Existing dense retrieval models struggle with reasoning-intensive retrieval task as they fail to capture implicit relevance that requires reasoning beyond surface-level semantic information. To address these challenges, we propose Scenario-Profiled Indexing with Knowledge Expansion (SPIKE), a dense retrieval framework that explicitly indexes implicit relevance by decomposing documents into scenario-based retrieval units. SPIKE organizes documents into scenario, which encapsulates the reasoning process necessary to uncover implicit relationships between hypothetical information needs and document content. SPIKE constructs a scenario-augmented dataset using a powerful teacher large language model (LLM), then distills these reasoning capabilities into a smaller, efficient scenario generator. During inference, SPIKE incorporates scenario-level relevance alongside document-level relevance, enabling reasoning-aware retrieval. Extensive experiments demonstrate that SPIKE consistently enhances retrieval performance across various query types and dense retrievers. It also enhances the retrieval experience for users through scenario and offers valuable contextual information for LLMs in retrieval-augmented generation (RAG).

翻译：现有的稠密检索模型在处理需要推理的检索任务时面临困难，因为它们难以捕捉需要超越表层语义信息进行推理的隐含相关性。为应对这些挑战，我们提出了基于知识扩展的场景化索引（SPIKE），这是一个通过将文档分解为基于场景的检索单元来显式索引隐含相关性的稠密检索框架。SPIKE将文档组织成场景，场景封装了揭示假设信息需求与文档内容之间隐含关系所需的推理过程。SPIKE利用强大的教师大语言模型（LLM）构建场景增强数据集，然后将这些推理能力蒸馏到更小、高效的场景生成器中。在推理阶段，SPIKE同时结合场景级相关性和文档级相关性，实现具备推理意识的检索。大量实验表明，SPIKE在各种查询类型和稠密检索器上均能持续提升检索性能。它通过场景提升了用户的检索体验，并为检索增强生成（RAG）中的大语言模型提供了有价值的上下文信息。