How does a language model like ChatGPT work?
A language model predicts the most likely next word based on billions of text examples. It does not consult a knowledge base: it generates a plausible response from statistical regularities learned during training. This mechanism explains both its performance and its errors.
A language model predicts the most probable next word based on everything that precedes it. Trained on vast quantities of text, it has learned statistical regularities: which words follow which others, in which contexts. When you ask it a question, it does not consult a knowledge base or search for the correct answer in a drawer. It generates, word after word, the most plausible continuation. This unique mechanism explains both its impressive performance and its surprising errors.
Training, in three stages
First, pre-training. The model reads billions of texts and adjusts billions of internal parameters to better predict what comes next. At the end of this phase, it handles language fluently but without any specific direction.
Next, instruction tuning. It is shown examples of good responses to prompts, so it learns to be useful and follow requests.
Finally, alignment through human feedback. People evaluate its responses, and the model is refined to produce outputs deemed more helpful, safer and more honest. It is not perfect, but it steers its behavior.
What this means for you
Since the model generates what is plausible, it can produce a false answer delivered with the same confidence as a correct one. It is not aware of the difference. That is why any critical information — an amount, a date, a legal rule, a medical fact — must be verified in a primary source.
Since the model only knows what was in its training data, it is generally unaware of events after its construction date, unless it is connected to real-time search.
Since the model predicts based on the context you give it, the quality of your request directly influences the quality of its response.
A useful image
Imagine someone who has read an entire library without ever living in the world. They would know countless expressions, they could imitate every style, but they could not verify on their own whether what they say is true. It is an imperfect but illuminating approximation of a language model.
Frequently Asked Questions
Does the model search the internet?
Not by default. Some assistants add a web search feature, but the base model generates from what it has learned.
Does it understand my question?
It processes the statistical form of your question and produces a coherent response. Speaking of understanding in the human sense is a misleading shortcut.
Why do I get different answers to the same question?
Because generation includes an element of controlled randomness, to avoid mechanical responses.
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