Dr. Ernesto Lee · Follow
9 min read · Oct 13, 2023
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This is the technology that makes ChatGPT works!
Imagine a world where language barriers no longer exist — a place where communication between people from different corners of the world becomes seamless. Now, picture an artificial intelligence system that can understand and generate human-like text, fostering innovation and enhancing user experiences across the digital realm. The groundbreaking research paper “Attention Is All You Need” by Vaswani et al., has paved the way for this transformative vision.
Published in 2017, this paper introduced the Transformer architecture, which has since become a cornerstone in modern natural language processing (NLP) techniques. Prior to its inception, recurrent neural networks (RNNs) and their variations like LSTM and GRU were the go-to solutions for sequential data processing. While effective, these models had their limitations, particularly in terms of training efficiency and handling long-range dependencies within data.
Enter the concept of “Attention”. Rather than processing data sequentially, the Attention mechanism allows the model to focus on different parts of the input data, providing it with a kind of “short-term memory” to discern what’s essential. This innovation enabled models to capture intricate patterns and relationships in data with remarkable accuracy.
So, why is this paper of monumental importance?
- Basis for Subsequent Innovations: The Transformer architecture forms the foundation for models like BERT, GPT, and T5, which have dominated NLP tasks ranging from translation to text generation.
- Elevated AI Capabilities: With the power of Attention, AI models can now generate more coherent and contextually relevant content. This led to enhanced chatbots, improved search engines, and more reliable language translation tools.
- Democratization of AI: The rise of pre-trained models, which owe their genesis to the Transformer, means businesses and developers without vast resources can now access state-of-the-art AI capabilities.
As we delve deeper into this concept, we’ll unravel the intuitive magic behind “Attention Is All You Need”. We’ll explore how this single concept has had a cascading impact, revolutionizing AI and establishing a new paradigm in machine learning. Whether you’re an AI enthusiast or a seasoned researcher, understanding this paper is like witnessing a defining moment in the history of technology. Welcome to the journey!
Imagine you’re reading a lengthy novel, and every time you come across a pronoun like “he” or “she”, you instantly recall who it refers to from the previous paragraphs or chapters. Instead of reading the novel linearly from start to finish to understand every sentence’s context, your brain intuitively “attends” to the relevant parts that help make sense of the current sentence.
This ability to “refer back” or “pay attention” to specific parts of a text for comprehension is exactly how the Attention mechanism in neural networks like ChatGPT.
Let’s take a simplified example:
Sentence: “Dr. Lee, who loves his dog Daisy, often takes her to the park and gives her several “gooooboyyys.”
If you wanted to know who “her” refers to, your brain would likely “attend” more to the words “Dr. Lee” and “dog Daisy” to derive the context. In essence, you assign different “weights” to different words based on their relevance.
In the Attention mechanism:
- Words in a sentence are assigned different weights.
- These weights determine how much focus or “attention” each word gets when trying to predict or comprehend another word.
Connecting to ChatGPT:
ChatGPT, like other Transformer-based models, leverages this Attention mechanism. When you pose a question, the model doesn’t just look at your current message in isolation. It “attends” to various parts of the entire conversation context, assigning weights to different parts based on relevance. This ability helps the model generate relevant and contextually appropriate responses.
So, just as your brain attends to specific words in our example sentence to understand the reference of “her”, ChatGPT attends to different parts of the conversation to provide a coherent reply.
Let’s break this down using the architecture diagram directly from this seminal paper:
Input Embedding & Source Sentence:
- You input the sentence “The cat sat on the mat.”
- The model first turns these words into numerical vectors through a process called “embedding.”
Positional Encoding:
- Since transformers don’t inherently understand the order of words, this step adds information about the position of each word in the sentence. So, the word “cat” might get some positional information indicating it’s the second word.
Multi-Head Attention (Left side):
- This is the magic step where the model figures out which words in the sentence are important and relevant to each other. Here, it might recognize that “cat” is closely related to “sat” (because the cat is the one doing the sitting).
Add & Norm:
- After attention is applied, the model normalizes the data, ensuring it’s in a format that’s easy for the model to work with.
Feed Forward:
- This is just a simple neural network that does further transformations on the data.
Now, on to the right side, which is the decoder:
Output Embedding & Output Sentence:
- The decoder starts its process by looking at any prior translations. Initially, it might just be a starting token indicating the beginning of a translation.
