RoBERTa: An Optimized Method for Pretraining Self-supervised NLP Systems

RoBERTa (Robustly Optimized BERT Pretraining Approach) is an improved version of BERT designed to address its limitations and enhance performance across various NLP problems. It introduced several key improvements, including dynamic masking, removal of the next sentence prediction task, larger training data and extended duration, increasing batch sizes, and byte text encoding. These modifications led to significant improvements in model performance on downstream tasks compared to the originally reported BERT results.