Attention mechanisms have significantly shaped NLP systems, initially revolutionizing neural machine translation and later extending to various applications like question answering and summarization. Despite the initial excitement surrounding transformers, the pace of innovation has slowed over the past five years, with many researchers advocating for scaling existing models rather than developing new architectures. However, emerging ideas, such as hierarchical attention, hint at potential advancements for handling longer contexts.
