In an Information Retrieval (IR) system, reranking plays a critical role by sorting candidate passages according to their relevance to a specific query. This process demands a nuanced understanding of the variations among passages linked to the query. In this work, we introduce RankFlow, a multi-role reranking workflow that leverages the capabilities of Large Language Models (LLMs) and role specializations to improve reranking performance. RankFlow enlists LLMs to fulfill four distinct roles: the query Rewriter, the pseudo Answerer, the passage Summarizer, and the Reranker. This orchestrated approach enables RankFlow to: (1) accurately interpret queries, (2) draw upon LLMs' extensive pre-existing knowledge, (3) distill passages into concise versions, and (4) assess passages in a comprehensive manner, resulting in notably better reranking results. Our experimental results reveal that RankFlow outperforms existing leading approaches on widely recognized IR benchmarks, such as TREC-DL, BEIR, and NovelEval. Additionally, we investigate the individual contributions of each role in RankFlow.

翻译：在信息检索系统中，重排序通过根据候选段落与特定查询的相关性对其进行排序起着关键作用。此过程需要深入理解与查询相关的段落之间的差异。本研究提出RankFlow，一种利用大语言模型能力与角色分工提升重排序性能的多角色重排序工作流。RankFlow调用大语言模型扮演四个不同角色：查询重写器、伪答案生成器、段落摘要器和重排序器。这种编排方法使RankFlow能够：（1）精准解释查询意图，（2）调用大语言模型丰富的预训练知识，（3）将段落提炼为精简版本，（4）以全面方式评估段落，从而显著提升重排序效果。实验结果表明，在TREC-DL、BEIR、NovelEval等广泛认可的信息检索基准测试中，RankFlow优于现有主流方法。此外，我们还探究了RankFlow中各角色的独立贡献。