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arXiv · 2111.01243

Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey

Bonan Min·Hayley Ross·Elior Sulem·Amir Pouran Ben Veyseh·Thien Huu Nguyen·Oscar Sainz·Eneko Agirre·Ilana Heinz·Dan Roth
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Abstract

Large, pre-trained transformer-based language models such as BERT have drastically changed the Natural Language Processing (NLP) field. We present a survey of recent work that uses these large language models to solve NLP tasks via pre-training then fine-tuning, prompting, or text generation approaches. We also present approaches that use pre-trained language models to generate data for training augmentation or other purposes. We conclude with discussions on limitations and suggested directions for future research.

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Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heinz, Dan Roth. 2021-11-01. Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey. https://arxiv.org/abs/2111.01243

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