What are language models and how do LLMs work?

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The language models They have become the heart of modern artificial intelligence: they are behind virtual assistants and chatbotsMachine translation and tools that write code or draft text almost like a person. Although it may seem like magic, they actually combine statistics, neural networks, and enormous amounts of data to predict which word, phrase, or even image makes the most sense next.

In recent years, the following have emerged strongly: LLM or Large Language ModelsThese are gigantic and far more powerful 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, what types exist, what real-world uses they have in companies, and what risks and limitations should be kept in mind.

What exactly is a language model?

Un language model It is, in essence, a statistical or computational system that assigns a probability of token sequencesA token can be a whole word, a subword, or even a single character. The goal of the model is to estimate which token is most likely to appear next in a given sequence.

If we think of a sentence with a gap, the model calculates which possible sequels fit best with 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 among several candidates above a certain threshold to provide variety.

This same mechanism of predict the next token It naturally extends to more complex tasks: full text generation, translation from one language to another, summary creation, question answering, classification, information extraction, etc. 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 with large corpora of text and they 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 the n-gram modelsAn 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 depending on how many times they have seen that trigram in their training corpus. If we have observed many phrases of the type "orange is ripe" and very few of the type "orange is cheerful", the first continuation will have more weight when the context is "orange is".

The problem is that the The available context is very limited.A trigram can only look two words back, which is often 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 that limitation, the following arrived recurrent neural networks (RNN)These methods process text token by token, 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 the capture of longer dependencies than with n-grams and reducing prediction errors in complex sentences.

However, natural resource management (NRM) has its own drawbacks: nature strictly sequential Their processing methods hinder parallelization and make training for long sequences costly and slow. Furthermore, they suffer from the well-known problem of... disappearance of the gradientThis 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 giant leap came with the Transformer architecture, presented in 2017 in the famous article "Attention is all you need". This approach completely abandoned recurrence and relied on a key mechanism: the self-care (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 to each position.

The process starts with the tokenizationin which the text is broken down into tokens (words, subwords, etc.). Each token is mapped to a numerical vector called embeddingwhich gathers semantic and syntactic information. These embeddings pass through multiple layers of the Transformer, and in each of them they are progressively refined, becoming richer contextual representations that incorporate information about the rest of the tokens.

To allow the model to know the position of each token, the following are added: positional encodingsThese indicate the token's position in the sequence and allow self-attention to distinguish between, for example, a word that appears at the beginning and an identical one that appears at the end, which is crucial for grasping the order and structure of sentences.

Self-attention works by projecting each embedding onto three distinct vectors through 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 the Transformer is that this mechanism is applied in a highly parallelizableUnlike 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 unthinkable just a few years ago.

What are LLMs (Large Language Models)?

Based on the Transformers, the following have emerged LLM or Large Language Modelsliterally large language models. 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 predominantly self-supervisedInstead 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 conversation patterns.

A classic LLM is initially trained by 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 stage of supervised learning or RLHF (Reinforcement Learning from Human Feedback)where human annotators evaluate generated responses, mark which are good or bad, and that signal is used to fine-tune the model's behavior.

This combination of massive pre-training and post-training adjustment allows LLMs to perform tasks such as translation, writing, summarizing, dialogue, code generation, or classification with near-human fluency. Tools like ChatGPT, Claude, Gemini, Llama, and many enterprise solutions rely precisely on this type of model to offer conversational assistants, advanced search systems, or 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 modeling statistical patterns and predict the most likely continuation, although the degree of sophistication is such that, for practical purposes, the difference is often difficult to appreciate in everyday life.

LLM training: data, weights, and loss function

LLM training begins with the collection and refinement of a gigantic datasetThis data is normalized, filtered to remove noise, and tokenized. The model weights are then initialized, and a loss function is defined to measure the error between the predictions and the 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 is from the correct sequence. Using algorithms such as gradient descent and backpropagationThe 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 projections of the embeddings, adopt increasingly useful configurations.

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

Once the pre-training is finished, a fine-tuning with more specific datasets to guide the model towards concrete tasks: following instructions, answering questions politely, respecting certain safety criteria, adopting a certain tone, etc. In conversational models such as GPT-4, this phase is usually accompanied by RLHF, where humans and sometimes other models evaluate response proposals and help guide the system towards more useful and safe behaviors.

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

GPT, ChatGPT and their relationship with LLMs

The term GPT The acronym 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 It 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 which 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 LLM 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 and conversation generation, the following stand out: GPT-3/GPT-4 from OpenAI, Claude from Anthropic, the models Palm and Gemini from Google, and the family Calls Meta, which has been a major driver of the open source ecosystem. Many enterprise platforms offer hubs where you can choose from several of these models depending on the use case, cost, latency, and privacy restrictions.

In the field of language comprehension, models like BERT Bidirectional Encoder Representations from Transformers (BERT) marked a turning point. BERT is trained bidirectionally, meaning it learns to predict masked words using both 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 ground multilingual and multimodal We find proposals like BLOOM, CLIP, or 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, or systems that combine visual and textual information; there are even voice and multimodal models such as MAI Voice 1 which show this evolution.

Finally, the following have gained weight: small or efficient LLMsDesigned to run on resource-constrained devices (mobile, edge, etc.) or to reduce inference costs, scaled-down versions of Llama, T5, ALBERT, or other models enable the deployment of generative AI capabilities without requiring large cloud infrastructures.

LLM vs. Traditional NLP

It is common to confuse the concepts LLM and NLPNatural Language Processing (NLP) 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 ad hoc trained: statistical algorithms, rule-based systems, n-gram models, LSTM networks, word2vec, etc.

LLMs represent a evolution of NLP traditional. 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 without additional training or with very little tuning (known as zero-shot and few-shot learning).

The key difference lies in the scale and approachWhile 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 huge variety of applications and productsIn the field of virtual assistants, they promote tools such as Siri, Google Assistant, Alexa, or web chatbots that understand requests in natural language and return relevant responses, execute commands, or perform actions such as sending messages and scheduling appointments.

In machine translation, advanced models allow to translate texts more accurately and naturally than classic rule-based systems. Platforms like Google Translate or DeepL have clearly improved their quality thanks to Transformer-type architectures trained with massive multilingual data.

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

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

There are also LLMs specialized by domainExamples include 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 doctors, lawyers, or analysts in reading and synthesizing large volumes of information.

Advantages, weaknesses, and ethical challenges

Large language models offer clear benefits: automate monotonous tasksThey 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 carry a series of major limitationsThe most well-known are "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 the biasLLMs 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.

El computational cost The 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 They demand transparency, risk assessment, and human oversight, especially in systems that interact with consumers or make decisions with significant impact. Added to this is the risk of vendor lock-in, something 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 stagesFirst, the purpose is defined: are you looking for 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.

Then the following is addressed pre-workoutThis involves collecting and standardizing a massive and diverse dataset. The text is then 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, adjustments are made hyperparameters such as the learning rate, batch size, number of steps, regularization strategies, and learning scheduling schemes. Once this stage is complete, fine-tuning begins, where the model is iteratively refined with 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 LLMs already pre-trained provided by large organizations or the open source community. They apply techniques such as light fine-tuning, prompt engineering, RAG, or distillation to adapt them to their context, reduce costs, and improve production efficiency.

Within this broader ecosystem, LLMs are considered foundational modelsLarge, 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 whole scenario means that language models have gone from being a laboratory curiosity to becoming a basic infrastructure of the digital economy: they are already transforming 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 harnessing their advantages while remaining aware of their risks and limitations.