The answer
No. Causal masks preserve which positions a prediction may use, even when many positions are computed together. Parallel computation must still respect the learning objective.
Why was the “Attention Is All You Need” paper important for AI?
Does parallel training remove the need for causal masking?
More FAQs in Why was the “Attention Is All You Need” paper important for AI?
No. Attention mechanisms existed before the Transformer. The paper’s contribution was a particular architecture that made attention central while dispensing with recurrent and convolutional sequence layers.
No. The original paper presented an encoder-decoder architecture for sequence transduction, including machine translation. Decoder-only language models are later uses of related transformer components.
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