Does parallel training mean an LLM generates every output token at once?
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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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. 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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