A hallucination isn't a malfunction of a YAG model, it is a normal output. We use the word "hallucination" as if something unusual is happening, as if the model drifted into a strange state and started dreaming or began to invent. This is not exactly what is happening. Here is what the model actually does. Given any input, it generates the most plausible sounding continuation. It was trained on enormous amount of text, so it learned what fluid or structured language looks like. And it produces exactly that. There is no built-in step where the model checks the answer against reality.
It has no mechanism for that. Large language models by design generate text that sounds right, because that is what it was trained to do. When the output happens to match reality, we call it a correct answer. When it doesn't, we call it a hallucination. But the model was doing the same thing both times. So if we define it simply, a hallucination is a plausible sounding answer that isn't actually true. Confidence is not something it adds on top when it is sure. It is the default register of the output, because the training data was written by humans that sounded confident.
Now an important clarification. This doesn't mean that nothing can be improved. Modern systems are already designed to reduce hallucinations. They can check answers against external sources, retrieve documents, apply constraints, or verify parts of the output. And these systems are getting better. But notice something important. All of that happens around the model, not inside the core generation step. The model itself is still producing the most plausible continuation. So hallucinations can be reduced even significantly, but the underlying behavior, producing fluent text without a built-in truth check, does not change.
A hallucination is not the model breaking down. It is the model working exactly as it was designed. Producing fluent text with no way to know whether the text is true or not. Even as systems around it improve, the distinction remains. The gap between fluency and truth is the thing to understand. This was a note on AI. today, thank you for watching and see you in the next one.
