After OpenAI released ChatGPT in 2022, the world has seen new technological advancements, and there seems to be no end to this ever-growing development. AI Chatbots have been released by Google, Microsoft, Meta, Anthropic and a number of other companies. All chatbots are powered by LLMs (Large Language Models). But what exactly is a large language model and how does it work? To learn more about the LLM, follow our explanation below.
How large language models work
An LLM (Large Language Model) is a type of artificial intelligence (AI) that is trained on a large dataset of texts. It is designed to understand and generate human language based on probabilistic principles. It is basically 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 data set, the better the capabilities of LLM's natural language processing (NLP). In general, AI researchers claim that LLMs with 2 billion or more parameters are "large" language models. If you're wondering what a parameter is, it's the number of variables the model is trained on. The larger the parameter size, the larger the model will be and will have more possibilities.
To give you an example, when OpenAI released 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 116 times larger model. And the state-of-the-art GPT-4 model has 1.76 trillion parameters.
