Reranking documents based on their relevance to a given query is critical in information retrieval. Traditional reranking methods often focus on improving the initial rankings but lack transparency, failing to explain why one document is ranked higher. In this paper, we introduce ReasoningRank, a novel reranking approach that enhances clarity by generating two types of reasoning: explicit reasoning, which explains how a document addresses the query, and comparison reasoning, which justifies the relevance of one document over another. We leverage large language models (LLMs) as teacher models to generate these explanations and distill this knowledge into smaller, more resource-efficient student models. While the student models may not outperform LLMs in speed, they significantly reduce the computational burden by requiring fewer resources, making them more suitable for large-scale or resource-constrained settings. These student models are trained to both generate meaningful reasoning and rerank documents, achieving competitive performance across multiple datasets, including MSMARCO and BRIGHT. Experiments demonstrate that ReasoningRank improves reranking accuracy and provides valuable insights into the decision-making process, offering a structured and interpretable solution for reranking tasks.

翻译：基于文档与给定查询的相关性进行重排序在信息检索中至关重要。传统的重排序方法通常侧重于改进初始排名，但缺乏透明度，无法解释为何某个文档排名更高。本文提出ReasoningRank，一种新颖的重排序方法，通过生成两种类型的推理来增强清晰度：显式推理，解释文档如何回应查询；以及比较推理，证明一个文档相对于另一个文档的相关性。我们利用大语言模型作为教师模型来生成这些解释，并将这些知识蒸馏到更小、资源效率更高的学生模型中。虽然学生模型在速度上可能无法超越大语言模型，但它们通过所需资源更少，显著降低了计算负担，使其更适用于大规模或资源受限的场景。这些学生模型经过训练，既能生成有意义的推理，又能对文档进行重排序，在包括MSMARCO和BRIGHT在内的多个数据集上实现了有竞争力的性能。实验表明，ReasoningRank提高了重排序的准确性，并为决策过程提供了有价值的见解，为重排序任务提供了一种结构化且可解释的解决方案。