Does parallel training remove the need for causal masking?
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.
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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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