Example Concept: Transformers use self-attention to process all elements of a sequence simultaneously, capturing dependencies between words regardless of their distance in the sequence. This contrasts with traditional RNNs, which process sequences step-by-step, potentially leading to long-term dependency issues. The self-attention mechanism assigns different attention scores to each part of the input, enabling the model to focus on relevant parts of the sequence when making predictions.