3 citations · 4 across the 9 of their papers we have counts for
9 papers · 1 filter
Closing the Indexing-Decoding Gap in Multimodal Generative Retrieval via Prefix Retention Optimization
Yufei Chen, Zihan Wang, Yubao Tang +3
Multimodal generative retrieval formulates multimodal retrieval as discrete identifier generation, eliminating the need for explicit similarity search over external embeddings. Exi…
Lost in Decoding? Reproducing and Stress-Testing the Look-Ahead Prior in Generative Retrieval
Kidist Amde Mekonnen, Yongkang Li, Yubao Tang +2
Generative retrieval (GR) ranks documents by autoregressively generating document identifiers. Because many GR methods rely on trie-constrained beam search, they are vulnerable to…
A Parametric Memory Head for Continual Generative Retrieval
Kidist Amde Mekonnen, Yubao Tang, Maarten de Rijke
Generative information retrieval (GenIR) consolidates retrieval into a single neural model that decodes document identifiers (docids) directly from queries. While this model-as-ind…
Multi-Step Semantic Reasoning in Generative Retrieval
Steven Dong, Yubao Tang, Maarten de Rijke
Generative retrieval (GR) models encode a corpus within model parameters and generate relevant document identifiers directly for a given query. While this paradigm shows promise in…
Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval
Kidist Amde Mekonnen, Yubao Tang, Maarten de Rijke
Generative information retrieval (GenIR) is a promising neural retrieval paradigm that formulates document retrieval as a document identifier (docid) generation task, allowing for…
Generative Retrieval for Book search
Yubao Tang, Ruqing Zhang, Jiafeng Guo +5
In book search, relevant book information should be returned in response to a query. Books contain complex, multi-faceted information such as metadata, outlines, and main text, whe…
Generative Retrieval Meets Multi-Graded Relevance
Yubao Tang, Ruqing Zhang, Jiafeng Guo +3
Generative retrieval represents a novel approach to information retrieval. It uses an encoder-decoder architecture to directly produce relevant document identifiers (docids) for qu…
Bootstrapped Pre-training with Dynamic Identifier Prediction for Generative Retrieval
Yubao Tang, Ruqing Zhang, Jiafeng Guo +3
Generative retrieval uses differentiable search indexes to directly generate relevant document identifiers in response to a query. Recent studies have highlighted the potential of…
Listwise Generative Retrieval Models via a Sequential Learning Process
Yubao Tang, Ruqing Zhang, Jiafeng Guo +3
Recently, a novel generative retrieval (GR) paradigm has been proposed, where a single sequence-to-sequence model is learned to directly generate a list of relevant document identi…