ai-agent-book/chapter5/paper-to-ppt/validation/runs/exp5-4-real-pdf-both-20260730-v4/dual_round3_slides.md

theme, title, author, date

theme	title	author	date
default	Attention Is All You Need	Ashish Vaswani et al.	NIPS 2017

Attention Is All You Need

Authors: Ashish Vaswani et al.
Conference: NIPS 2017

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Abstract

Key Innovation

• First sequence transduction model based solely on attention
• Dispenses with recurrence and convolutions entirely
• More parallelizable with significantly less training time

Performance Highlights

• WMT 2014 EN-DE: 28.4 BLEU (+2+ over previous SOTA)
• WMT 2014 EN-FR: 41.8 BLEU (new single-model SOTA)
• Trained in 3.5 days on 8 GPUs (small fraction of previous costs)

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Background & Key Idea

Traditional Sequence Models

• Recurrent (LSTM/GRU): Sequential computation limits parallelization
• Convolutional: Requires multiple layers for long-range dependencies

The Transformer

• Replaces recurrence/convolution with self-attention
• Enables direct modeling of long-range dependencies
• Massive parallelization → faster training

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Transformer Architecture

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Encoder Stack

• 6 identical layers with two sub-layers:
  1. Multi-head self-attention mechanism
  2. Position-wise feed-forward network
• Residual connections around each sub-layer
• Layer normalization after each sub-layer
• Output dimension: d_model = 512

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Decoder Stack

• 6 identical layers with three sub-layers:
  1. Masked multi-head self-attention (prevents leftward flow)
  2. Multi-head attention over encoder output
  3. Position-wise feed-forward network
• Same residual connections and normalization as encoder
• Output embeddings offset by one position (auto-regressive property)

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Attention Mechanism

Scaled Dot-Product Attention

\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V

• Scaling by \sqrt{d_k} prevents small gradients
• Efficient with matrix multiplication

Multi-Head Attention

\text{MultiHead}(Q, K, V) = \text{Concat}(\text{head}_1,...,\text{head}_h)W^O

• h=8 parallel heads, d_k=d_v=64
• Captures diverse dependency patterns

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Attention Applications

1. Encoder-decoder attention: Queries from decoder, keys/values from encoder
2. Encoder self-attention: All positions attend to all input positions
3. Decoder self-attention: Positions attend to previous positions (masked)

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Positional Encoding

Adds sequence position information via sinusoidal functions:

\text{PE}_{(pos,2i)}=\sin(pos/10000^{2i/d_{\text{model}}}) \text{PE}_{(pos,2i+1)}=\cos(pos/10000^{2i/d_{\text{model}}})

• Same dimension as embeddings (d_{\text{model}})
• Enables learning of relative positions
• Performs similarly to learned embeddings

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Why Self-Attention?

Layer Type	Complexity	Sequential Ops	Max Path Length
Self-Attention	O(n^2 \cdot d)	O(1)	O(1)
Recurrent	O(n \cdot d^2)	O(n)	O(n)
Convolutional	O(k \cdot n \cdot d^2)	O(1)	O(\log_k n)

• Better parallelization than RNNs
• Shorter path length than CNNs
• More interpretable attention patterns

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Training Data & Batching

• Datasets:
  • WMT 2014 EN-DE (4.5M sentence pairs)
  • WMT 2014 EN-FR (36M sentence pairs)
• Tokenization:
  • Byte-pair encoding with shared vocabulary
  • 37K tokens (EN-DE), 32K tokens (EN-FR)
• Batching:
  • Sentences grouped by approximate length
  • Each batch contains ~25K source and target tokens

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Training Hardware & Schedule

• Hardware: 8 NVIDIA P100 GPUs
• Base model:
  • 100,000 training steps
  • ~12 hours total training time
• Big model:
  • 300,000 training steps
  • ~3.5 days total training time

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Training Optimization

• Optimizer: Adam

  • \beta_1 = 0.9, \beta_2 = 0.98, \epsilon = 10^{-9}
  • Learning rate schedule with warmup steps=4000

• Regularization:

  • Residual dropout (P_drop = 0.1)
  • Label smoothing (\epsilon_{ls} = 0.1)
  • Dropout on embeddings + positional encodings

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Machine Translation Results

Model	EN-DE BLEU	EN-FR BLEU	Training Cost (FLOPs)
GNMT + RL Ensemble	26.30	41.16	1.8 \cdot 10^{20}
ConvS2S Ensemble	26.36	41.29	7.7 \cdot 10^{19}
Transformer (big)	28.4	41.8	$2.3 \cdot 10^{19}$

• Outperforms all previous SOTA with lower training cost

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Attention Visualization: Long-Distance Dependencies

Tracking "making...more difficult" dependency across distant positions

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Attention Visualization: Anaphora Resolution

Resolving "its" to antecedent "The Law"

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Key Contributions

• Introduced Transformer, first attention-only transduction model
• Eliminated recurrence/convolution → better parallelization
• Set new SOTA in machine translation with lower training cost
• Generalizes to other tasks (e.g., constituency parsing)

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Future Work

• Apply to other modalities (images, audio, video)
• Explore local attention for large sequences
• Improve generation sequentiality
• Enhance attention interpretability

Code: https://github.com/tensorflow/tensor2tensor