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Augmenting pretrained language models with retrievers has shown promise in effectively solving common NLP problems, such as language modeling and question answering.
spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing
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Pointer sentinel mixture models
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Generalization through memorization: Nearest neighbor language models
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Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies
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Chain-of-thought prompting elicits reasoning in large language models
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Unsupervised dense information retrieval with contrastive learning
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Atlas: Few-shot learning with retrieval augmented language models
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