{"id":2951,"date":"2022-10-06T18:49:29","date_gmt":"2022-10-06T18:49:29","guid":{"rendered":"https:\/\/autogenai.dsstaging2.com\/apac\/blog\/what-is-a-large-language-model\/"},"modified":"2025-06-11T14:59:14","modified_gmt":"2025-06-11T14:59:14","slug":"what-is-a-large-language-model","status":"publish","type":"post","link":"https:\/\/autogenai.com\/apac\/blog\/what-is-a-large-language-model\/","title":{"rendered":"What Is a Large Language Model?"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/autogenai.com\/apac\/blog\/what-is-a-large-language-model\/#Large_Language_Models_LLMs\" >Large Language Models (LLMs)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/autogenai.com\/apac\/blog\/what-is-a-large-language-model\/#Whats_a_Parameter\" >What&#8217;s a Parameter?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/autogenai.com\/apac\/blog\/what-is-a-large-language-model\/#Large_Language_Models_LLMs-2\" >Large Language Models (LLMs)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/autogenai.com\/apac\/blog\/what-is-a-large-language-model\/#Whats_a_Parameter-2\" >What&#8217;s a Parameter?<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Large_Language_Models_LLMs\"><\/span>Large Language Models (LLMs)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Cutting-edge LLMs are pre-trained on nearly the entire corpus of digitised human knowledge. This includes\u00a0<a href=\"https:\/\/commoncrawl.org\/\">Common Crawl<\/a>, Wikipedia, digital books and other internet content.<\/p>\n<p>The table below summarises what modern LLMs have \u2018read\u2019\u2026<\/p>\n<table>\n<thead>\n<tr>\n<th>Data Source<\/th>\n<th>Number of words from that data source<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Common Crawl<\/td>\n<td>580,000,000,000<\/td>\n<\/tr>\n<tr>\n<td>Books<\/td>\n<td>26,000,000,000<\/td>\n<\/tr>\n<tr>\n<td>Wikipedia<\/td>\n<td>94,000,000,000<\/td>\n<\/tr>\n<tr>\n<td>Other web text<\/td>\n<td>26,000,000,000<\/td>\n<\/tr>\n<tr>\n<td>TOTAL<\/td>\n<td>726,000,000,000<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>LLMs have \u2018read\u2019 over 700,000,000,000 words. A human reading 1 word every second would take 23,000 years to achieve the same feat.<\/p>\n<h3><b>The Sophistication of LLMs<\/b><\/h3>\n<p>LLMs are now capable of generating text that is sophisticated enough to complete scientific papers. They can output computer code that is superior to many expert developers. Elon Musk has described LLMs as \u201cthe most important advance in Artificial Intelligence\u201d.<\/p>\n<figure style=\"width: 1024px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/autogenai.com\/apac\/wp-content\/uploads\/sites\/5\/2025\/02\/UyJrSUwWRfyLrbHtmJR8.webp\" alt=\"\" width=\"1024\" height=\"370\" \/><figcaption class=\"wp-caption-text\"><strong>Large Large Model timeline and growth<\/strong><br \/>The first language model was in 2018 and they have been exponentially increasing in size &#8211; measured by the number of parameters they contain &#8211; ever since.<\/figcaption><\/figure>\n<h3><b>The Growth of Models<\/b><\/h3>\n<p>The models themselves have been exponentially increasing in size measured by the number of parameters they contain. This means that their parameter growth is continually getting faster. Parameters are numbers the model learns through training.<\/p>\n<h3><b>Parameter Scales<\/b><\/h3>\n<p>Imagine a range from -2,147,483,648 to 2,147,483,647; that\u2019s the scale of their parameters. It\u00a0 helps provide them with impressive computing power.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Whats_a_Parameter\"><\/span><b>What&#8217;s a Parameter?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>A parameter is an adjustable value or characteristic that can influence the outcome of a system or process. Set or adjusted to measure behaviour, performance and outcomes to change the process or function of a system. Parameters are crucial in various fields, such as mathematics, science, engineering and programming.<\/p>\n<h3><b>How are Parameters Used in Large Language Modelling?<\/b><\/h3>\n<p>Parameters play a crucial role in large language modelling. Designed to understand and generate human-like text by learning patterns. These also learn relationships, and context from vast amounts of text data. Here&#8217;s how parameters work in large language models:<\/p>\n<h3><b>Model Architecture Parameters:\u00a0<\/b><\/h3>\n<p>The architecture of a large language model, such as GPT. Defined by parameters that determine the structure of the neural network.