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attention-is-all-you-need.pdf

15 pages · summary ready

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What is “attention” in simple terms?

Think of it as the model deciding which words to “look at” when reading each word — like how you glance back at earlier parts of a sentence to understand the ending.
p.3 §3.2Fig. 2
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Attention Is All You Need

Vaswani et al. · 2017 · 15 pages

BeginnerEngineerResearcher

Summary

This paper introduces the Transformer — a model that processes entire sequences at once using attention, instead of reading word-by-word like earlier RNNs. It trains faster and set new translation records.

Key contributions

  • First architecture built entirely on self-attention
  • Massively parallel training — days, not weeks
  • State-of-the-art BLEU on English↔German translation

Keywords

TransformerAttentionSeq2SeqMachine Translation

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Why is this faster than an RNN?

An RNN must process words one after another — step 100 waits for step 99. The Transformer looks at all words simultaneously, so the whole sequence is computed in parallel on GPUs.
p.6 §4Table 1

What are the limitations?

Attention cost grows with the square of sequence length, so very long documents get expensive — a limitation the authors note for future work.
p.9 §7
Explain Section 3Simplify the paperHow can I implement this?

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“Explain multi-head attention”

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Like several readers scanning the same sentence, each noticing something different — one tracks names, another grammar.

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Eight parallel attention layers with separate learned projections, concatenated and projected back to d_model = 512.

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Subspace projections let heads attend to disjoint representation subspaces, mitigating the averaging effect of single-head attention.

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