Why language models hallucinate

At OpenAI, we’re working hard to make AI systems more useful and reliable. Even as language models become more capable, one challenge remains stubbornly hard to fully solve: hallucinations. By this we mean instances where a model confidently generates an answer that isn’t true. Our new research paper⁠(opens in a new window) argues that language models hallucinate because standard training and evaluation procedures reward guessing over acknowledging uncertainty.

ChatGPT also hallucinates. GPT‑5 has significantly fewer hallucinations especially when reasoning⁠, but they still occur. Hallucinations remain a fundamental challenge for all large language models, but we are working hard to further reduce them.

What are hallucinations?

Hallucinations are plausible but false statements generated by language models. They can show up in surprising ways, even for seemingly straightforward questions. For example, when we asked a widely used chatbot for the title of the PhD dissertation by Adam Tauman Kalai (an author of this paper), it confidently produced three different answers—none of them correct. When we asked for his birthday, it gave three different dates, likewise all wrong. 

Teaching to the test

Hallucinations persist partly because current evaluation methods set the wrong incentives. While evaluations themselves do not directly cause hallucinations, most evaluations measure model performance in a way that encourages guessing rather than honesty about uncertainty.

Think about it like a multiple-choice test. If you do not know the answer but take a wild guess, you might get lucky and be right. Leaving it blank guarantees a zero. In the same way, when models are graded only on accuracy, the percentage of questions they get exactly right, they are encouraged to guess rather than say “I don’t know.”

For a concrete example, consider the SimpleQA eval as an example from the GPT5 System Card⁠(opens in a new window).