LLMs Are Classifiers: How Language Models Predict the Next Token

In this video, we break down one of the most important ideas behind large language models: there is no magic behind text generation — it’s a classification problem. You’ll see how LLMs take a prompt as input and assign probabilities to every possible next token in their vocabulary, then choose the most likely one. Using simple, visual examples, we show how changing just one word in a prompt can completely change the model’s probability distribution and final output. You’ll learn how to: * Think about LLM text generation as multi-class classification * Understand what “next-token prediction” …