Example Concept: Transformers utilize a self-attention mechanism that computes attention scores for each token in a sequence relative to all other tokens. This allows the model to focus on relevant parts of the input sequence, regardless of their distance from each other. In contrast, traditional neural networks, like RNNs, process sequences sequentially, which can lead to difficulties in capturing dependencies over long distances due to vanishing gradient problems.