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AI models make things up because they generate likely text, not verified facts. When information is missing or uncertain, a model may still produce a fluent answer instead of saying it does not know. The result is often called a hallucination: a plausible but false AI-generated statement, not a human-like experience.
Why can a model give a wrong answer so confidently?
A language model generates text by predicting likely continuations. It does not automatically check each sentence against the world. That can make an answer sound coherent even when the model lacks reliable information or has failed to signal its uncertainty.
OpenAI argues that common training and evaluation practices can reinforce this behavior: when a test rewards correct answers but gives little or no credit for admitting uncertainty, guessing may be a better-scoring strategy than abstaining. As OpenAI puts it, “Our new research paper argues that language models hallucinate because standard training and evaluation procedures reward guessing over acknowledging uncertainty.” This is an important proposed contributor, not a universal explanation for every model error. OpenAI’s explanation and its 2025 research paper discuss the argument.
The basic pressure to keep generating also matters. Anthropic summarizes it this way: “At a basic level, language model training incentivizes hallucination: models are always supposed to give a guess for the next word.” That describes a general feature of text generation; the specific internal processes examined in Anthropic’s study were observed in Claude, not established as the mechanism for every AI system. Anthropic’s study explains its scope and limitations.
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Are all hallucinations caused by missing information?
No. Google researchers distinguish between errors associated with a lack of relevant knowledge and errors made despite relevant knowledge being available to the model. In the first case, the model may fill a gap with a plausible completion. In the second, the problem is not simply missing information: the model may fail to use or communicate what it appears to know. Google’s categories are useful for understanding different failure modes, rather than proof that every error fits neatly into one bucket. Google Research’s 2024 analysis describes the distinction.
- Knowledge-related error: The model does not have dependable information for the question and supplies an unsupported answer.
- Uncertainty-related error: Relevant information may be present, but the model gives a confident answer that is wrong or insufficiently qualified.
Anthropic has also reported a possible model-specific mechanism in Claude: a feature associated with recognizing a known entity could suppress a default refusal mechanism. If recognition of a name is mistaken for knowing the answer, the model may continue with a plausible but untrue response. This finding comes from particular experiments and prompts; Anthropic notes that its interpretability method captures only part of a model’s computation and can include artifacts. It should not be treated as a demonstrated circuit shared by all models.
What do benchmark numbers tell us—and what do they not?
In an example presented by OpenAI on September 5, 2025, results from the SimpleQA setup in the GPT-5 System Card showed different trade-offs between answering and abstaining. These are benchmark results for the stated models and setup, not general hallucination rates.
| Model in OpenAI’s example | Abstention | Accuracy | Error |
|---|---|---|---|
| gpt-5-thinking-mini | 52% | 22% | 26% |
| OpenAI o4-mini | 1% | 24% | 75% |
The contrast illustrates why evaluation choices matter: a system that answers less often can have a different balance of accuracy, error, and abstention than one that guesses more. It does not establish how often language models in general make mistakes in ordinary use. The figures and their context are in OpenAI’s September 2025 explainer.
That explainer also uses a birthday-guessing example with a 1-in-365 chance. This is an illustration of how a guess can occasionally be correct despite uncertainty, not a measured performance result.
Can search or retrieval stop hallucinations?
Retrieval and web search can give a model external evidence to use, which may reduce errors caused by missing or outdated knowledge. They are not guarantees. Search can return irrelevant, incomplete, or unreliable material; the model can misread a source or make an intrinsic mistake such as a miscalculation even when evidence is available. Treat retrieved citations as leads to inspect, not automatic proof that an answer is true.
Other proposed safeguards include allowing a model to abstain, expressing uncertainty when confidence is not warranted, and evaluating systems in ways that reward calibrated uncertainty rather than confident guessing alone. Google researchers frame uncertainty expression as a path beyond a simple answer-or-abstain choice: “If we understand hallucinations as confident errors — incorrect information delivered without appropriate qualification — a third path emerges beyond the answer-or-abstain dichotomy: expressing uncertainty.” This is a position paper’s proposed direction, not a guarantee that uncertainty signaling will eliminate errors. Google Research’s 2026 position paper sets out that framing.
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- Identify claims that matter. Names, dates, quotations, statistics, legal or medical guidance, and current events deserve particular scrutiny.
- Open the cited source. Confirm that it exists, supports the specific claim, and is current enough for your purpose. A citation that merely mentions the topic is not confirmation.
- Cross-check consequential facts. Look for independent, authoritative sources, especially when a wrong answer could cause harm or cost.
- Ask for uncertainty or evidence. Request the model’s sources, assumptions, or a distinction between what is verified and what is inferred. A revised answer can still be wrong, so verify important claims yourself.
For high-stakes or time-sensitive questions, use primary sources and independent verification rather than relying on a chatbot’s confidence, wording, or search results alone.
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