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In recent years, there has been significant progress in developing pre-trained language models for NLP.
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. 2020a · 1901
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Exploiting cloze questions for few shot text classification and natural language inference
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Unsupervised paraphrase generation using pre-trained language models
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Speech and language processing daniel jurafsky and james h. martin (stanford university and university of colorado at boulder) pearson prentice hall, 2009, xxxi+ 988 pp; hardbound, isbn 978-0-13-187321-6
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It’s not just size that matters: Small language models are also few-shot learners
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Making pre-trained language models better few-shot learners
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Prototypical networks for few-shot learning
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Improving language understanding by generative pre-training
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Learning to compare: Relation network for few-shot learning
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ParaNMT-50M: Pushing the limits of paraphrastic sentence embeddings with millions of machine translations
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Parameter-efficient transfer learning for nlp
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Submodular optimization-based diverse paraphrasing and its effectiveness in data augmentation
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Ptr: Prompt tuning with rules for text classification
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Simple contrastive representation adversarial learning for nlp tasks
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Paraphrase generation: A survey of the state of the art
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