What are language models and how do LLMs work?

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Language models have become the heart of modern artificial intelligence: they power virtual assistants and chatbots , machine translation, and tools that write code or draft text almost like a person. While it may seem like magic, they actually combine statistics, neural networks, and massive amounts of data to predict which word, phrase, or even image makes the most sense next.

In recent years, Large Language Models (LLMs ) have emerged as powerful, gigantic versions of classic language models. These systems not only generate fluent text but also summarize documents, answer complex questions, translate between languages, and even reason at a certain level. Let's take a closer look at what they are, how they work internally, the different types available, their real-world applications in businesses, and the risks and limitations that should be kept in mind.

What exactly is a language model?

A language model is essentially a statistical or computational system that assigns probabilities to sequences of tokens . A token can be a complete word, a subword, or even a single character. The model's goal is to estimate which token is most likely to appear next in a given sequence.

If we consider a sentence with a gap, the model calculates which possible continuations best fit the context. For example, given the sentence "When I hear rain on my roof, I _______ in my kitchen," the system weighs alternatives such as "cook soup," "heat a kettle," or "take a nap," assigning each a different probability. An application can choose the option with the highest probability or sample from several candidates above a certain threshold to provide variety.

This same mechanism of predicting the next token naturally extends to more complex tasks: full text generation, translation from one language to another, summarization, question answering, classification, information extraction, and so on. By modeling statistical language patterns, the system ends up developing very rich internal representations that capture grammar, style, and relationships between concepts.

To achieve this, language models are trained on large corpora of text and learn to adjust their internal parameters to bring their predictions closer to real-world examples. The number of these parameters (weights) is what we usually refer to when we talk about models with millions, billions, or even trillions of parameters.

Context: from n-grams to neural networks

For a long time, the most common approach to building language models was n-gram models . An n-gram is an ordered sequence of N words: when N=2 we call them bigrams; when N=3, trigrams; and so on. For example, starting with the phrase "you are very nice," the bigrams would be "you are," "are very," and "very nice."

Using a trigram model, given a two-word context, the system calculates the probability of each possible third word based on how many times it has seen that trigram in its training corpus. If we have observed many phrases like "orange is ripe" and very few like "orange is cheerful," the first continuation will carry more weight when the context is "orange is."

The problem is that the available context is very limited . A trigram can only look two words back, which is usually insufficient to resolve ambiguities (for example, whether "orange" is a fruit or a color) or to capture long-range dependencies. Increasing N provides more context, but also exacerbates the scarcity of data: 6-grams or 7-grams appear so rarely that it is difficult to estimate reliable probabilities.

To overcome this limitation, recurrent neural networks (RNNs) emerged , which process text token by token while maintaining an internal state that acts as a memory of the previous context. Variants such as LSTM or GRU improved the ability to retain information for longer periods, allowing for the capture of longer dependencies than with n-grams and reducing prediction errors in complex sentences.

However, RNNs have their own drawbacks: the strictly sequential nature of their processing makes parallelization difficult and training for long sequences expensive and slow. Furthermore, they suffer from the well-known gradient vanishing problem , which limits the amount of useful context they can handle in practice. This combination of bottlenecks motivated the search for new, more efficient architectures.

The Transformer revolution and the self-care mechanism

The real leap forward came with the Transformer architecture , presented in 2017 in the famous article "Attention is all you need." This approach completely abandoned recursion and relied on a key mechanism: self- attention , which allows the model to simultaneously "look" at all the tokens in a sequence and weigh which parts of the context are most relevant for each position.

The process begins with tokenization , in which the text is broken down into tokens (words, subwords, etc.). Each token is mapped to a numerical vector called an embedding , which contains semantic and syntactic information. These embeddings pass through multiple layers of the Transformer, and in each layer they are progressively refined, becoming richer contextual representations that incorporate information about the other tokens.

To enable the model to know the position of each token, positional encodings are added . These indicate the token's place in the sequence and allow self-attention to distinguish between, for example, a word that appears at the beginning and an identical word that appears at the end—something crucial for grasping the order and structure of sentences.

