What are language models?


Language models, key to text processing, have revolutionized the way machines understand and generate text. In this report and video you will learn what they are and their great benefits.

In the current era of artificial intelligence, language models have become a hot topic. These represent a major innovation in the field of AI and are being widely used in various applications and technologies.

They have become fundamental in the development of speech and text processing systems, as well as in human-machine interaction. Its ability to understand and generate human-like language has transformed the way people communicate and use technology in their daily lives.

These models have experienced impressive growth in terms of performance and applicability . Its use has expanded to fields such as automatic translation, voice recognition or the generation of text and images.

However, along with its growth and applications, challenges and ethical considerations around language models also arise. The need to address bias in language, data privacy, and the social implications of these technologies is becoming increasingly important. 

What is a language model?

A language model is a type of statistical or computational model that is used to understand and generate natural language text. This uses machine learning to perform a probability distribution on the words used to predict the next most likely word in a sentence based on previous input.

To understand it better, a language model is like an intelligent “machine” that learns to understand and generate text as we humans do. You can think of it as a virtual assistant or an app on your mobile that can speak and write naturally. 

This model is trained using large amounts of text to learn how words work in conversations, story creation, and many other cases. 

For example, imagine that you are writing a text message on your mobile and you start typing “Hi, how do you…?”. Your mobile, thanks to the language model, can predict that you probably want to write “Hello, how are you?”. This is because the model has analyzed millions of conversations and has learned which words tend to follow others.

The language model can also generate text autonomously. For example, if you give it a catchphrase like “Once upon a time in a land far, far away”, the model could generate an entire story based on that catchphrase. This is possible because he has learned the structures and patterns of language from the texts with which he was trained.

Two main approaches when it comes to language modeling

There are several types of language models, but broadly, they can be classified into 2 main categories: rule-based language models and statistical or machine learning language models.

Rule-based language models

These models use predefined grammar rules and structures to understand and generate text. They are based on a set of grammatical and semantic rules designed manually by linguists and language experts.

These rules define the syntax and semantics of the language in question. Rule-based language models are more common in older natural language processing systems and are limited by the complexity of defining all the rules necessary for a complete language.

The problem with this type of model is that it can be a very laborious and complex process. In addition, they can have difficulties in handling ambiguity and variations in the language, since they cannot automatically adapt to new situations or learn from data not included in the predefined rules.

Probabilistic or machine learning language models (‘Machine Learning’)

These models are based on the statistical analysis of large amounts of text to learn the properties and patterns of language. They can use different approaches, such as n-gram models, recurrent neural networks (RNN), and transformer models, among others. 

N-gram models for better understanding is a type of language model that helps predict which words are most likely to appear after others in a text. Imagine that you are reading a sentence and you want to guess the next word. An n-gram model helps you make that prediction.

The main idea behind it is that the probability of a word appearing in a given context depends on the words that precede it . For example, if the last 2 words in a sentence are “I’m happy”, the next word is more likely to be something like “now” or “because” rather than “dog” or “sun”.

Recurrent Neural Networks (RNN) are an improvement on this topic. Since RNNs can be either a short-term memory (LSTM) cell-based network or a closed recursive unit (GRU), they take all previous words into account when choosing the next word.

The main drawback of RNN-based architectures stems from its sequential nature and short-term memory. Consequently, training times skyrocket for long sequences because there is no possibility of parallelization. The solution to this problem is the transformer architecture .

Thanks to these, language models have been improved by allowing them to better understand the relationships between words in a text, no matter how far apart they are. They can also learn automatically and adapt to different tasks and situations, which has led to an improvement in the accuracy and quality of the generated text.

Involved in a wide variety of applications and technologies

1. Virtual assistants: they are fundamental in virtual assistants such as Siri, Google Assistant or Amazon Alexa. They use language models to understand and respond to voice commands, perform searches, provide information, and perform tasks such as sending messages or making calls.

2. Machine translation: they are essential for machine translation systems. They help translate text from one language to another more accurately and naturally. A more than well-known example is Google Translate or DeepL.

Proofreading: Used in proofreading tools, such as spelling and grammar checkers. These tools use the model to identify and correct common writing errors, improving the quality and accuracy of the text.

4. Text generation: they can generate coherent and relevant text in different contexts such as the clear example of ChatGPT, Bard or Bing Chat. These are also used in applications for the generation of automatic video subtitles or the creation of content for social networks.

5. Text autocompletion: they are used in functions such as word suggestions on smartphone keyboards or Google search recommendations, for example. They help predict and suggest words and phrases as you type.

language models evolution
language models evolution

4 language models you need to know


OpenAI’s latest release, GPT-4, is the most powerful and impressive AI model yet from the company behind ChatGPT and DALL-E. 

Already available to some ChatGPT users, GPT-4 has been trained on a massive cloud supercomputing network linking thousands of GPUs, custom designed and built in conjunction with Microsoft Azure.

The company has unveiled the powers of the language model on its blog saying that it is more creative and collaborative than ever. While ChatGPT powered by GPT-3.5 only accepted text input, GPT-4 can also use images to generate captions and analysis.

The most notable change is that it is multimodal, allowing it to understand more than one modality of information. ChatGPT’s GPT-3 and GPT-3.5 were limited to text input and output, meaning they could only read and write. However, GPT-4 may receive images and ask you to understand this information.

Regarding the differences with the previous models, one that must be emphasized from the beginning is based on the concept of “More power on a smaller scale.” OpenAI, as usual, is very cautious when offering all the information and parameters used to train GPT-4 in this case.


Google’s LaMDA (Language Model for Dialogue Applications) model is so accurate that it reportedly convinced an artificial intelligence engineer that it had feelings.

When not scaring workers away, the model can generate free-form conversational dialogue, compared to the task-based responses that traditional models typically generate.

This is because LaMDA was trained in dialogue. According to Google, their approach allowed the model to capture the nuances that distinguish open conversation from other forms of language.

First unveiled at the company’s I/O event in May 2021, Google plans to use the model across all of its products, including its search engine, the Google Assistant, and the Workspace platform.

And at its 2022 I/O event, the company announced expansions to the model’s capabilities via LaMDA 2. The latest version is reportedly more fine-tuned than the original and can now generate recommendations based on user queries.. LaMDA2 was trained on Google’s Pathways Language Model (PaLM), which has 540 billion parameters.


BERT (Bidirectional Encoder Representations from Transformers) is a language model based on the transformer architecture that has revolutionized natural language processing. It was introduced by Google in 2018 and stands out for its ability to capture the context and meaning of words in a deep way.

Unlike traditional language models that are trained in a one-way fashion, BERT uses a two-way approach. This means that it has the ability to see both words before and after the word in question during training. 

This bidirectionality helps BERT understand the context of a word and better capture the complex relationships between words in a sentence.

BERT is trained on large amounts of unlabeled text in a process called “pretraining”. During this the model learns to predict hidden words in a sentence using the context given by the surrounding words. This hidden word forecasting task is known as Masked Language Modeling (MLM).


LLaMA (Large Language Model Meta AI) is a model from the company Meta introduced on February 25, 2023 that is based on the transformer architecture. Like other prominent language models, LLaMA works by taking a sequence of words as input and predicting the next word, recursively generating text.

What sets LLaMA apart is its training in a wide range of publicly available text data spanning numerous languages ​​and its models are available in various sizes: 7B, 13B, 33B and 65B parameters, and you can access them on Hugging Face.

As you have seen, language models are key tools in natural language processing, allowing to understand and generate text effectively, and their relevance lies in their ability to transform the way we interact with language, facilitating communication. and opening up new possibilities in the digital realm.

Leave a Reply