Year: 2025
Type: conference-paper
Abstract: In an Information Retrieval (IR) system, reranking plays a pivotal role by ordering candidate passages based on their relevance to a specific query.This process necessitates a fine-grained understanding of the variations among passages associated with the query.However, existing zero-shot reranking methods often rely on the original queries and passages from the dataset without modification, which can sometimes be vague, ambiguous, or redundant.In this work, we introduce RankFlow , a multi-role reranking framework designed to address these limitations and enhance reranking performance.RankFlow decomposes the reranking task into four components by leveraging large language models to perform four distinct roles: the query Rewriter, the pseudo Answerer, the passage Summarizer, and the Reranker.This structured approach enables RankFlow to (1) more accurately interpret queries, (2) utilize the vast pre-trained knowledge of LLMs, (3) distill passages into concise representations, and (4) comprehensively evaluate passage relevance, resulting in significantly improved reranking outcomes.Our experimental results demonstrate that RankFlow surpasses state-of-the-art methods on well-established IR benchmarks, including TREC-DL, BEIR, and NovelEval.Notably, on the less contaminated NovelEval dataset, RankFlow achieves an improvement of over 5 points in NDCG@10 than RankGPT-4.Furthermore, we * Both authors contributed equally to this research.
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: Universitas Brawijaya
Awards: G1896033094, G3795423919, G5354172317
OpenAlex ID: W4410636400