Large Language Models like ChatGPT hallucinate because they are trained to predict the most plausible next token, not to retrieve factual information; when asked about niche, obscure, or recent topics where no single token dominates the probability distribution, the model generates confident but incorrect answers that sound plausible, as it lacks an 'I don't know' mechanism in its architecture.
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Why ChatGPT lies to your face — confidently #shorts #ai #chatgptAñadido:
Now the uncomfortable part.
Ask ChatGPT about a niche fact, a recent event, or an obscure [music] person, and it may give you a detailed, confident, completely wrong answer.
Fake citations, invented [music] quotes, papers that don't exist. This is called hallucination, and it's not a bug. It's baked into how the model works.
Remember, the model isn't retrieving facts. [music] It's predicting probabilities over tokens.
When you ask [music] something well-represented in the training data, the correct token dominates the distribution.
But when you ask something rare, obscure, or [music] recent, no single token dominates.
The model picks a plausible-sounding answer.
It has no in
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