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AI tasks encompass a wide range of domains and fields.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
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Imagenet: A large-scale hierarchical image database
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Microsoft coco: Common objects in context
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UCI machine learning repository
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Natural Questions: a benchmark for question answering research
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Model cards for model reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019 · 2019
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Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2020
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Datasheets for datasets
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé Iii, and Kate Crawford. 2021 · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
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Learning transferable visual models from natural language supervision
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Scaling language models: Methods, analysis & insights from training gopher
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An explanation of in-context learning as implicit bayesian inference
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Passage-mask: A learnable regularization strategy for retriever-reader models
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Openagi: When llm meets domain experts
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Palm: Scaling language modeling with pathways
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Scaling instruction-finetuned language models
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Self-consistency improves chain of thought reasoning in language models
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Emergent abilities of large language models
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In-context retrieval-augmented language models
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