What is a Large Language Model (LLM): Explained

In Short
  • LLMs or large language models are a type of Artificial Intelligence (AI) that can understand and generate human language, based on probability.
  • LLMs are built on transformer-based neural networks and trained on a large corpus of textual data including books, articles, websites, and more.
  • The larger the LLM model, the more capabilities it exhibits. It can generate code, deduce patterns, and understand complex reasoning questions.

After OpenAI released ChatGPT in 2022, the world has witnessed new technological advancements, and it seems there is no end to this ever-expanding development. AI Chatbots have been released by Google, Microsoft, Meta, Anthropic, and a score of other companies. All of the chatbots are powered by LLMs (Large Language Models). But what exactly is a large language model and how does it work? To learn about LLMs, follow our explainer below.

A Basic Definition of LLM

An LLM (Large Language Model) is a type of Artificial Intelligence (AI) that is trained on a large dataset of texts. It’s designed to understand and generate human language based on principles of probability. It’s essentially a deep-learning algorithm. An LLM can generate essays, poems, articles, and letters; generate code; translate texts from one language to another, summarize texts, and more.

how large language models work
Image Courtesy: Google

The larger the training dataset, the better the LLM’s natural language processing (NLP) capabilities. Generally, AI researchers contend that LLMs with 2 billion or more parameters are “large” language models. If you are wondering what is a parameter, it’s the number of variables on which the model is trained. The larger the parameter size, the larger will be the model, and will have more capabilities.

To give you an example, when OpenAI released the GPT-2 LLM in 2019, it was trained on 1.5 billion parameters. Later in 2020, GPT-3 was released with 175 billion parameters, over 116x larger model. And the state-of-the-art GPT-4 model has 1.76 trillion parameters.

As you can see, over time, the parameter size is getting larger, bringing advanced and more complex capabilities to large language models.

How LLMs Work: The Training Process

In simple terms, LLMs learn to predict the next word in a sentence. This learning process is called pre-training where the model is trained on a large corpus of text including books, articles, news, extensive textual data from websites, Wikipedia, and more.

In this pre-training process, a model learns how a language works, its grammar, syntax, facts about the world, reasoning abilities, patterns, and more. Once the pre-training is done, a model goes through the fine-tuning process. As you can deduce, fine-tuning is done on specific datasets.

For example, if you want the LLM to be good at coding, you fine-tune it on extensive coding datasets. Similarly, if you want the model to be good at creative writing, you train the LLM on a large corpus of literature material, poems, etc.

What is the Transformer Architecture for LLMs?

Almost all modern LLMs are built on the transformer architecture, but what is it exactly? Let’s briefly go through the history of LLMs. In the pre-transformer era, there were several neural network architectures like RNN (Recurrent Neural Network), CNN (Convolutional Neural Network), and more.

However, in 2017, researchers from the Google Brain team released a seminal paper called “Attention is All You Need” (Vaswani, et al). This paper introduced the Transformer architecture which has now become the foundation of all LLMs dealing with natural language processing tasks. The core idea of the transformer architecture is self-attention.

attention is all you need paper
Image Courtesy: arXiv / Google

It can process all words in a sentence parallelly, understanding the context and relationship between words. It also leads to efficient training as it unlocks parallelism. After the paper was released, Google released the first transformer-based LLM called BERT in 2018. Later, OpenAI joined in and released its first GPT-1 model on the same architecture.

Applications of LLMs

We already know that LLMs now power AI chatbots like ChatGPT, Gemini, Microsoft Copilot, and more. It can perform NLP tasks including text generation, translation, summarization, code generation, writing stories, poems, etc. LLMs are also being used for conversational assistants.

chatgpt writing a poem

Recently, OpenAI demoed its GPT-4o model which is remarkable at engaging in conversations. Apart from that, LLMs are already being tested for creating AI agents that can perform tasks for you. Both OpenAI and Google are working to bring AI agents in the near future.

Overall, LLMs are being widely deployed as customer chatbots and used for content generation as well. While large language models are on the rise, ML researchers believe that another breakthrough is required to achieve AGI — an AI system more intelligent than humans.

We have not seen such breakthrough developments in the Generative AI era yet, however, some researchers believe that training a much larger LLM could lead to some level of consciousness in AI models.

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