Self-attention is the core mechanism behind the Transformer architecture, allowing each token in a sequence to focus on other relevant tokens to capture contextual relationships. Instead of processing input sequentially, it computes attention scores between all token pairs in parallel. This helps the model understand dependencies regardless of distance in the sequence. Each token's representation is updated based on a weighted sum of all tokens, where the weights are determined by their relevance. Self-attention enables richer feature representations and is especially powerful for tasks like language modeling. In the context of the article, it's the primary subject of exploration. Understanding self-attention is key to grasping how modern NLP models operate.