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Large Language Models

Large language models (LLMs) like GPT-4 are the result of a significant evolution in the field of artificial intelligence, particularly within the subset of machine learning known as natural language processing (NLP).

AI llms How it works

6 November 2023 by Kevin McAleer | Share this article on

Large language models (LLMs) like GPT-4 are the result of a significant evolution in the field of artificial intelligence, particularly within the subset of machine learning known as natural language processing (NLP).

At their core, these models are designed to understand, generate, and sometimes even translate human language in a way that is coherent, contextually relevant, and often indistinguishable from that written by humans.

Below is an overview of how these models function.

Foundation: Neural Networks

LLMs are built on artificial neural networks, which are computational systems vaguely inspired by the biological neural networks that constitute animal brains. These networks are made up of nodes, or “neurons,” connected by “synapses.” In machine learning, the strength of these connections is adjustable through a process known as “training,” where the neural network is fed large amounts of data.

An example LLM written in Python:

Large Language Model Diagram

Training Large Language Models

Training an LLM like GPT-4 involves inputting vast datasets of text. This text is not just from one domain but from various sources, including books, websites, articles, and more, to give the model a broad understanding of language and context. As the model processes this text, it adjusts the weights of its neural connections through a method called backpropagation, essentially learning which patterns correspond to successful language generation.

Architecture: Transformers

A breakthrough in LLMs came with the development of the transformer architecture. This system allows for attention mechanisms that let the model weigh the importance of different words in a sentence or a paragraph, enabling it to generate or interpret information in a contextually aware manner. Transformers work with what’s known as self-attention, meaning they can assess which parts of the input are most relevant for understanding the rest.

Tokenization and Decoding

When generating text, an LLM breaks down input into tokens, which can be words, parts of words, or even single characters. It then uses the trained model to predict the next token in a sequence, given the tokens that came before. The model generates text by repeatedly predicting the next token until it forms a complete response or reaches a specified limit.


Even after initial training, LLMs can be fine-tuned on more specialized datasets. This helps the model perform better on specific tasks, like legal analysis or medical inquiries, by adjusting the neural network to be more sensitive to the language and information patterns present in those fields.

Inference and Applications

When an LLM is put to use, it’s in the inference stage. It’s given new inputs it has never seen and must generate appropriate outputs based on its training. These applications range from writing assistance, like drafting articles or generating code, to answering questions and even engaging in conversation.

Challenges and Ethical Considerations

Despite their capabilities, LLMs face challenges. They can inadvertently generate biased or incorrect information, struggle with understanding nuances such as sarcasm, and require vast computational resources. Ethical considerations also come into play, with concerns around privacy, the potential for misuse, and the environmental impact of training and running such large models.

In conclusion, large language models are complex systems that emulate understanding and generating human language by leveraging neural networks, vast amounts of data, and advanced architectures like transformers. They are powerful tools with a wide range of applications, but they also present new challenges and responsibilities in the field of AI.


This video contains an explaination and demonstration of how to create a Large Language Model in Python.

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