Self-attention works by projecting each embedding onto three distinct vectors using learned weight matrices : queries (Q), keys (K), and values ​​(V). The query represents what a token "looks for" in the rest of the sequence, the key reflects the information that each token "offers," and the value is the information that will be propagated, weighted by attention.

The model then calculates alignment scores, such as the similarity between each query and all the keys. After normalizing these scores (for example, with softmax), it obtains attention weights that determine how much each token's value contributes to the new representation of the current token. In this way, the network flexibly focuses on the relevant context and leaves less useful tokens (such as certain function words or irrelevant terms in a given passage) in the background.

One of the great advantages of Transformer is that this mechanism is highly parallelizable . Unlike RNNs, where tokens are processed one by one, here all positions in the sequence are processed simultaneously, which greatly accelerates training on modern hardware. This combination of more context, better ability to capture long dependencies, and computational efficiency has allowed models to scale to sizes unimaginable just a few years ago.

What are LLMs (Large Language Models)?

Building upon Transformers , Large Language Models (LLMs) have emerged . These are deep neural networks with millions, billions, or even trillions of parameters, trained on massive amounts of text from books, articles, websites, technical documentation, and other public (and sometimes private) resources.

These models use deep learning and are trained in a predominantly self-supervised manner : instead of relying on manually labeled data, they learn from unannotated text, solving internal tasks such as predicting the next word or filling in gaps in a sentence. From there, they implicitly acquire knowledge about grammar, languages, world facts, writing styles, reasoning processes, and conversational patterns.

A classic LLM (Language Learning Model) is initially trained using unsupervised learning to predict the next word given a context. In some cases, a similar second phase is performed, expanding the data or adjusting the training objective to better capture the context. This is usually followed by a supervised learning or RLHF (Reinforcement Learning from Human Feedback) stage , where human annotators evaluate the generated responses, marking them as correct or incorrect, and this feedback is used to refine the model's behavior.

This combination of massive pre-training and subsequent fine-tuning allows LLMs to perform tasks such as translation, writing, summarizing, dialogue, code generation, and classification with near-human fluency. Tools like ChatGPT, Claude, Gemini, Llama, and many enterprise solutions rely precisely on these types of models to offer conversational assistants, advanced search systems, and autonomous agents that interact with corporate data.

It's worth emphasizing that, despite their apparent intelligence, an LLM doesn't "understand" language like a person. What they do is model statistical patterns and predict the most likely continuation, although the level of sophistication is such that, in practical terms, the difference is often difficult to discern in everyday situations.

LLM training: data, weights, and loss function

Training a Large Model Line (LLL) begins with collecting and cleaning a massive dataset . This data is normalized, filtered to remove noise, and tokenized. Next, the model weights are initialized, and a loss function is defined to measure the error between predictions and actual training sequences.

Over millions or even billions of training steps, the model makes token-by-token predictions , and the loss function quantifies how far it deviates from the correct sequence. Using algorithms like gradient descent and backpropagation , the weights are adjusted layer by layer in each iteration to reduce this error. In this way, the matrices that generate the self-service queries, keys, and values, as well as the embedding projections, adopt increasingly useful configurations.

In this process, the model learns semantic associations: tokens like "dog" and "bark" end up close together in the vector space when the context refers to pets, while "bark" and "tree" appear less related. This embedding space captures similarities in meaning, analogies, and relationships between concepts, which are then exploited in subsequent tasks.

Once pre-training is complete, fine-tuning is usually performed using more specific datasets to guide the model toward concrete tasks: following instructions, answering questions politely, respecting certain safety criteria, adopting a specific tone, etc. In conversational models like GPT-4, this phase is often accompanied by RLHF, where humans and sometimes other models evaluate proposed responses and help guide the system toward more useful and safer behaviors.

The end result is a model that has internalized grammatical patterns, factual knowledge, reasoning structures, and styles distributed across its parameters. When it receives new input, it can generate coherent, context-sensitive, and, in many cases, creative outputs.

