Generative retrieval has recently emerged as a new alternative of traditional information retrieval approaches. However, existing generative retrieval methods directly decode docid when a query is given, making it impossible to provide users with explanations as an answer for "Why this document is retrieved?". To address this limitation, we propose Hierarchical Category Path-Enhanced Generative Retrieval(HyPE), which enhances explainability by generating hierarchical category paths step-by-step before decoding docid. HyPE leverages hierarchical category paths as explanation, progressing from broad to specific semantic categories. This approach enables diverse explanations for the same document depending on the query by using shared category paths between the query and the document, and provides reasonable explanation by reflecting the document's semantic structure through a coarse-to-fine manner. HyPE constructs category paths with external high-quality semantic hierarchy, leverages LLM to select appropriate candidate paths for each document, and optimizes the generative retrieval model with path-augmented dataset. During inference, HyPE utilizes path-aware reranking strategy to aggregate diverse topic information, allowing the most relevant documents to be prioritized in the final ranked list of docids. Our extensive experiments demonstrate that HyPE not only offers a high level of explainability but also improves the retrieval performance in the document retrieval task.

翻译：生成式检索作为传统信息检索方法的新兴替代方案近期备受关注。然而，现有生成式检索方法在给定查询时直接解码文档标识符，导致无法为用户提供"为何检索此文档？"的解释性答案。为突破此局限，本文提出分层类别路径增强的生成式检索方法，该方法通过在解码文档标识符前逐步生成分层类别路径来增强可解释性。HyPE将分层类别路径作为解释机制，从宽泛到具体的语义类别逐层递进。该方法通过利用查询与文档共享的类别路径，能够为同一文档根据不同查询生成多样化解释，并通过从粗粒度到细粒度的方式反映文档语义结构，从而提供合理的解释依据。HyPE借助外部高质量语义层次结构构建类别路径，利用大语言模型为每个文档筛选合适的候选路径，并通过路径增强数据集优化生成式检索模型。在推理阶段，HyPE采用路径感知重排序策略聚合多样化主题信息，使最相关的文档能够在最终排序的文档标识符列表中优先呈现。大量实验表明，HyPE不仅具备高水平的可解释性，还能显著提升文档检索任务中的检索性能。