<\/p>\n<h3><b>Weights and Biases<\/b><\/h3>\n<p>Large language models consist of a vast number of weights and biases that learn from text data during the training process.<\/p>\n<h3><b>Embedding Parameters<\/b><\/h3>\n<p>Embeddings represent words or tokens in a continuous vector space.<\/p>\n<p>Parameters in large language models determine the model&#8217;s architecture. Learning representations, its behaviour during training and generation, and its ability to understand and generate natural language text.<\/p>\n<h3><strong>Large Large Model timeline and growth<\/strong><\/h3>\n<p>The first language model was in 2018 and they have been exponentially increasing in size. &#8211; Measured by the number of parameters they contain &#8211; ever since.<\/p>\n<h3><b>Gordon Moore<\/b><\/h3>\n<p>In 1965, American engineer Gordon Moore famously predicted that the number of transistors per silicon chip would double every year. Transistors are the electronic switches within a silicon chip. The silicon chip is the building block of all modern electronic devices.<\/p>\n<h3><b>Who Was Gordon Moore?<\/b><\/h3>\n<p>Gordon Moore is an American engineer and co-founder of Intel Corporation. Intel Corporation, commonly known as Intel, is a multinational technology company that designs and manufactures microprocessors and other semiconductor components.<\/p>\n<h3><b>Shaping Modern Technology<\/b><\/h3>\n<p>Gordon Moore&#8217;s contributions to the field of semiconductors has had a major impact in technology. As has his role in co-founding Intel, which has shaped the modern technology landscape. He partly helped usher in the era of microelectronics. Enabling the rapid progress in computing power and miniaturisation that we continue to experience today.<\/p>\n<h3><strong>Updating Moore\u2019s Law<\/strong><\/h3>\n<p>The new \u201cMoore&#8217;s Law\u201d for LLMs suggests an approximately eight-fold increase in the number of parameters every year. Potentially yielding a similar increase in performance. Although recent evidence suggests this might not be the case. We might have reached the limit of performance improvements purely due to size.<\/p>\n<h3><strong>\u201cMoore\u2019s Law\u201d for LLMs<\/strong><\/h3>\n<p>The new \u201cMoore&#8217;s Law\u201d for LLMs suggests an approximately eight-fold increase in the number of parameters every year<\/p>\n<figure style=\"width: 1024px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/autogenai.com\/apac\/wp-content\/uploads\/sites\/5\/2025\/02\/FP9yWkzRoel9pcTy3I4w.webp\" alt=\"\" width=\"1024\" height=\"484\" \/><figcaption class=\"wp-caption-text\"><strong>\u201cMoore\u2019s Law\u201d for LLMs<strong><br \/>The new \u201cMoore&#8217;s Law\u201d for LLMs suggests an approximately eight-fold increase in the number of parameters every year<\/strong><\/strong><\/figcaption><\/figure>\n<h3><b>Performance and training<\/b><\/h3>\n<p>Most meaningful assessments of the output quality of current LLMs are subjective. This is perhaps inevitable when dealing with language. LLMs have written convincing articles for example like this one <a href=\"https:\/\/www.theguardian.com\/commentisfree\/2020\/sep\/08\/robot-wrote-this-article-gpt-3\">published in the Guardian<\/a>.<\/p>\n<h3><strong>Surpassing Humans<\/strong><\/h3>\n<p>LLMs have surpassed expert human-level performance in some quantitatively-assessed writing tasks. These include machine translation, next token prediction (content generation), and even some computer programming assignments.<\/p>\n<h3><b>The Cost of Training<\/b><\/h3>\n<p>It currently costs approximately $10m to train a LLM and the current process takes a month. The research and development costs for the most sophisticated models like OpenAI\u2019s GPT-3 are unknown but are likely much higher. <a href=\"https:\/\/openai.com\/blog\/microsoft\/\">Microsoft invested $1b into OpenAI in 2019<\/a>.<\/p>\n<h3><strong>Computing Power<\/strong><\/h3>\n<p>The high training costs of LLMs are primarily due to the huge amounts of computing power. This is to find the best model parameters across such vast amounts of data.<\/p>\n<h3><b>Other Models<\/b><\/h3>\n<p>Other models, such as recurrent neural networks and classical machine learning models have had access to similar data as LLMs. But none have managed to surpass human-level performance on some tasks like LLMs have.<\/p>\n<h3><b>What does the future hold?<\/b><\/h3>\n<p>LLMs is transforming industries that rely on text including translation services and copywriting. Already deployed in next generation chatbots and virtual assistants. The business that I work for, AutogenAI, is deploying specifically trained enterprise-level large language models. This is in order to speed and improve the production of tenders, bids and proposals.