What is a transformer, and why did it change NLP?

A transformer is a neural-network architecture (introduced in 'Attention Is All You Need', 2017) built around self-attention rather than recurrence. It processes all tokens in parallel and lets each token attend to every other, capturing long-range relationships efficiently. This parallelism made training on massive data practical and scaled far better than RNNs/LSTMs — which is why virtually every modern LLM is a transformer.