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Generative Retrieval (GR), autoregressively decoding relevant document identifiers given a query, has been shown to perform well under the setting of small-scale corpora.
On relevance weights with little relevance information
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Language models are few-shot learners
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Dense passage retrieval for open-domain question answering
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ERNIE 2.0: A continual pre-training framework for language understanding
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A neural corpus indexer for document retrieval
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Adversarial retriever-ranker for dense text retrieval
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Rocketqav2: A joint training method for dense passage retrieval and passage re-ranking
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Approximate nearest neighbor negative contrastive learning for dense text retrieval
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Autoregressive search engines: Generating substrings as document identifiers
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Learning diverse document representations with deep query interactions for dense 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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Learning to tokenize for generative retrieval
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