How Large Language Models Work: From Next-Word Prediction to Chatbots

What actually happens inside a large language model: how it learns by predicting the next word, what the Transformer and its attention mechanism do, why size changed what these models can do, how a raw text predictor is turned into an assistant that follows instructions, and why it sometimes makes things up with total confidence.

0:30 A machine that guesses the next word

Pretraining: the model reads huge amounts of text and learns to predict the next word, with no human labels.

Sources: Hugging Face LLM Course (original) · OpenAI (original)

1:54 The Transformer and attention

The Transformer, introduced by Google researchers in the paper Attention Is All You Need, lets every word look at every other word in one step.

Sources: Google Research (original) · arXiv (original)

3:27 Why size changed everything

Most modern LLMs keep only the decoder half of the Transformer; scaling them up made few-shot learning possible.

Sources: Hugging Face LLM Course (original) · arXiv (original)

4:56 From text predictor to assistant

Fine-tuning with human demonstrations and rankings, known as reinforcement learning from human feedback, turns a raw text predictor into an assistant.

Sources: OpenAI (original) · arXiv (original)

6:16 Why it makes things up

Hallucinations come from next-word prediction on rare facts and from tests that reward guessing over saying I don't know.

Sources: OpenAI (original)

Follow the show in your podcast app to get each new episode as soon as it is out.

Produced by Juan Pablo.