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Large_Language_Models_LLMs-2\"><\/span>Large Language Models (LLMs)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Cutting-edge LLMs are pre-trained on nearly the entire corpus of digitised human knowledge. This includes\u00a0<a href=\"https:\/\/commoncrawl.org\/\">Common Crawl<\/a>, Wikipedia, digital books and other internet content.<\/p>\n<p>The table below summarises what modern LLMs have \u2018read\u2019\u2026<\/p>\n<table>\n<thead>\n<tr>\n<th>Data Source<\/th>\n<th>Number of words from that data source<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Common Crawl<\/td>\n<td>580,000,000,000<\/td>\n<\/tr>\n<tr>\n<td>Books<\/td>\n<td>26,000,000,000<\/td>\n<\/tr>\n<tr>\n<td>Wikipedia<\/td>\n<td>94,000,000,000<\/td>\n<\/tr>\n<tr>\n<td>Other web text<\/td>\n<td>26,000,000,000<\/td>\n<\/tr>\n<tr>\n<td>TOTAL<\/td>\n<td>726,000,000,000<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>LLMs have \u2018read\u2019 over 700,000,000,000 words. A human reading 1 word every second would take 23,000 years to achieve the same feat.<\/p>\n<h3><b>The Sophistication of LLMs<\/b><\/h3>\n<p>LLMs are now capable of generating text that is sophisticated enough to complete scientific papers. They can output computer code that is superior to many expert developers. Elon Musk has described LLMs as \u201cthe most important advance in Artificial Intelligence\u201d.<\/p>\n<figure style=\"width: 1024px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/autogenai.com\/apac\/wp-content\/uploads\/sites\/5\/2025\/02\/UyJrSUwWRfyLrbHtmJR8.webp\" alt=\"\" width=\"1024\" height=\"370\" \/><figcaption class=\"wp-caption-text\"><strong>Large Large Model timeline and growth<\/strong><br \/>The first language model was in 2018 and they have been exponentially increasing in size &#8211; measured by the number of parameters they contain &#8211; ever since.<\/figcaption><\/figure>\n<h3><b>The Growth of Models<\/b><\/h3>\n<p>The models themselves have been exponentially increasing in size measured by the number of parameters they contain. This means that their parameter growth is continually getting faster. Parameters are numbers the model learns through training.<\/p>\n<h3><b>Parameter Scales<\/b><\/h3>\n<p>Imagine a range from -2,147,483,648 to 2,147,483,647; that\u2019s the scale of their parameters. It\u00a0 helps provide them with impressive computing power.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Whats_a_Parameter-2\"><\/span><b>What&#8217;s a Parameter?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>A parameter is an adjustable value or characteristic that can influence the outcome of a system or process. Set or adjusted to measure behaviour, performance and outcomes to change the process or function of a system. Parameters are crucial in various fields, such as mathematics, science, engineering and programming.<\/p>\n<h3><b>How are Parameters Used in Large Language Modelling?<\/b><\/h3>\n<p>Parameters play a crucial role in large language modelling. Designed to understand and generate human-like text by learning patterns. These also learn relationships, and context from vast amounts of text data. Here&#8217;s how parameters work in large language models:<\/p>\n<h3><b>Model Architecture Parameters:\u00a0<\/b><\/h3>\n<p>The architecture of a large language model, such as GPT. Defined by parameters that determine the structure of the neural network.<\/p>\n<h3><b>Weights and Biases<\/b><\/h3>\n<p>Large language models consist of a vast number of weights and biases that learn from text data during the training process.<\/p>\n<h3><b>Embedding Parameters<\/b><\/h3>\n<p>Embeddings represent words or tokens in a continuous vector space.<\/p>\n<p>Parameters in large language models determine the model&#8217;s architecture. Learning representations, its behaviour during training and generation, and its ability to understand and generate natural language text.<\/p>\n<h3><strong>Large Large Model timeline and growth<\/strong><\/h3>\n<p>The first language model was in 2018 and they have been exponentially increasing in size. &#8211; Measured by the number of parameters they contain &#8211; ever since.<\/p>\n<h3><b>Gordon Moore<\/b><\/h3>\n<p>In 1965, American engineer Gordon Moore famously predicted that the number of transistors per silicon chip would double every year. Transistors are the electronic switches within a silicon chip. The silicon chip is the building block of all modern electronic devices.<\/p>\n<h3><b>Who Was Gordon Moore?