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- W4382246105 abstract "Large, pre-trained language models (PLMs) such as BERT and GPT have drastically changed the Natural Language Processing (NLP) field. For numerous NLP tasks, approaches leveraging PLMs have achieved state-of-the-art performance. The key idea is to learn a generic, latent representation of language from a generic task once, then share it across disparate NLP tasks. Language modeling serves as the generic task, one with abundant self-supervised text available for extensive training. This article presents the key fundamental concepts of PLM architectures and a comprehensive view of the shift to PLM-driven NLP techniques. It surveys work applying the pre-training then fine-tuning, prompting, and text generation approaches. In addition, it discusses PLM limitations and suggested directions for future research." @default.
- W4382246105 created "2023-06-28" @default.
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- W4382246105 date "2023-09-14" @default.
- W4382246105 modified "2023-10-03" @default.
- W4382246105 title "Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey" @default.
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- W4382246105 doi "https://doi.org/10.1145/3605943" @default.
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