Review of the seminal 2017 paper Attention is all you need.
These paper introducea the Transformer architecture, a dominant model in natural language processing that relies entirely on multi-head attention instead of recurrent or convolutional networks. The first paper, "Attention Is All You Need," introduces the Transformer, showcasing its superior performance and training efficiency in machine translation and constituency parsing. The subsequent papers, "Analyzing Multi-Head Self-Attention" and "Are Sixteen Heads Really Better than One?", investigate the importance and interpretability of individual attention heads within the Transformer. Both studies surprisingly conclude that a significant portion of attention heads can be removed without substantially impacting performance, revealing that many heads are redundant and that specialized heads perform the most critical functions, particularly in encoder-decoder attention.
