LLMs Explained: How AI Like ChatGPT Understands Language

Artificial intelligence is all the rage right now, and tools like Chatgpt are blowing up. Have you ever wondered how these AI models work? In this article, I will explain how large language models (LLMs) like GPT work.

Firstly, large language models are defined as AI that can process, understand, and generate human language. They do this by analysing huge amounts of text and learning patterns in how words are used in sentences. The first step and foundation for any LLM is data, enormous amounts of data. For example, Chatgpt 4 is trained on 1,000,000 gigabytes of data. This includes text from books, online sources, essays, etc.

The next step is training the model using the collected data. During the training process, the model takes an incomplete sentence and tries to predict the next word. For example, take the sentence: “The cat sat on the ___”. The model tries to predict the next word. It might initially guess “wall” or “cushion”, but the answer may be mat. When the prediction is incorrect, the model adjusts its internal parameters slightly and tries again, until it becomes highly accurate at making these predictions. This process makes the model very good at mimicking human language.

The architecture behind this is called “transformer”. The concept of the transformer was first introduced by Google, and it has completely revolutionised the AI space. The transformer, in essence, allows the model to pay attention to specific parts of the sentence. For example, in the sentence “The dog chased the ball because it was fast,” the model learns to figure out whether “it” refers to the dog or the ball by analysing context. This is called the attention mechanism, and this is what allows LLMs to understand human language.

Using this concept, every time you type something into Chatgpt, the model analyses your input, predicts what kind of response will be most likely by a human, and generates it one word at a time. What’s impressive is that this all happens in a fraction of a second, thanks to powerful hardware.

In conclusion, large language models like GPT-4 work by learning patterns in language through vast data and powerful training techniques. They are not conscious or intelligent in the human sense, but they are incredibly good at mimicking human language, which makes them useful in everything from customer support to creative writing.