D3: Dynamic Docid Decoding for Multi-Intent Generative Retrieval (2026.eacl-industry)
Copied to clipboard
| Challenge: | Existing GR systems rely on offline DocID assignment and constrained decoding . offline Doc ID assignment and decoding often prevents GR from capturing query-specific intent . |
| Approach: | They propose a mechanism that adaptively refines DocIDs through query-informed identifier expansion. |
| Outcome: | The proposed mechanism improves retrieval accuracy on unseen and multi-intent documents. |
Similar Papers
DiffusionRet: Diffusion-Enhanced Generative Retriever using Constrained Decoding (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Generative retrieval methods have suffered from the lack of the intermediate reasoning step . generative retrieval uses sequence-to-sequence diffusion models to map a query to relevant docids . |
| Approach: | They propose a novel method that uses query as an intermediate step before retrieval . they propose to use sequence-to-sequence diffusion models to map a query to relevant docids . |
| Outcome: | Experiments show that proposed method outperforms existing methods on MARCO and Natural Questions datasets. |
Multi-level Relevance Document Identifier Learning for Generative Retrieval (2025.acl-long)
Copied to clipboard
Fuwei Zhang, Xiaoyu Liu, Xinyu Jia, Yingfei Zhang, Shuai Zhang, Xiang Li, Fuzhen Zhuang, Wei Lin, Zhao Zhang
| Challenge: | Existing methods generate DocIDs based on textual content, which may result in weak semantic connections for similar documents due to variations in expression. |
| Approach: | They propose a new retrieval paradigm that generates unique document identifiers . they propose to use queries as a bridge to connect documents with varying relevance levels . |
| Outcome: | The proposed approach outperforms existing methods on multilingual e-commerce search datasets. |
DFAMS: Dynamic-flow guided Federated Alignment based Multi-prototype Search (2026.acl-long)
Copied to clipboard
Zhibang Yang, Xinke Jiang, Rihong Qiu, Ruiqing Li, Yihang Zhang, Yue Fang, Yongxin Xu, Hongxin Ding, Xu Chu, Junfeng Zhao, Yasha Wang
| Challenge: | Existing methods for ambiguous queries struggle to retrieve high-quality documents . DFAMS outperforms advanced FR methods by 14.37% in knowledge classification accuracy . |
| Approach: | They propose a framework that leverages dynamic information flow to identify latent query intents and construct semantically aligned knowledge partitions for accurate retrieval across heterogeneous sources. |
| Outcome: | The proposed framework outperforms existing methods in classification accuracy and retrieval recall tests. |
Bootstrapped Pre-training with Dynamic Identifier Prediction for Generative Retrieval (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for document retrieval rely on static document identifiers . experimental results show that generative retrieval is outperforms dense retrieval in document retrievals. |
| Approach: | They propose a bootstrapped pre-training method that dynamically adjusts document identifiers during pre-train to accommodate the continuing memorization of the corpus. |
| Outcome: | The proposed method significantly outperforms existing pre-training generative retrieval baselines and performs well even in zero-shot settings. |
Multilingual Generative Retrieval via Cross-lingual Semantic Compression (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for multilingual retrieval still face cross-lingual identifier misalignment and identifiere inflation. |
| Approach: | They propose a framework that unifies semantically equivalent multilingual keywords into shared atoms to align semantics and compresses the identifier space. |
| Outcome: | The proposed framework improves cross-lingual alignment and reduces redundancy. |
Exploring the Practicality of Generative Retrieval on Dynamic Corpora (2024.emnlp-main)
Copied to clipboard
| Challenge: | a lack of comprehensive comparison between GR and Dual Encoders in IR systems is limiting . GR is more adaptable to evolving knowledge (4–11%), robust in learning knowledge with temporal information, and efficient in terms of inference FLOPs (x2), indexing time (x6) and storage footprint (x4) |
| Approach: | They propose to use autoregressive language models to perform information retrieval (IR) their results highlight the potential of GR for future use in practical IR systems . |
| Outcome: | The proposed model is more adaptable to evolving knowledge (4–11%), robust in learning knowledge with temporal information, efficient inference FLOPs (x2), indexing time (x6), and storage footprint (x4) compared to the most common model, Dual Encoder (DE). |
Nonparametric Decoding for Generative Retrieval (2023.findings-acl)
Copied to clipboard
Hyunji Lee, JaeYoung Kim, Hoyeon Chang, Hanseok Oh, Sohee Yang, Vladimir Karpukhin, Yi Lu, Minjoon Seo
| Challenge: | Existing text retrieval models depend on the information encoded in its parameters without external memory, its information capacity is limited and fixed. |
| Approach: | They propose a nonparametric decoding approach which uses external memory instead of vanilla vocab embeddings as decoder voka embedds. |
| Outcome: | The proposed model can utilize parametric and nonparametric space. |
Why These Documents? Explainable Generative Retrieval with Hierarchical Category Paths (2026.findings-acl)
Copied to clipboard
| Challenge: | Generative retrieval directly decodes a document identifier, making it impossible to provide explanations for its retrieval decision. |
| Approach: | They propose a hierarchical category path-Enhanced Generative Retrieval that generates category paths step-by-step and decodes docid. |
| Outcome: | The proposed method provides explanations for retrieval decision by generating hierarchical category paths step-by-step and decoding docid. |
MoLoRAG: Bootstrapping Document Understanding via Multi-modal Logic-aware Retrieval (2025.emnlp-main)
Copied to clipboard
| Challenge: | Document Understanding is a foundational AI capability with broad applications . Large Vision-Language Models (LLMs) can't handle multi-page document comprehension . a logic-aware retrieval framework for multi-modal, multi- page document understanding is proposed . |
| Approach: | They propose a logic-aware retrieval framework for multi-modal, multi-page document understanding . MoLoRAG uses semantic and logical relevance to deliver more accurate retrieval . |
| Outcome: | The proposed framework improves on four DocQA datasets and demonstrates 9.68% accuracy improvement over existing methods. |
Generative Dense Retrieval: Memory Can Be a Burden (2024.eacl-long)
Copied to clipboard
| Challenge: | Empirical results show that Generative Dense Retrieval (GDR) achieves an average of 3.0 R@100 improvement on NQ dataset under multiple settings and has better scalability. |
| Approach: | They propose a Generative Dense Retrieval paradigm that auto-decodes document identifiers given a query and uses memory to avoid memory confusion. |
| Outcome: | Empirical results show that the proposed paradigm improves on the small-scale corpora and improves scalability. |