DIVER：一种用于推理密集型信息检索的多阶段方法
DIVER: A Multi-Stage Approach for Reasoning-intensive Information Retrieval
AI总结：
DIVER通过多阶段流程提升推理密集型信息检索性能，包含文档预处理、查询扩展、检索和重排序四个环节，有效应对需要抽象推理和多步推理的复杂查询。
摘要：
DIVER通过多阶段流程提升推理密集型信息检索性能，包含文档预处理、查询扩展、检索和重排序四个环节，有效应对需要抽象推理和多步推理的复杂查询。
英文摘要：
Retrieval-augmented generation has achieved strong performance on knowledge-intensive tasks where query-document relevance can be identified through direct lexical or semantic matches. However, many real-world queries involve abstract reasoning, analogical thinking, or multi-step inference, which existing retrievers often struggle to capture. To address this challenge, we present DIVER, a retrieval pipeline designed for reasoning-intensive information retrieval. It consists of four components. The document preprocessing stage enhances readability and preserves content by cleaning noisy texts and segmenting long documents. The query expansion stage leverages large language models to iteratively refine user queries with explicit reasoning and evidence from retrieved documents. The retrieval stage employs a model fine-tuned on synthetic data spanning medical and mathematical domains, along with hard negatives, enabling effective handling of reasoning-intensive queries. Finally, the reranking stage combines pointwise and listwise strategies to produce both fine-grained and globally consistent rankings. On the BRIGHT benchmark, DIVER achieves state-of-the-art nDCG@10 scores of 46.8 overall and 31.9 on original queries, consistently outperforming competitive reasoning-aware models. These results demonstrate the effectiveness of reasoning-aware retrieval strategies in complex real-world tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
