that the model can easily process.
Imagine you have a book with pictures of different animals: a cat, a dog, a lion, and so on. Now, instead of showing the entire picture, you want to give a short numerical description of each animal.
The Embedding Layer does something similar, but with words. When you tell it the word «cat,» it can transform it into a set of numbers, like [0.2, 0.5, 0.7].
This set of numbers (or vector) now represents the word «cat» for the computer. Thus, instead of working with letters and words, the model works with these numerical representations, making its processing much faster and more efficient.
For example, the word «dog» might be [0.3, 0.6, 0.1], and «lion» – [0.9, 0.4, 0.8]. Each word gets its unique numerical «portrait,» which helps the model understand and process the text.
Recurrent Layers: They are used for processing sequences, such as sentences or paragraphs.
Recurrent Neural Networks (RNNs) and their variations, like LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Units), are popular choices for these layers, as they can «remember» information from previous parts of the sequence.
Imagine reading a book and every time you turn the page, you forget what happened before. It would be hard to understand the story, wouldn’t it?
But in real life, when you read a book, you remember the events of previous pages and use this information to understand the current page.
RNNs work in a similar way. When they process words in a sentence or paragraph, they «remember» previous words and use this information to understand the current word.
For example, in the sentence «I love my dog because she…» the word «she» refers to «dog,» and the RNN «remembers» this.
Variations of RNNs, like LSTM and GRU, are designed to «remember» information even better and for longer periods of time.
Transformers: This is a modern architecture that uses attention mechanisms to process information.
Models based on transformers, such as GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers), have shown outstanding results in language modeling tasks.
We will talk about these two models in more detail in the following chapters, compare their principles of operation, and try to give them our assessment.
Output Layer: Usually, this is a fully connected layer that transforms the model’s hidden states into probabilities of the next word or token in the sequence.
Imagine a candy factory. In the early stages of production, ingredients are mixed, processed, and formed into semi-finished products.
But before the candies are packaged and sent to stores, they pass through the final stage – a control device that checks each candy and determines whether it is suitable for sale.
The output layer in a neural network works similarly to this control device. After all the information has been processed within the model, the output layer transforms it into the final result.
In the case of a language model, it determines the probabilities of what the next word or token will be in the sequence.
So, if the model reads the phrase «I love to eat…", the output layer might determine that the words «apples,» «chocolate,» and «ice cream» have a high probability of being the next word in this phrase.
The architecture of the language model determines how it will learn and how it will generate text. The choice of the right architecture depends on the specific task, the volume of data, and the required performance.
Moreover, language models don’t just mechanically generate texts. They «understand» context. For example, if you ask them a question about finance, the answer will be relevant.
They are trained on such a vast dataset that they can account for nuances, idioms, and language specifics.
Language models are a tool that may soon become an integral part of your business process. They offer new possibilities, making text processing and creation more efficient, faster, and innovative.
The first steps in the field of language models were taken decades ago. If we could go back in time to the beginnings of the computer era, we would see that the initial language systems were primitive and limited.
They were based on simple rules and templates. But, as in many areas, progress did not stop. In the 1980s, statistical language models were developed.
They used probabilistic approaches to predict the next word in a sequence. This was a big step forward, but still far from perfect.
With the advent of the 2000s, thanks to increased computing power and the availability of large volumes of data, the era of deep learning began.
It was during this period that we began to see real breakthroughs in the field of language models. Networks, such as LSTM (Long Short-Term Memory) and transformers, implemented new approaches to language processing.
A significant milestone was the creation of the BERT model in 2018 by Google. This model was capable of understanding the context of a word in a sentence, which was considered a revolutionary achievement.
But an even bigger resonance was caused by the appearance of GPT models, especially GPT-3 and GPT-4, from the American startup OpenAI.
With its ability to generate quality texts based on a given context, it represented a real revolution in the field of language models.
Each stage in the history of language models carried its own lessons and challenges. But the general trend was clear: from simple rules to complex algorithms, from limited models to systems capable of «thinking» and «creating».
Looking back on this journey, we can only marvel at how far we have come. But, as in any business, the key to success lies in understanding the past to better see the future and understand how they work.
When we, as humans, learn something new, we rely on our experience, knowledge, and understanding of the world. And what if language models learn in a similar way, but on a much larger and accelerated scale?
Let’s imagine that every book, article, or blog you have ever read is just a small part of what a language model is trained on.
They «read» millions and billions of lines of text, trying to understand the structure, grammar, stylistics, and even nuances such as irony or metaphors.
At the heart of this process lies a neural network. This is an architecture inspired by the structure of the human brain.
Neural networks consist of layers, each of which processes information and passes it to the next layer, refining and improving the result.
Transformers, which I mentioned earlier, are a special type of neural networks. They can process different parts of the text simultaneously, allowing them to understand the context and relationships between words.
Think of language models as musicians playing instruments. The texts are the notes, and the algorithms and mathematics are the instruments.
