What are AI hallucinations? Why AIs sometimes make things up

What are AI hallucinations? Why AIs sometimes make things up

When AI systems try to bridge gaps in their training data, the results can be wildly off the mark: fabrications and non sequiturs researchers call hallucinations.

“Hallucinating” AI models help coin Cambridge Dictionary’s word of the year

Cambridge: "When an artificial intelligence hallucinates, it produces false information."

Okay so, we just found that over 50 papers published at @Neurips 2025 have AI hallucinations by @alexcdot(Alex Cui) | Twitter Thread Reader

Okay so, we just found that over 50 papers published at @Neurips 2025 have AI hallucinations I don't think people realize how bad the slop is right now It's not just that researchers from @GoogleDeepMind, @Meta, @MIT, @Cambridge_Uni are using AI - they allowed LLMs to generate hallucinations in their papers and didn't notice at all. It's insane that these made it through peer review👇

AI hallucinations are getting worse – and they're here to stay

An AI leaderboard suggests the newest reasoning models used in chatbots are producing less accurate results because of higher hallucination rates. Experts say the problem is bigger than that

AI Comes for Academics. Can We Rely on It?

By now, the fact that artificial intelligence can hallucinate is, I hope, well known. There are countless examples of platforms like ChatGPT giving the wrong answer to a straightforward question or

OpenAI admits AI hallucinations are mathematically inevitable, not just engineering flaws

In a landmark study, OpenAI researchers reveal that large language models will always produce plausible but false outputs, even with perfect data, due to fundamental statistical and computational limits.

AI Translations Are Adding ‘Hallucinations’ to Wikipedia Articles

AI translated articles swapped sources or added unsourced sentences with no explanation, while others added paragraphs sourced from completely unrelated material.

Five times AI hallucinations embarrassed governments

From the Trump administration’s “formatting errors” to South Africa’s historic policy withdrawal, AI confabulation is infiltrating official documents.

Shameless Guesses, Not Hallucinations

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Lawyers Caught Citing AI-Hallucinated Cases Call It a 'Cautionary Tale'

The attorneys filed court documents referencing eight non-existent cases, then admitted it was a "hallucination" by an AI tool.

Why Language Models Hallucinate

Like students facing hard exam questions, large language models sometimes guess when uncertain, producing plausible yet incorrect statements instead of admitting uncertainty. Such "hallucinations" persist even in state-of-the-art systems and undermine trust. We argue that language models hallucinate because the training and evaluation procedures reward guessing over acknowledging uncertainty, and we analyze the statistical causes of hallucinations in the modern training pipeline. Hallucinations need not be mysterious -- they originate simply as errors in binary classification. If incorrect statements cannot be distinguished from facts, then hallucinations in pretrained language models will arise through natural statistical pressures. We then argue that hallucinations persist due to the way most evaluations are graded -- language models are optimized to be good test-takers, and guessing when uncertain improves test performance. This "epidemic" of penalizing uncertain responses can only be addressed through a socio-technical mitigation: modifying the scoring of existing benchmarks that are misaligned but dominate leaderboards, rather than introducing additional hallucination evaluations. This change may steer the field toward more trustworthy AI systems.