Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey
Raytheon BBN Technologies（雷神BBN技术公司）；Harvard University（哈佛大学）；University of Pennsylvania（宾夕法尼亚大学）；University of Oregon（俄勒冈大学）；University of the Basque Country (UPV/EHU)（巴斯克大学 (UPV/EHU)）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
