Text to Text Transformer

Text-to-text transformers are a class of neural network models designed to process and generate text, enabling diverse applications like machine translation, summarization, and question answering. Current research focuses on improving their efficiency for longer sequences, enhancing zero-shot generalization through novel pretraining strategies (e.g., using model-generated signals), and adapting them to multilingual settings and specific tasks like conversational recommendations. These advancements are driving improvements in performance across various NLP benchmarks and expanding the practical applicability of text-to-text transformers in diverse fields.