What are the implications of BERT and similar models on the downstream NLP tasks?

BERT (Bidirectional Encoder Representations from Transformers) and similar models have revolutionized downstream NLP tasks such as question answering, named entity recognition, and sentiment analysis. These models pre-train on a large corpus of text in a self-supervised manner, learning a rich representation of language that can be fine-tuned on smaller, task-specific datasets with much better performance than previous techniques.