LLM AI: The Revolution is Underway

Chat GPT, Gemini, Copilot, Llama, ... It is impossible to have missed these large language models (LLM). Trained on enormous volumes of data, these AI LLMs are capable of understanding human language with astonishing efficiency. Able to generate code, text, images, and even videos, these new tools are taking on an increasingly important role in our modern societies. Let's decode their functioning, interests, and advantages.

What is an LLM in AI?

LLM stands for Large Language Model. These models represent a major advancement in the field of artificial intelligence, in the sense that they are capable of learning autonomously.

To achieve this, AI LLMs rely on a set of neural networks that have been trained on a vast amount of data (up to several hundred billion parameters).

This prior training allows them to understand human language and all its subtleties (such as relationships between words, colloquial expressions, and the transmission of emotions). Thus, when a user sends a request, they are able to respond with astonishing precision.

It should be noted that they are only trained to perform specific tasks, such as automatic translation or text generation. Unlike general AI, the same tool cannot write a poem and play chess.

How do large language models (LLM) work?

AI LLMs are based on machine learning, and more specifically deep learning. This uses enormous amounts of data to learn autonomously, without human intervention.

For example, by feeding the artificial intelligence with texts in French and Spanish, the large language model will be able to differentiate between them. But only after analyzing thousands of texts. It will use probabilities to identify the words that appear in Spanish texts and those that appear in French texts. As the number of examples increases, its ability to distinguish improves.

To learn in this way, the large language model is built from a neural network called a transformer. Like the human brain, this network is made up of neurons (or nodes) connected to each other. The particularity of AI LLMs lies in the functioning of transformers. They are capable of processing large sets of data in parallel (and no longer sequentially). This transformer is also equipped with a self-attention mechanism to detect subtle connections between elements in a sequence.

All of this allows them to optimize the learning process and assimilate all linguistic nuances. They will thus understand human language, even when it is ambiguous, poorly defined, organized, or contextualized in new ways.

Good to know: while the volumes of data used for AI LLMs allow them to produce impressive results, the quality of the data provided is also of great importance.

What are large language models used for?

Due to their ability to understand all the subtleties of human language, LLMs can perform a multitude of tasks related to generative AI. Here are some examples:

‍Writing sales emails: large language models can assist sales teams in writing sales emails. However, be sure to add a personal touch to avoid sending a 100% AI-generated email.

Answering questions: similar to the internet, it is possible to use LLM models to get answers to questions. But since the search mode here is more conversational, you can refine your query to improve the quality of the response.

Generating code: AI LLMs understand all types of languages, including programming languages such as Python, JavaScript, and Ruby, among others.

Beyond generative AI, LLMs can also perform other tasks related to human language. For example, they can distinguish comments with a negative or positive connotation, which facilitates customer segmentation in data marketing.

In this sense, large language models can act as true virtual assistants.

What are the advantages and limitations of AI LLMs?

Due to their extremely advanced linguistic capabilities, large language models can assist humans in many daily tasks.

In a professional context, they enhance team productivity, regardless of the industry: marketing, sales, human resources, finance, ...

However, while these tools are very competent virtual assistants, it is important not to rely 100% on these models.

On the one hand, because the training data will condition their performance. And even if the quality of the data is generally optimal, the provided sets often contain biases. These language models tend to reproduce these biases very easily.

And worse still, when they are unable to provide a precise answer, AI LLMs can provide false information.

On the other hand, because they lack humanity. For example, if you use them to write all your sales emails, blog articles, and social media posts, it is very likely that you are not the only one. Your competitors can do the same. The result: you will have communication entirely based on AI, which may not necessarily allow you to stand out. It is important to always maintain a personal touch.

These tools are very useful as long as they are used as assistants, not replacements.

What are the best large language models?

The most famous AI LLM is undoubtedly ChatGPT. Launched at the end of 2022 by OpenAI, it paved the way for the democratization of large language models.

Since then, startups and major tech companies have been competing to provide the best large language model. For example, we can mention:

Gemini by Google

Llama by Meta

Copilot by Microsoft

Mistral AI, a French startup.

So, what is the best AI tool?

At present, ChatGPT and its various versions maintain a significant lead over its competitors. But the race for AI is still in its early stages.

At École Cube, we offer AI training, specifically on ChatGPT. The AI revolution is underway!