What is a Large Language Model (LLM): Explained

Jun. 8, 2024

A Basic Definition of LLM

An LLM (Large Language Model) is a type ofArtificial 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.

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.

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

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.

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.

We already know that LLMs now powerAI chatbotslike 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.

Recently, OpenAI demoed itsGPT-4o modelwhich 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 theGenerative AIera 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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