3 citations · 4 across the 9 of their papers we have counts for
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cs.IR2024
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…
cs.IR2024
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…
cs.IR2024★ 1 cited
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…