In the recent decade, one of the most groundbreaking advancements has been the development of ChatGPT and Large Language Models (LLMs). This technology is the fusion of artificial intelligence and linguistic prowess and is not only intelligent but also surprisingly conversational.
Some definitions:
GPT: Generative Pre-trained Transformer. It’s an advanced type of AI model designed to understand and generate human-like text. It’s a part of a broader category of AI known as Natural language processing (NLP). “Pre-trained” means that before GPT is ever used for specific tasks, it undergoes an extensive training process. During this phase, it’s fed large amounts of text data. This data includes books, articles, websites, and other forms of written language. The goal of this training is to help the model learn the patterns and nuances of human language. Transformer is a type of neural network architecture that’s particularly effective for handling sequential data, like text. Transformers are known for their ability to handle “long-range dependencies” in text. This means they’re good at understanding how earlier parts of a sentence can affect the meaning of later parts, which is crucial for understanding and generating coherent and contextually accurate text.
NLP: teaching computers to understand the nuances of human language (syntax, semantics, and context)
So, how does all that work? LLMs’ core function is to understand, interpret, and generate human-like text. It begins with the training phase, where LLMs are exposed to vast amounts of text data, including books, articles, websites, and other sources. This exposure enables the models to learn human language, allowing it to process sequential data like text. Most importantly, LLMs learn the likelihood of word sequence in human language, essentially understanding which word is likely to follow a given series of words.
In the pre-training stage, they learn general language patterns and try to “understand” language.
Then, in the generation phase, LLMs will generate responses when the user inputs text into the model, based on the language patterns it learned during training. The model makes predictions about the most likely next word or sequence of words, considering the entire context of users’ input. It allows LLMs to perform different tasks.