GPT, ChatGPT and their relationship with LLMs

The term GPT stands for "Generative Pre-trained Transformer." It refers to a specific family of LLMs developed by OpenAI that is directly based on the Transformer architecture. "Generative" indicates its ability to produce new content, "Pre-trained" refers to the fact that it is trained on large corpora before being adapted to specific tasks, and "Transformer" denotes the underlying architecture.

ChatGPT is, in reality, a chat application built on GPT models (such as GPT-4 and its variants). The LLM acts as the "brain" that generates the responses, while the ChatGPT interface is the layer that allows users to easily converse with that model. Without an underlying language model, ChatGPT would be nothing more than an empty text box with no generation capabilities.

The difference between GPT and LLM can be understood as follows: LLM is the general category that encompasses any large language model; GPT is a specific family within that category. Other examples of LLMs that do not belong to GPT are Claude (Anthropic), Gemini (Google), Llama (Meta), Mistral, or open models like Bloom.

Types of language models and prominent families

Within the current ecosystem, there are multiple types of LLMs and language models, each with distinct objectives and characteristics. Some are designed for general-purpose tasks, others for deep context understanding, some for code generation, and others for highly specialized domains.

Among the general-purpose models geared towards text generation and conversation, OpenAI's GPT-3/GPT-4 , Anthropic's Claude , Google's PaLM and Gemini models , and Meta's Llama family stand out, the latter having been a major driver of the open-source ecosystem. Many enterprise platforms offer hubs where users can choose from several of these models depending on their use case, cost, latency, and privacy constraints.

In the field of language understanding , models like BERT (Bidirectional Encoder Representations from Transformers) marked a turning point. BERT is trained bidirectionally, meaning it learns to predict masked words using both the preceding and following context, allowing it to better capture nuances and complex relationships within a sentence. Variants such as DistilBERT, RoBERTa, ALBERT, and XLM-R optimize performance, size, or multilingual support.

For code generation, there are models like Codex (the basis of GitHub Copilot) or AlphaCode, specifically trained on programming repositories and algorithmic problems. These systems are capable of suggesting functions, completing code blocks, or even solving complex exercises from natural language descriptions.

In the multilingual and multimodal field , we find proposals such as BLOOM, CLIP, and modern GPT systems, capable of working with text, images, audio, and even video. The clear trend is toward models that integrate several modalities simultaneously, opening the door to applications such as video analysis with textual description, assistants that understand diagrams, and systems that combine visual and textual information; there are even voice and multimodal models like MAI Voice 1 that demonstrate this evolution.

Finally, small or efficient LLMs have gained traction , designed to run on devices with limited resources (mobile, edge, etc.) or to reduce inference costs. Reduced versions of Llama, T5, ALBERT, or other models allow the deployment of generative AI capabilities without the need for large cloud infrastructures.

LLM vs. Traditional NLP

It is common to confuse the concepts of LLM and NLP . NLP (Natural Language Processing) is the broad field that encompasses all techniques for the automatic processing of language: sentiment analysis, entity extraction, topic detection, translation, summarization, etc. Historically, each of these tasks was solved with specific models trained ad hoc: statistical algorithms, rule-based systems, n-gram models, LSTM networks, word2vec, etc.

Large-scale modeling (LLM) represents an evolution of traditional NLP . Instead of training a different model for each task, a single large, general-purpose model can perform translation, summarization, classification, text generation, basic reasoning, and many other operations with little or no additional training (known as zero-shot and few-shot learning).

The key difference lies in scale and approach : while classical NLP models were trained on relatively small, labeled datasets, LLMs learn from trillions of unlabeled tokens, capturing much richer patterns. This doesn't mean NLP has become obsolete; rather, LLMs have become foundational models upon which specific NLP solutions are built in real-world contexts.

Practical applications of language models

Today, LLMs are the backbone of a vast array of applications and products . In the realm of virtual assistants, they power tools like Siri, Google Assistant, Alexa, and web chatbots that understand natural language requests and provide relevant responses, execute commands, or perform actions such as sending messages and scheduling appointments.

