Why Language Models Make Things Up

What the problem looks like

Ask a language model for a reference and it may give you a title, authors and a year that look entirely credible, for a work that does not exist. Ask about a component and it may describe a pin layout or a voltage rating that sounds right and is not. This behaviour is commonly called hallucination, though the word is a loose metaphor. The model is not seeing things or lying; it is doing what it was built to do, and the output happens to be false.

Understanding why helps you decide where to trust a model, where to check it, and how to design a project that copes with its mistakes.

The core reason: predicting plausible text

A language model is trained to predict what text is likely to come next, given the text so far. Through that training on huge amounts of writing, it absorbs grammar, style, a great deal of general information and many patterns of reasoning. But its basic job is producing a continuation that fits, not looking up a verified fact.

That has a direct consequence. When the model has strong, consistent patterns to draw on, such as a widely documented fact, the likely continuation is often also the correct one. When it has weak or conflicting patterns, such as an obscure part, a niche library or a recent event, the most plausible-sounding continuation can still be produced fluently. Nothing in the process automatically raises a flag saying “I do not actually know this”. The tone of the answer is therefore a poor guide to its reliability.

The general mechanics of how models learn from examples are described in how models are trained. Fluency comes from that training; truthfulness is only partly a by-product of it.

Situations that raise the risk

Errors are more likely in certain circumstances.

• Specific details: exact numbers, dates, names, quotations, citations, version numbers and part specifications. These are precisely the details builders care about.
• Rare or niche topics: subjects with little written coverage, or where sources disagree.
• Recent information: a model’s knowledge comes from data collected up to some point, and it may not know what changed afterwards.
• Leading questions: if your question assumes something false, a model may go along with the assumption rather than correct it.
• Requests for sources: asking for references can produce invented ones that look formatted correctly.
• Long outputs: small slips early can compound, and the model tends to stay consistent with its own earlier statements even when they were wrong.
• Arithmetic and multi-step logic: models can make calculation errors, and a convincing explanation does not guarantee the result is right.
• Unusual contexts: instructions that pull in different directions, or inputs unlike anything in training.

The quality and coverage of the underlying text matters too. Gaps and skews in the material the model was built from, discussed under training data, show up as uneven reliability across topics.

Why the confident tone

The style of an answer is learned from human writing, which is mostly written in an assured, explanatory voice. Models are also often tuned to be helpful and to give complete answers, which can make an answer feel more settled than the evidence supports. Some systems can be asked to express uncertainty, and that may help somewhat, but a model’s stated confidence is not a dependable measurement of its accuracy.

Habits for checking answers

You cannot remove the problem by wording alone, but you can manage it.

1. Treat output as a draft, not a source. Use it to get ideas, outlines and starting points, then verify what matters.
2. Check specifics against primary documents. Pin assignments, voltage limits and timing values belong in the manufacturer’s datasheet. The habits in reading datasheets are the right backstop for any model-supplied number.
3. Verify citations exist. Look up any referenced book, paper or page yourself before relying on it.
4. Run the code. If a model writes code or commands, test them in a safe setting, and read them before use. Library functions and options may be invented or outdated. Short tests, as in automated testing, expose many errors quickly.
5. Recompute calculations. Do the arithmetic yourself or with a calculator or script.
6. Cross-check with a second source, and treat agreement with another model as weak evidence, since models can share the same blind spots.
7. Ask for the basis. Request which part of the supplied text supports a claim, then verify it. The explanation can itself be unreliable, so do not stop there.
8. Be most careful where mistakes cost most. For electrical safety, batteries, health, law or money, do not act on model output without confirming it with authoritative sources or a qualified person.

Designing projects that cope

If you build the model into something, plan for wrong answers.

• Ground it in your own material. Providing the relevant text in the prompt, or retrieving it by similarity as described in embeddings and semantic search, gives the model something concrete to work from. This tends to reduce invention but does not remove it, because the model may still misread or embellish the text.
• Constrain the task. Narrow jobs, such as extracting fields from supplied text, are generally safer than open-ended question answering. Good prompt design, including permission to answer “not stated”, helps.
• Validate outputs in code. Check formats and ranges, and reject anything impossible.
• Keep a person in the loop for consequential decisions, and make it easy to see the source behind an answer.
• Do not give the model unchecked control of anything where a wrong action could cause harm, such as motors, heaters or locks. Use conventional safeguards.
• Measure. Build a small test set of questions with known answers and track how often the system gets them wrong, repeating the test when you change the model or prompts. Part of any AI feature evaluation is deciding whether the remaining error rate is tolerable.

Will it go away?

Methods for reducing false output keep improving, including better training, retrieval from trusted sources and tools that let a model check things. But there is no basis for expecting a guarantee of accuracy from a system that generates text by prediction. It is more realistic to expect fewer errors than none, and to keep verification as a standing habit.

This is also a useful corrective to broader claims about machines that understand everything. The current systems are impressive pattern learners with specific, well-known failure modes, a very different thing from the speculative idea of artificial general intelligence.