Are transformers only useful for text?
No. Transformer architectures can process representations of images, audio, and other data. The input representation and training objective determine the task they learn.
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No. Attention mechanisms existed before the transformer. The influential contribution was an architecture that relied on attention instead of recurrence for its central sequence-processing design.
No. Standard autoregressive generation chooses each new token using the preceding context. Parallelism during training does not remove that dependency in ordinary generation.
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