PDMR: Passage-Driven Multi-ID Document Retrieval

• URL: http://arxiv.org/abs/2609.08762v1
• Date: Tue, 08 Sep 2026 13:58:59 GMT
• Title: PDMR: Passage-Driven Multi-ID Document Retrieval
• Abstract summary: Passage-Driven Multi-ID Retrieval (PDMR) is a generative retrieval framework that represents documents through multiple passage-level identifiers.<n>PDMR segments each document and assigns one identifier to each selected passage, which provides multiple semantic entry points for retrieving the same document.
• Score: 1.1661238776379117
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative Retrieval (GR) models map queries directly to document identifiers, replacing conventional retrieval over external sparse or dense indexes with autoregressive identifier generation. However, most generative retrieval frameworks rely on a single-identifier assumption, mapping each document to a single target sequence. This forces the model to represent all document content with one sequence. Since documents are often multi-faceted, this can lead to lossy representations and reduced robustness to query variation, where multiple query intents must compete for a single generative access path. In this work, we introduce Passage-Driven Multi-ID Retrieval (PDMR), a generative retrieval framework that represents documents through multiple passage-level identifiers. PDMR segments each document and assigns one identifier to each selected passage, which provides multiple semantic entry points for retrieving the same document. This multi-entry representation allows the model to align queries with specific semantic facets, thereby reducing the dependence on a single document-level target. To address the supervision ambiguity of this one-to-many mapping, we formulate training as a multi-target learning problem and explore an objective function designed to distribute probability mass across multiple valid passage-level identifiers. We evaluate PDMR on NQ320K and MS MARCO Document. On NQ320K, PDMR improves over strong generative and non-generative baselines on Recall@1 and MRR@100. On MS MARCO Document, PDMR achieves the best Recall@1 and MRR@10 among the reported methods, while remaining competitive on Recall@10. Controlled ablations further show that passage-level supervision, identifier design, training-query augmentation, and multi-target learning contribute complementary gains.

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