Research/NLP_Paper
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[BERT] Pre-training of Deep Bidirectional Transformers for Language UnderstandingResearch/NLP_Paper 2024. 7. 20. 13:51
https://arxiv.org/pdf/1810.04805AbstractWe introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pretrain deep bidirectional representations from unlabeled text by jointly conditioning on both left and right ..
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[GPT] Improving Language Understanding by Generative Pre-TrainingResearch/NLP_Paper 2024. 7. 20. 09:06
https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdfAbstractNatural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering, semantic similarity assessment, and document classification. Although large unlabeled text corpora are abundant, labeled data for learning these specific tasks is scarce, making ..
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[Transformer] Attention Is All You NeedResearch/NLP_Paper 2024. 7. 19. 09:10
※ https://arxiv.org/pdf/1706.03762AbstractThe dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing wit..