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Generative retrieval (GR) directly predicts the identifiers of relevant documents (i.e., docids) based on a parametric model.
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Natural Questions: a Benchmark for Question Answering Research
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A Neural Corpus Indexer for Document Retrieval
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Dense Text Retrieval Based on Pretrained Language Models: A Survey
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Ultron: An Ultimate Retriever on Corpus with a Model-based Indexer
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Shengyao Zhuang, Houxing Ren, Linjun Shou, Jian Pei, Ming Gong, Guido Zuccon, and Daxin Jiang. 2022 · 2022
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A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning. In SIGIR
Jiangui Chen, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Yiqun Liu, Yixing Fan, and Xueqi Cheng. 2023 · 2023
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Semantic-Enhanced Differentiable Search Index Inspired by Learning Strategies
Yubao Tang, Ruqing Zhang, Jiafeng Guo, Jiangui Chen, Zuowei Zhu, Shuaiqiang Wang, Dawei Yin, and Xueqi Cheng. 2023 · 2023
Closest in time.
DynamicRetriever: A Pre-trained Model-based IR System Without an Explicit Index
Yu-Jia Zhou, Jing Yao, Zhi-Cheng Dou, Ledell Wu, and Ji-Rong Wen. 2023 · 2023
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