📄 Attention is All You Need
The paper that changed everything
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0 / 5 completedThe Landmark Paper
📚 Publication Details
Lead Authors
- •Ashish Vaswani
- •Noam Shazeer
- •Niki Parmar
- •Jakob Uszkoreit
- •+ 4 others
💡 The Big Idea
Before this paper, sequence modeling relied heavily on recurrent neural networks (RNNs) and LSTMs. The authors proposed a radical idea: eliminate recurrence entirely and rely solely on attention mechanisms. This simple change enabled:
⚡ Parallelization
Process all tokens simultaneously instead of sequentially
🔗 Long-Range Dependencies
Direct connections between any two positions in the sequence
📈 Scalability
Train larger models on more data efficiently
🎯 Interpretability
Visualize what the model attends to
🎯 Key Contributions
Attention mechanism without recurrence
Parallel attention in different subspaces
Inject sequence order information
Train much faster than RNNs
🌟 Impact Summary
This single paper sparked a revolution in deep learning. The Transformer architecture became the foundation for BERT, GPT, T5, and virtually every major breakthrough in NLP since 2017. It expanded beyond text to vision (ViT), speech, and multi-modal AI. With over 100,000 citations, it's one of the most influential AI papers of all time.