The answer
Not by itself. Many heads, layers, residual paths, and later transformations contribute to an answer, so one attention weight is not a complete causal explanation.
How does attention help transformers understand sequences and context?
Does the largest attention weight explain why an answer was generated?
More FAQs in How does attention help transformers understand sequences and context?
Yes, when the architecture’s attention pattern permits those positions to interact within the available context. Some models restrict attention through windows or other patterns.
Those tokens are not yet available during generation. Masking future positions during training keeps the prediction task consistent with that information constraint.
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