In machine translation, advanced models allow for more accurate and natural text translation than traditional rule-based systems. Platforms like Google Translate and DeepL have significantly improved their quality thanks to Transformer-type architectures trained on massive multilingual datasets.

In terms of productivity, language models are integrated into grammar and style checkers , autocomplete features in mobile devices and word processors, search suggestions in browsers and forms, as well as content generation systems for social media, blogs, and advertising campaigns. If you want to learn how to use artificial intelligence in your documents , there are practical guides that show you how to apply these features in modern editors.

In the business world, LLMs are used to automate customer service through chatbots capable of resolving common questions, creating executive summaries of internal documents, assisting in report writing, generating code for development teams, or helping with repetitive administrative tasks. Techniques like RAG (Retrieval-Augmented Generation) allow the model to be connected to internal knowledge bases so that responses are based on verified and up-to-date information.

There are also domain-specific LLMs , such as BioBERT for biomedical research, FinBERT for financial texts, and LegalBERT for legal documents. These models are refined on specific corpora to improve accuracy in their field and support clinicians, lawyers, and analysts in reading and synthesizing large volumes of information.

Advantages, weaknesses, and ethical challenges

Large language models offer clear benefits: they automate monotonous tasks , increase productivity, enable the creation of more natural conversational assistants, streamline translations, accelerate programming, and facilitate access to complex information. They are a disruptive force similar to robotization in industry, but applied to knowledge work.

However, they do have several significant limitations . The most well-known is "hallucinations": the model can generate responses that sound very convincing but are false or inaccurate. Because it learns from statistical correlations and not from a deep understanding of the world, it can invent quotes, data, or references that never existed.

Another key challenge is bias . LLMs inherit cultural biases, stereotypes, or discriminatory patterns from training data, which can lead to problematic responses if not filtered and corrected. Furthermore, they raise privacy and regulatory compliance issues when used with sensitive data, especially if deployed via external APIs rather than proprietary infrastructure.

The computational cost of training and running gigantic models is very high, both in economic and energy terms. This generates debates about sustainability and the concentration of technological power in a few companies with the capacity to train next-generation models.

In Europe and other regions, regulatory frameworks such as the AI ​​Act require transparency, risk assessment, and human oversight, especially for systems that interact with consumers or make decisions with significant impact. Added to this is the risk of vendor lock-in, which many companies are trying to mitigate by exploring open models and hybrid strategies.

How LLMs are designed and adjusted in practice

From an engineering perspective, creating and running an LLM involves following a series of key steps . First, the purpose is defined: Is the goal a general-purpose model, a technical support assistant, a system for legal analysis, or an AI for marketing and sales? This decision guides what data is selected and how performance will be evaluated.

Next comes pre-training , which involves collecting and standardizing a massive and diverse dataset. Then, the text is tokenized, and the architecture is defined (number of layers, size of embeddings, number of attention heads, etc.). The choice of infrastructure is critical: high-performance servers with many GPUs or TPUs, or cloud clusters capable of handling enormous workloads, are needed.

During training, hyperparameters such as learning rate, batch size, number of steps, regularization strategies, and learning schedules are adjusted. Once this stage is complete, fine-tuning begins, where the model is iteratively refined using specific data, quality metrics, and, in many cases, human evaluation.

In real-world use, many professionals don't train models from scratch, but instead rely on pre-trained LLMs provided by large organizations or the open-source community. They then apply techniques such as light fine-tuning, prompt engineering, RAG, or distillation to adapt these models to their specific context, reduce costs, and improve production efficiency.

Within this broader ecosystem, LLMs are considered foundational models : large, general networks upon which vertical solutions are built. Their adaptability, coupled with the rapid advancement of multimodal and more efficient versions, points to a future in which increasingly accessible tools will allow companies and users to leverage generative AI on a daily basis.

This entire scenario has transformed language models from a laboratory curiosity into a fundamental infrastructure of the digital economy: they are already reshaping customer service, marketing, software development, research, and the way we interact with technology. Understanding how they work, what they can do, and where they fall short is key to leveraging their advantages while remaining aware of their risks and limitations.