You asked ChatGPT a question about a famous scientist and received a compelling biographical text with dates, citations and titles of works. Only later did it become clear: half of the facts were made up. The scientist exists, but the quotes do not, the book with that title does not exist, and one of the dates is simply wrong.
Welcome to the world of “hallucinations” of neural networks – one of the main problems of modern AI.
What are neural network hallucinations?
The term “hallucinations” in the context of AI refers to situations where the model generates confident-sounding information that is factually incorrect. She does not “lie” in the human sense – she does not know that she is mistaken, she has no intention of deceiving. It simply does what it was designed to do: generate believable text.
This is the key difference. A person who lies knows the truth and hides it. A neural network that “hallucinates” does not have access to the “truth” – it works with statistical patterns in the text.
Why this happens: a simple explanation
A large language model is trained to predict the next word in a text based on previous ones. Very roughly speaking, she learned to answer questions “the way they are usually written in texts.”
Imagine a huge encyclopedia that can speak. When you ask about Leo Tolstoy, she answers accurately, because millions of texts have been written about him. When you ask about a little-known regional historian, the model “completes” the answer by analogy with how the biographies of historians usually look. Sounds convincing. But the specific facts are made up.
The problem is compounded by the fact that the model does not know what it knows reliably and what it does not. She does not have an internal “certainty detector” for every fact.
In what situations do neural networks lie most often?
Specific facts about little-known people and eventsBiographies, dates, positions, publications of little-known people – there is a high risk of fiction.
Links to sourcesAsk the AI to link to an article or book, and it will often come up with a plausible-looking link that doesn’t exist. DOI, URL, journal name – everything is convincing, everything is non-existent.
Latest eventsModels are trained on data up to a certain date. They either don’t know about events after this date or are just making things up.
Legal, medical and financial detailsSpecific legal provisions, drug dosages, tax rates – AI can be wrong about details that are critically important.
Hard numbers and statistics“According to research, 67% of users…” is a number that AI can simply generate without having a real source.
When can neural networks be trusted?
Understanding the limitations is not a reason to abandon AI. There are tasks where it is reliable:
✅ Explaining Common Concepts— how photosynthesis works, what inflation is, how the processor works. Here the model relies on a huge number of consistent sources.
✅ Writing and editing text– here the AI does not “invent facts”, but works with your material.
✅ Generation of ideas and structures— brainstorming, outline of the article, options for solving the problem. The accuracy of the facts is not critical here.
✅ Help with code— writing and explaining code. The code either works or it doesn’t – it’s testable.
✅ Translation and synthesis— working with your text, where the source is you.
How to check AI answers
Rule 1: Any specific fact – checkDate, name, title, number, quote – if this is important, open a search engine and check.
Rule 2: Ask for a source – and check its existenceIf the AI provides a link to an article or book, don’t be lazy to find it in reality. If the source does not exist, this is a signal to reconsider the entire answer.
Rule 3: Ask a clarifying question“Are you sure about this fact? Where does this information come from? — good models honestly admit uncertainty.
Rule 4: Cross-checkAsk the same thing from another AI or look in several independent sources.
Rule 5: Critical decisions – only with a live expertМедицинский диагноз, юридический совет, финансовое решение — ИИ может дать общее понимание, но не замену специалисту.
Вывод
Нейросети — мощный инструмент, но не энциклопедия и не оракул. Они отлично помогают думать, писать, объяснять и генерировать идеи. Но в конкретных фактах — особенно редких, свежих или специализированных — они ошибаются уверенно и без предупреждения.
Грамотный пользователь ИИ — это не тот, кто слепо верит ответам, и не тот, кто отвергает ИИ из-за его ошибок. Это тот, кто понимает, где доверять, а где проверять. Это и есть цифровая грамотность сегодняшнего дня.