Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey
It synthesizes advances in NLP for researchers and practitioners, but is incremental as a survey.
This survey reviews recent work using large pre-trained language models like BERT to solve NLP tasks through methods such as fine-tuning and prompting, and discusses limitations and future directions.
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.