Explores decoding from neural models in modern NLP, covering encoder-decoder models, decoding algorithms, issues with argmax decoding, and the impact of beam size.
Explores pretraining sequence-to-sequence models with BART and T5, discussing transfer learning, fine-tuning, model architectures, tasks, performance comparison, summarization results, and references.
Explores chemical reaction prediction using generative models and molecular transformers, emphasizing the importance of molecular language processing and stereochemistry.
