RNNs read words one by one. Over time, important info fades; early signals get lost. Gradients can vanish or explode. It’s biased toward recent inputs.
Attention solves this:
1. Each token looks at all others, not just the past 2. Relevance, not distance, determines connection strength 3. All connections computed in parallel; no waiting for previous tokens
else if
Why Attention Beats RNNs:
RNNs read words one by one. Over time, important info fades; early signals get lost. Gradients can vanish or explode. It’s biased toward recent inputs.
Attention solves this:
1. Each token looks at all others, not just the past
2. Relevance, not distance, determines connection strength
3. All connections computed in parallel; no waiting for previous tokens
This lets models focus where it matters, fast.
8 months ago | [YT] | 3
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