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.
With each new «composition,» the model becomes more skilled in its «performance.»
The work of language models is based on analyzing and understanding language in its deepest details. They literally «immerse» themselves in the text to give us outputs that can sometimes surprise even the most experienced linguists.
The training of models occurs according to certain principles. Here are some, and you will see the similarity with the principles of human learning:
Supervised Learning: This is the primary training method for most language models. Models are trained on examples where they are given both input data (text) and corresponding output data.
The goal here is to learn to make predictions or generate text based on the given examples. Imagine that you are a teacher in a school, and you have a student named Vasya.
You want to teach Vasya to solve math problems correctly. For this, you provide him with examples of problems (input data) and show the correct solutions (output data).
Vasya learns from these examples and, over time, begins to solve similar problems independently, based on his knowledge.
Transfer Learning: After the model has been pre-trained on a large volume of data, it can be further trained (or «fine-tuned») on specialized data for specific tasks. This allows the model to apply general knowledge to specific scenarios.
Fine-Tuning Models: This is when a language model is adjusted or «tuned» for a specific task.
This is often used after transfer learning so that the model can better handle the unique aspects of a specific task.
For example, if you bought a new piano and you already know how to play classical pieces, but you decide to join a jazz band.
Although you already have basic piano skills, jazz requires a special style and technique. To adapt to this new style, you start taking additional lessons and practice exclusively in jazz.
This process of adapting your skills to a new style is akin to «fine-tuning» in the world of machine learning.
In the same way, if we have a language model trained on a large volume of data, and we want it to solve a specific task (for example, analyzing restaurant reviews), we can «retrain» or «tune» this model on specialized review data so that it performs better in this specific task.
Reinforcement Learning: In this method, the model is «rewarded» or «punished» based on the quality of its responses or actions, encouraging it to improve its results over time.
Imagine a child’s game where a child controls a radio-controlled car, trying to navigate a closed track. Initially, the child may frequently veer off the track or collide with obstacles.
But each time the car successfully completes a lap around the track without errors, the child rejoices and feels satisfaction.
This joyful feeling serves as a «reward.» If the car goes off the track or collides with an obstacle, the child may experience disappointment or frustration – this is «punishment.»
Over time, responding to these rewards and punishments, the child improves their skills in controlling the car and makes fewer mistakes.
In the world of artificial intelligence, this is analogous to how reinforcement learning works.
A model, for example, playing a computer game, receives a «reward» for correct actions and a «punishment» for