Pre Trained Seq2seq Model

Pre-trained sequence-to-sequence (seq2seq) models are revolutionizing natural language processing by leveraging large datasets to learn generalizable representations for various tasks. Current research focuses on improving efficiency through techniques like initializing models from each other, enhancing robustness against adversarial examples, and adapting these models for diverse applications such as text summarization, grammatical error correction, and multi-lingual text style transfer. These advancements are significantly impacting fields like machine translation, question answering, and information extraction by enabling more accurate and efficient processing of textual data. The development of unified frameworks that address multiple tasks simultaneously is also a key area of investigation.