What is the primary reason that the Transformer architecture has revolutionized natural language processing compared to earlier models?

The Transformer’s main breakthrough is its use of self‑attention, which lets every word look directly at every other word in a sentence, so long‑range relationships are captured instantly rather than step by step. This means the model can be trained in parallel across a whole sentence instead of sequentially, drastically speeding up learning and allowing much larger datasets to be used. Because each word’s representation is updated all at once, Transformers handle context and nuance much more flexibly than RNNs or CNNs that relied on fixed‑length windows. For example, in the sentence “The bank was flooded,” the Transformer can instantly connect “bank” with “flooded” to infer a riverbank, whereas older models would struggle to link distant words. This combination of parallelism, scalability, and powerful context modeling has made Transformers the foundation for modern NLP systems.