Hallucination in AI: The Hidden Flaw in Large Language Models
In the context of Large Language Models (LLMs), hallucination refers to the phenomenon where the model generates text that is nonsensical, factually incorrect, or inconsistent with its training data . Essentially, it's when the model "makes things up" or presents fabricated information as if it were true. What it is: LLMs are trained on vast amounts of text data, and they learn to predict the next word in a sequence. This process can lead to the generation of text that sounds plausible but is actually false or misleading. Why it happens: LLMs don't understand truth or facts in the same way humans do. They are probabilistic models, meaning they generate text based on patterns learned from their training data. When there isn't enough information or the model encounters ambiguity, it may fill in the gaps with invented information. Examples: A model might confidently assert a historical event that never happened or provide incorrect biographical details. It c...