt strategies and LLM APIs. Our experimental results on four popular QA and passage reranking datasets show that EcoRank outperforms other budget-aware supervised and unsupervised baselines.