Softmax is a mathematical function that converts raw scores into probabilities. It's used in self-attention to normalize attention weights so they sum to one. The article explains how attention scores are computed using dot products, then passed through softmax to determine relevance. This operation allows the model to focus on the most important tokens. Softmax ensures that attention distributions are interpretable and differentiable. It's critical for learning and optimization. Recognizing its role clarifies how self-attention balances focus across inputs.