> [!cite]- Metadata > 2025-07-22 22:12 > Status: #paper > Tags: `Read Time: 2m 16s` > [!Abstract] [Attention Is All You Need](https://arxiv.org/pdf/1706.03762) ### **One-Sentence Summary** >The 2017 paper “Attention Is All You Need” introduced the Transformer architecture, which replaces recurrence and convolutions with self-attention mechanisms to achieve state-of-the-art performance in machine translation while enabling faster, more parallelizable training. ### Concepts Explained for Beginners Imagine you're reading a sentence and trying to understand each word based on the entire context—not just the previous word, but the whole sentence, instantly. The Transformer works this way: it pays attention to all parts of a sentence at once, deciding which words matter most when interpreting another word. ### Core ideas simplified: **Self-Attention:** A mechanism that allows every word to look at every other word in a sentence and decide how relevant they are to understanding each other. **Transformer:** A model built entirely from layers of self-attention and simple math operations (no RNNs or CNNs), allowing for faster training and better accuracy. **Encoder-Decoder Architecture:** The encoder reads the input (e.g., English), and the decoder generates the output (e.g., German), both using stacks of self-attention and feed-forward layers. **Multi-Head Attention:** Instead of a single attention pass, the model splits its attention into multiple “heads,” each looking at the sentence in a different way (e.g., grammar, meaning). **Positional Encoding:** Since there's no sequence order built-in, sine and cosine patterns are added to input tokens to help the model know word order. **Feedforward Layers:** Simple neural nets applied to each word’s representation after attention to help with learning deeper patterns. **Masking:** Ensures the decoder doesn't "peek" at future words when generating text, preserving sequential logic. ### Key Components / Steps Involved Input Preparation Tokenize input sentences (e.g., using Byte Pair Encoding) Add positional encodings to represent word order Encoder (Stack of 6 layers) Self-attention layer (each word attends to all others) Feed-forward layer Decoder (Stack of 6 layers) Masked self-attention (no peeking ahead) Encoder-decoder attention (look at encoder output) Feed-forward layer Output Use softmax to predict next word one token at a time Apply beam search to generate best sequence ### How It’s Used in Application The Transformer architecture is now the foundation of almost all modern NLP models, including: Google Translate (language translation) GPT / ChatGPT / Claude / Gemini (text generation) BERT / RoBERTa (language understanding for search engines, chatbots) DALL·E / Stable Diffusion (in combination with vision transformers for text-to-image) ### Examples of Use **Text-to-text:** English → French machine translation (e.g., "Hello world" → "Bonjour le monde") **Text summarization:** Summarize a long article into a few sentences **Question answering:** Answering “What is the capital of France?” from a document **Text completion:** Writing the next sentence in a story **Search engines:** Understanding what you’re really asking ### Related Areas of Study Natural Language Processing (NLP) Neural Machine Translation (NMT) Sequence Modeling Deep Learning / Self-Supervised Learning Multimodal Learning (text + images/audio) Large Language Models (LLMs) ### Thesis – Antithesis – Synthesis **Thesis:** Sequence-to-sequence tasks like machine translation require RNNs or CNNs to model dependencies over time. **Antithesis:** These approaches are slow, hard to parallelize, and struggle with long-range dependencies. **Synthesis:** The Transformer removes both recurrence and convolution, using self-attention to model global dependencies efficiently, resulting in faster training and superior performance on translation and many other tasks. --- ### **References** [1706.03762](https://arxiv.org/pdf/1706.03762)