The large language modelsLarge Language Models (LLM) are an advanced class of artificial intelligence that allows the understanding, interpretation and generation of texts in natural language.
Although these models may seem like a very recent phenomenon, in reality they are not, because their development dates back several years, when the architectures of neural networks and transformers began to be perfected, such as the GPT (Generative Pretrained Transformer) models, GPT- 3 and GPT-4 from OpenAI.
Companies such as Google, Meta and IBM have also played an important role in the evolution of LLMs, mainly boosting their ability to process large volumes of text and perform different tasks such as content generation, language translation, coding assistance, attention to the client, among others.
A notable characteristic of LLMs is that they not only have the ability to recognize words, but they can also interpret the contextual relationships between them. This advantage makes them extremely useful for essential sectors of society such as health, education and commerce.
How Great Language Models Work
LLMs are based on transformers that are responsible for training the models with an enormous amount of volumes of textual data and with a deep learning technique. They work this way:
-Initial training. LLMs are trained on a massive corpus of textual data (books, articles, Internet content…). The model learns to predict the next word in a sequence of texts, but focusing mainly on the context of the previous words.
-Tokenization. Once the model learns to predict the words, the text is fragmented into smaller units that are called tokens. They can be complete words, parts of words or even characters.
In this sense, the model is responsible for assigning each of these tokens a numerical value and represents its meaning within the context of the text.
-Numerical representation of the tokens. In this step, the tokens become embeddings, or in other words, numerical representations that reflect the contextual relationships between them.
These representations help the model to actually “understand” the meaning that words have in relation to others found within the same sentence or paragraph.
-An attention mechanism is generated. At this point, an attention mechanism is generated that allows the model to focus on the most relevant parts of that text.
Above all, the context necessary to generate a correct prediction is prioritized and in this way longer sequences of text are handled without losing valuable information.
-Prediction and generation of content. Once the embeddings and context are finished, the model can now predict the next word or phrase in the sequence.
It must be taken into account that this generation process is executed autonomously. Furthermore, as a result of the large number of parameters adjusted during training, the LLM has the ability to generate very coherent and useful content.
-Precision fit. While it is true that large language models are pre-trained with enormous volumes of data, they can still be tuned so that they can perform very particular tasks.
This is called fine tuning and involves training the model using a smaller, more specific set of data.
In this way, it is possible to improve your performance in very specific areas, which may be writing emails or even translating texts that are too technical.
