Large language models, also known as LLMs, are very large deep learning models that are pre-trained on vast amounts of data. The underlying transformer is a set of neural networks that consist of an encoder and a decoder with self-attention capabilities. The encoder and decoder extract meanings from a sequence of text and understand the relationships between words and phrases in it.
Transformer LLMs are capable of unsupervised training, although a more precise explanation is that transformers perform self-learning. It is through this process that transformers learn to understand basic grammar, languages, and knowledge.
Unlike earlier recurrent neural networks (RNN) that sequentially process inputs, transformers process entire sequences in parallel. This allows the data scientists to use GPUs for training transformer-based LLMs, significantly reducing the training time.

Transformer neural network architecture allows the use of very large models, often with hundreds of billions of parameters. Such large-scale models can ingest massive amounts of data, often from the internet, but also from sources such as the Common Crawl, which comprises more than 50 billion web pages, and Wikipedia, which has approximately 57 million pages.

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Why are large language models important?
Large language models are incredibly flexible. One model can perform completely different tasks such as answering questions, summarizing documents, translating languages and completing sentences. LLMs have the potential to disrupt content creation and the way people use search engines and virtual assistants.
While not perfect, LLMs are demonstrating a remarkable ability to make predictions based on a relatively small number of prompts or inputs. LLMs can be used for generative AI (artificial intelligence) to produce content based on input prompts in human language.

LLMs are big, very big. They can consider billions of parameters and have many possible uses. Here are some examples: