{"id":168461,"date":"2026-02-19T21:43:29","date_gmt":"2026-02-19T20:43:29","guid":{"rendered":"https:\/\/liora.io\/en\/?p=168461"},"modified":"2026-02-19T21:43:30","modified_gmt":"2026-02-19T20:43:30","slug":"large-language-models-llm-everything-you-need-to-know","status":"publish","type":"post","link":"https:\/\/liora.io\/en\/large-language-models-llm-everything-you-need-to-know","title":{"rendered":"Large Language Models (LLM) : Everything you need to know"},"content":{"rendered":"

Since the end of 2022 and the massive worldwide use of ChatGPT, artificial intelligence-based language models have been attracting increasing interest, both from the general public and from businesses. So what are large language models? How do they work? What are they used for? What are their advantages? Find out in this article.<\/strong><\/p>\n\n\n\n

What are large language models?<\/h2>\n\n\n\n

Whether it’s to communicate, connect, understand the world, or shape it, language is crucial to our humanity. What characterizes us? Not so much. Despite the complexity of human language<\/b>, machines are now able to understand its subtleties<\/b> thanks to large language models (LLMs). By using as much data as possible in their training, these new technologies have developed a richer understanding<\/b> of language than ever before. <\/p>\n\n\n\n

In concrete terms, this is a transform-based neural network<\/a>. These basic models use generative AI (and more specifically Deep Learning<\/a>) for natural language processing<\/b> (NLP) and natural language generation<\/b> (NLG).<\/p>\n\n\n\n

How do large language models work?<\/h2>
\"Diagram<\/figure>\n\n\n\n

As the aim of large language models is to learn the complexity of human language, they are pre-trained on a large amount of data<\/b> (such as text, images, videos, speech, and structured data…). The more parameters an LLM uses, the better its performance. As such, large linguistic models<\/b> require significant resources in terms of data, computation, and engineering.<\/p>\n\n\n\n

This is particularly true in the pre-training phase. At this stage, large language models<\/b> need to learn basic linguistic tasks and functions. Once the learning model has been pre-trained, it can be trained with specific new data<\/b>. The aim is to refine its capabilities for specific use cases. This is known as fine-tuning. This learning phase requires less data and energy.<\/p>\n\n\n\n

What are LLMs used for?<\/h2>\n\n\n\n

Large language models can be used for a multitude of tasks. For example: <\/p>\n\n\n\n