hallucination
A hallucination is when an AI model produces content that sounds plausible but is false or unsupported.
In generative AI such as large language models, a hallucination is a confident answer containing facts, figures, quotes or sources that are not grounded in the training data or the material provided. It stems from the way models predict plausible next words, and developers try to reduce it with methods such as retrieval-augmented generation and human review.
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Articles covering this entry
Splitting the work also limits hallucination, the tendency of a model to produce plausible but wrong output.
Some may contain subtle flaws or hallucinations, meaning plausible but incorrect content.
The cause is often not hallucination, where a model makes up plausible but false content.
How can a self-repairing loop be safe in regulated fields such as finance and healthcare, where hallucinations and adversarial inputs carry real consequences?
OpenAI shipped early ChatGPT despite frequent hallucinations, meaning confident but wrong answers, and kept improving it based on user feedback.