How LLMs Learn Facts: It's About Training Data, Not True Understanding

Large language models like ChatGPT do not understand the world the way humans do, but instead predict the next word or token based on patterns learned from vast amounts of text scraped from books, websites, and blogs. Behind the chat interface, a computer program formats conversations into long strings and feeds them to the model, which essentially auto-completes the dialogue. A model answers that the sky is blue not because it perceives color, but because that phrasing appeared far more frequently in its training data than alternatives like 'the sky is red.' After initial training on raw text, models undergo fine-tuning where human raters score outputs for helpfulness and accuracy, shaping the model to produce more useful responses. This means an LLM's apparent factual knowledge is a reflection of what humans have collectively written, revealing as much about human communication patterns as about the technology itself.