<\/b><\/h3>\n<p>Gordon Moore is an American engineer and co-founder of Intel Corporation. Intel Corporation, commonly known as Intel, is a multinational technology company that designs and manufactures microprocessors and other semiconductor components.<\/p>\n<h3><b>Shaping Modern Technology<\/b><\/h3>\n<p>Gordon Moore&#8217;s contributions to the field of semiconductors has had a major impact in technology. As has his role in co-founding Intel, which has shaped the modern technology landscape. He partly helped usher in the era of microelectronics. Enabling the rapid progress in computing power and miniaturisation that we continue to experience today.<\/p>\n<h3><strong>Updating Moore\u2019s Law<\/strong><\/h3>\n<p>The new \u201cMoore&#8217;s Law\u201d for LLMs suggests an approximately eight-fold increase in the number of parameters every year. Potentially yielding a similar increase in performance. Although recent evidence suggests this might not be the case. We might have reached the limit of performance improvements purely due to size.<\/p>\n<h3><strong>\u201cMoore\u2019s Law\u201d for LLMs<\/strong><\/h3>\n<p>The new \u201cMoore&#8217;s Law\u201d for LLMs suggests an approximately eight-fold increase in the number of parameters every year<\/p>\n<figure style=\"width: 1024px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/autogenai.com\/apac\/wp-content\/uploads\/sites\/5\/2025\/02\/FP9yWkzRoel9pcTy3I4w.webp\" alt=\"\" width=\"1024\" height=\"484\" \/><figcaption class=\"wp-caption-text\"><strong>\u201cMoore\u2019s Law\u201d for LLMs<strong><br \/>The new \u201cMoore&#8217;s Law\u201d for LLMs suggests an approximately eight-fold increase in the number of parameters every year<\/strong><\/strong><\/figcaption><\/figure>\n<h3><b>Performance and training<\/b><\/h3>\n<p>Most meaningful assessments of the output quality of current LLMs are subjective. This is perhaps inevitable when dealing with language. LLMs have written convincing articles for example like this one <a href=\"https:\/\/www.theguardian.com\/commentisfree\/2020\/sep\/08\/robot-wrote-this-article-gpt-3\">published in the Guardian<\/a>.<\/p>\n<h3><strong>Surpassing Humans<\/strong><\/h3>\n<p>LLMs have surpassed expert human-level performance in some quantitatively-assessed writing tasks. These include machine translation, next token prediction (content generation), and even some computer programming assignments.<\/p>\n<h3><b>The Cost of Training<\/b><\/h3>\n<p>It currently costs approximately $10m to train a LLM and the current process takes a month. The research and development costs for the most sophisticated models like OpenAI\u2019s GPT-3 are unknown but are likely much higher. <a href=\"https:\/\/openai.com\/blog\/microsoft\/\">Microsoft invested $1b into OpenAI in 2019<\/a>.<\/p>\n<h3><strong>Computing Power<\/strong><\/h3>\n<p>The high training costs of LLMs are primarily due to the huge amounts of computing power. This is to find the best model parameters across such vast amounts of data.<\/p>\n<h3><b>Other Models<\/b><\/h3>\n<p>Other models, such as recurrent neural networks and classical machine learning models have had access to similar data as LLMs. But none have managed to surpass human-level performance on some tasks like LLMs have.<\/p>\n<h3><b>What does the future hold?<\/b><\/h3>\n<p>LLMs is transforming industries that rely on text including translation services and copywriting. Already deployed in next generation chatbots and virtual assistants. The business that I work for, AutogenAI, is deploying specifically trained enterprise-level large language models. This is in order to speed and improve the production of tenders, bids and proposals.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Large Language Models (LLMs) Cutting-edge LLMs are pre-trained on nearly the entire corpus of digitised human knowledge. This includes\u00a0Common Crawl, Wikipedia, digital books and other internet content. The table below summarises what modern LLMs have \u2018read\u2019\u2026 Data Source Number of words from that data source Common Crawl 580,000,000,000 Books 26,000,000,000 Wikipedia 94,000,000,000 Other web text&#8230;<\/p>\n","protected":false},"author":149,"featured_media":2955,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[4],"tags":[],"class_list":["post-2951","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-category-2"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>What Is a Large Language Model? | AutogenAI APAC<\/title>\n<meta name=\"description\" content=\"Large Language Models (LLMs) are silicon brains that can produce and analyse language. 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