Masked Multi-Head Attention:
- The decoder pays attention to the previously translated words. This is “masked” to ensure the model doesn’t look ahead at future words, which it shouldn’t know yet.
Multi-Head Attention (Right side):
- Now, the decoder also pays attention to the encoder’s output, trying to figure out the best word to translate next. So, it might look at “cat” and decide the next French word should be “chat”.
Add & Norm:
- Again, the data is normalized.
Feed Forward:
- The neural network does its transformations.
Linear & Softmax:
- These steps convert the decoder’s internal data back into actual words. After going through this, the model might output the word “chat” as the translation for “cat”.
This process continues for each word until the full sentence “Le chat s’est assis sur le tapis” is generated.
Remember, this is a very simplified example. In reality, the model considers many possible translations at once and uses complex math to choose the best words. But this should give you a basic idea of how data flows through a transformer when translating a sentence!
Imagine Teaching a Robot to Translate
You want your robot to translate the sentence “The cat sat on the mat” into another language. But how does it do it? Let’s walk through the steps using the architecture diagram from the original paper:
Inputting The Sentence:
- What’s Happening? You feed the sentence to the robot.
- Image Guide: Look at the “Inputs” box at the bottom-left.
- Simple Analogy: Think of this as giving the robot a puzzle to solve.
Understanding Word Positions:
- What’s Happening? The robot marks each word with a number to know its position.
- Image Guide: This is the “Positional Encoding” box.
- Simple Analogy: It’s like labeling each puzzle piece with a number so you know where it fits.
Deciding Word Importance:
- What’s Happening? The robot determines which words are more related or important to others.
- Image Guide: This is the “Multi-Head Attention” section on the left.
- Simple Analogy: Imagine the robot shining a flashlight brighter on important puzzle pieces, making them easier to see and fit together.
Smoothing Things Out:
- What’s Happening? The robot ensures everything is balanced and in order.
- Image Guide: The “Add & Norm” and “Feed Forward” sections.
- Simple Analogy: After placing a few puzzle pieces, the robot smooths them out to make sure they fit perfectly.
Now, the robot has understood your sentence. It’s time to translate it.
Starting the Translation:
- What’s Happening? The robot begins translating, word by word.
- Image Guide: Check the “Output Embedding” section on the bottom-right.
- Simple Analogy: The robot starts assembling a new puzzle using clues from the old one.
Choosing the Right Words:
- What’s Happening? The robot decides the best word for translation using clues from both the original and what it has translated so far.
- Image Guide: This is the “Masked Multi-Head Attention” and the right “Multi-Head Attention”.
- Simple Analogy: Think of the robot peeking at the picture on the puzzle box for hints.
Final Touches:
- What’s Happening? The robot finalizes the translation.
- Image Guide: The “Linear & Softmax” section at the top.
- Simple Analogy: The final puzzle piece is placed, revealing the full picture.
In the end, your robot translates “The cat sat on the mat” to a sentence in another language, piece by piece, ensuring every word fits perfectly in its new place.
The marvel of generative AI, much like the intricate process of assembling a jigsaw puzzle, hinges on careful attention to details and understanding the bigger picture. By drawing parallels between translating sentences and piecing together puzzles, we unravel the intricate workings of models like the one depicted in the image. The step-by-step breakdown provides a glimpse into the meticulous methodology employed by these Generative AI systems.
Understanding this intuition is critical for several reasons:
- Demystifying AI: Simplifying complex mechanisms makes AI more approachable for the general public, removing the ‘magic’ and revealing the structured logic underneath.
- Informed Decision Making: Grasping the foundational principles aids businesses, developers, and users in making more informed decisions about AI’s applications and potential.
- Fostering Trust: Knowledge begets trust. When people comprehend how AI works, even at a basic level, they are more likely to trust and use it responsibly.
- Stimulating Innovation: When more minds understand the core concepts, it paves the way for innovation, leading to more advanced and tailored applications of generative AI.
In essence, just as understanding the nuances of puzzle pieces helps in piecing together a coherent picture, comprehending the foundational principles of AI lays the groundwork for harnessing its full potential. As AI continues to permeate every facet of our lives, this understanding will be the key to unlocking a future where humans and machines work in harmony, each amplifying the other’s strengths.