In recent years, artificial intelligence has experienced exponential growth, transforming the way we interact with technology. Advanced tools like ChatGPT, DALL-E, and Midjourney have captured global attention by generating coherent texts, stunning images, and synthetic voices from simple instructions. But what is the engine behind this revolution? The answer lies in a fundamental technology: Transformers.
The term GPT, which stands for Generative Pretrained Transformer, gives clues about how it works. "Generative" indicates its ability to create new content, while "pretrained" suggests that it has acquired knowledge from a vast amount of data and is capable of adapting to specific tasks. However, the key component is "Transformer," a type of neural network that underpins the current wave of AI.
This article will analyze, in an accessible way, how a Transformer works, breaking down the data flow step-by-step to understand how these models manage not only to predict the next word but also to generate conversations and complex texts.
The First Step: From Words to Vectors
The process begins with the text provided to the model. This text is broken down into smaller units known as tokens, which can be whole words, segments of words, or even punctuation marks. In situations where the model works with images or sounds, the tokens consist of patches of images or segments of sound.
Each of these tokens is converted into a vector, a long list of numbers in a process known as "embedding." This procedure aims to encode the meaning of each token. One could visualize these vectors as coordinates in a multi-dimensional space— for example, GPT-3 operates in a space of 12,288 dimensions. In this space, words with similar meanings tend to cluster together.
An interesting idea is that the differences in this space can have semantic significance. For instance, the vector difference between "woman" and "man" is quite similar to the difference between "queen" and "king." In this way, the model has learned to organize the language in such a way that conceptual relationships (like gender or family relationships) are geometrically represented.
To carry out this initial transformation, the model uses a large matrix known as the embedding matrix. This matrix includes a column for each token that makes up the model's vocabulary, and the corresponding values in these columns are learned and adjusted during the training process. For example, in a model like GPT-3, this matrix could contain over 617 million adjustable parameters.
The Heart of the Transformer: Attention Blocks and Multi-Layer Perceptrons
After obtaining the vectors, they pass through the core of the Transformer, a process that is repeated in several stages, incorporating series of blocks:
Attention Block
This component, considered the most innovative, allows the vectors to communicate with each other, exchanging information to update their meanings according to context. For example, the meaning of "model" varies in “a machine learning model” and “a runway model.” The attention mechanism determines which words are relevant in a context to update the meaning of other words. The primary goal of this network is to allow each vector to acquire a richer and more specific meaning than what could be represented by isolating a single word.
Multi-Layer Perceptron
After passing through the attention block, the updated vectors are then processed in a distinct operation known as a feed-forward layer. In this step, the vectors do not interact with each other; each one is processed simultaneously. It can be understood that the model carries out a series of questions about each vector and then updates them based on the answers.
These two blocks are alternated repeatedly, allowing the vectors to be refined and absorb an increasingly complex and nuanced context from the original text.
The Final Prediction: What Comes Next?
At the end of the cycle through all the attention layers and perceptrons, it is expected that the last vector in the sequence has captured all the essential meaning of the passage. This final vector is the one used to predict the next word.
The process involves multiplying this vector by another matrix, known as the unembedding matrix. This operation transforms the context vector into a long list of numbers, where each one corresponds to a token from the model's vocabulary. These numbers, called logits, represent the “score” of how likely it is that each token is the next.
However, the logits are still not probabilities. To convert them into a valid probability distribution (where all values fall between 0 and 1 and their sum is 1), a function called Softmax is applied. This function ensures that the tokens with higher scores are the ones that receive the greatest probability.
Once the model has this probability distribution, text can be generated. The process is simple yet powerful:
- An initial text is provided to the model.
- The model predicts the probability distribution for the next token.
- A sample is taken from this distribution to choose the next token.
- The new token is added to the text, and the sequence repeats over and over.
This cycle of prediction and sampling is precisely what is observed when interacting with a chatbot like ChatGPT, generating responses word by word.
The Creative Touch: “Temperature”
An intriguing aspect of the sampling process is the concept of "temperature." This parameter can be adjusted to regulate the randomness of the model's responses:
- Low temperature (close to 0): The model almost always chooses the most probable word, which may result in very predictable texts that can sometimes be repetitive or lack originality.
- High temperature: Here, the model gives more weight to less probable words, increasing creativity and originality, but also raising the risk that the text lacks coherence or sense.
This adjustment allows for a balance between coherence and creativity in the generated responses.
Conclusion: An Architecture for Language Understanding
Transformers are a monumental achievement in the field of machine learning. Their architecture, which manipulates vectors in high-dimensional spaces, utilizes attention blocks that capture context and employs an iterative prediction cycle, has proven to be extremely effective for scaling and processing natural language. Although their internal functioning involves billions of parameters and complex matrix multiplications, the fundamental principle is surprisingly simple: transforming words into contextual meanings and using those meanings to predict what comes next.
Understanding these principles not only demystifies artificial intelligence but also allows us to appreciate the depth and ingenuity behind the tools that are redefining our digital future.
To read more about related topics, we invite you to continue exploring the blog.
