What exactly is AI in the form of Large Language Models (LLMs), and how do they process language?

So, Large Language Models (LLMs) are a form of artificial intelligence that predict the next word in a sequence by performing billions of matrix multiplications on token embeddings. These models learn statistical patterns in language from massive datasets, using deep neural networks (transformers) to encode context, attention mechanisms to weigh relationships, and gradient-based optimization to update millions or billions of parameters.

Each word or subword in the input is first converted into a token embedding, which is a high-dimensional vector representing its semantic meaning. These embeddings are then processed through multiple layers of the transformer network. Within each layer, self-attention mechanisms allow the model to determine which tokens in the sequence are most relevant to predicting the next word, effectively capturing context across long sentences or paragraphs.

The model outputs a probability distribution over the entire vocabulary for the next token. The token with the highest probability is selected as the predicted word. During training, the model compares its predictions with the actual words from the dataset, calculates the loss, and uses gradient descent to adjust millions or billions of parameters to improve accuracy.

LLMs are highly versatile and can perform a variety of language tasks such as text generation, summarization, translation, question-answering, and even code generation. Their power comes from learning statistical relationships in massive datasets, not from true human understanding. By stacking more layers and increasing the number of parameters, these models can capture increasingly complex patterns in language, making them one of the most advanced tools in modern AI.