In recent years,artificial intelligence agentshave become true co-pilots for a variety of activities. Whether at work or performing personal tasks,AIchatbots have become important tools for consultation and task completion. Behind them are LLMs,applicationsinneural networkarchitecture, trained to interact with the user in natural language.

LLMs is the acronym for Large Language Model. The model, in this case, is mathematical: as we explained in the post onartificial neural networks, they are formed by perceptrons, linear classifiers that generate, as an output value, the weighted sum of input values – which have weights that express the importance of each input value. The output value of each perceptron is one of the inputs of all classifiers in the next layer, and so on, until the result layer.

If the model is mathematical, the language is usually a spoken language. LLMs have the ability to process "semantic units" in different languages, understand them in the context of the message we send toartificial intelligence agents, and respond to us in the same language and in an appropriate manner. To understand in detail how all this works, follow along with us in this text!

History

The history ofartificial intelligenceis long, spanning almost a century. However, for most of the world's population,AIbecame part of everyday life at theend of 2022 with the launch of Chat GPT 3.5. It was the first popular LLM, reaching one million users just five days after its launch.

The "turning point" that enabled the transformation ofAIapplicationsintoartificial intelligence agentscapable of interacting with users as if they were human beings was a review of the architecture ofartificial neural networks. It was proposed in the article"Attention is all you need," published by Google researchers in 2017. In the study, they argue that the architecture of recurrentneural networks, or RNNs, was limited in its ability to work with text, as they were slow to process sentences and ineffective at “remembering” longer contexts.

The researchers proposed the self-attention mechanism, a model that calculates the relationship between all words in a sentence simultaneously. Thus, by analyzing the word in the context of a sentence, the system understands its meaning more accurately. To give an example, when reading the word "bank" and associating it with the context of the sentence,artificial intelligencemore easily identifies whether the word refers to a financial institution or a type of seat.

Achieving this result was possible thanks to a change inthe architecture oftheseAIapplications: instead of RNNs, they began to be built asfeedforward neural networkswith the addition of the self-attention mechanism. This change led to what we know asTransformer architecture, a type of FNN subtype. Since the model can “look” at all the words in the sentence at the same time, it understands the context without needing recurrent feedback.

LLMs: how they work and their applications

Throughout 2018, the firstTransformer-typeartificial neural networkswere announced for use:Chat GPT 1andBert, Google'sartificial intelligenceapplication that was integrated into the search engine, greatly improving thequalityof results. With Bert, the search engine began to understand context, such as the placement of prepositions, realizing how they changed the meaning of the query. Search results became much better.

In general terms, LLMs have three essential components: the self-attention mechanism, the encoder/decoder, and the embedder. In practice, it works as follows:

1. The user sends a message to theartificial intelligence agent.
2. It converts the input text into tokens, the "semantic units."
3. The incorporation layer accesses other "semantic units" in a larger space to begin understanding the context.
4. Once context comprehension has begun, the self-attention mechanism simultaneously analyzes the relationships between all "semantic units" to complete context comprehension.
5. Based on this understanding, he encodes the request and formulates the response to the user.
6. Once formulated, it is decoded into natural language to be delivered to the user.

With a more detailed understanding of how LLMs work, it is easy to see why they quickly came to be applied in various natural language processing activities. Right away, they were immediately used in language translation systems, such as Google Translate.

LLMs have become part ofthe application architecture ofvariousAIs. Chatbot-typeartificial intelligence agentscurrently process programming language and even unstructured data such as images and sound. Using LLMs, it is possible to createapplications, videos, music, and even voiceovers with your own voice.

And, just like goodfeedforward neural networks,artificial intelligencesystems that use LLMs can recognize and classify patterns, perform data regression with high accuracy, generate predictions based on data analysis... These are important processes for solving various types of problems that we face in our daily business activities.

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