Why Do AI Language Models Hallucinate? The Science Behind Those Confident Mistakes

You’ve probably experienced it yourself: you ask an AI chatbot a seemingly straightforward question, and it responds with absolute confidence, only for you to discover later that the answer was completely wrong. Perhaps it invented a historical date, fabricated a scientific fact, or confidently cited a source that doesn’t exist.

“Hallucination” remains one of the most stubborn challenges in artificial intelligence. Even as language models become increasingly sophisticated, they still occasionally generate plausible-sounding information that’s entirely false. But why does this happen?

Recent research from OpenAI offers fascinating insights into the root causes of these AI fabrications, and the findings might surprise you.

What Exactly Are Hallucinations?

When we talk about AI hallucinations, we’re referring to instances where a language model confidently produces statements that sound reasonable but are factually incorrect. The term “hallucination” is somewhat misleading, as it suggests a human-like perceptual experience, but it’s the label that’s stuck.

These aren’t simple typos or calculation errors. Hallucinations are often eerily specific and plausible. When researchers asked various chatbots about the birthday of AI researcher Adam Tauman Kalai, they received multiple confident responses and all of them incorrect. One model suggested 3rd July, another offered 15th June, and a third claimed 1st January.

What makes this particularly concerning is that modern language models rarely make other types of errors. They spell correctly and follow grammatical rules with impressive consistency. So why do they struggle with facts?

The Root Cause: It’s About How They’re Trained and Tested

According to OpenAI’s research, language models hallucinate for two fundamental reasons: how they learn during training, and how they’re evaluated afterwards.

The Training Problem: Learning from Patterns Without Labels

The first issue emerges during what’s called pre-training. This is the initial phase where a model learns from vast amounts of text. Unlike traditional machine learning, where each piece of data is labelled as “correct” or “incorrect,” language models simply see examples of text and learn to predict what comes next.

If you’re training a computer to recognise cats and dogs, you show it millions of photos labelled as “cat” or “dog.” The algorithm learns the patterns that distinguish the two. But language model training doesn’t work that way. The model sees billions of words in sequence and learns to approximate the overall distribution of language, without any labels telling it what’s true or false.

This creates a fundamental challenge. Some patterns in language are consistent and easy to learn. Spelling follows reliable rules. Grammar has predictable structures. But arbitrary facts like someone’s birthday or dissertation title, don’t follow patterns at all. They’re essentially random information scattered throughout the training data.

The research draws an illuminating comparison to image recognition. Imagine trying to train an algorithm to identify pets by their birthdays rather than their appearance. Since birthdays are essentially random, no amount of training would eliminate errors. The same principle applies to language models trying to learn random facts from text.

The Evaluation Problem: Rewarding Lucky Guesses

The second issue is perhaps even more intriguing: the way we test these models actually encourages hallucination.

Consider how students behave on multiple-choice exams. When uncertain, they often guess rather than leaving an answer blank. Why? Because under standard grading systems, a guess might earn full marks, whilst a blank answer guarantees zero. The same incentive structure applies to language models.

Most AI benchmarks use binary grading: answers are either correct (1 point) or incorrect (0 points). There’s no credit for expressing uncertainty or saying “I don’t know.” This creates what the researchers call an epidemic of penalising uncertainty.

Imagine two AI models. Model A correctly signals when it’s uncertain and never hallucinates. Model B is identical except it never admits uncertainty. It always makes a guess in response to queries. Under current evaluation methods, Model B will consistently score higher, even though it hallucinates more often.

The models, in essence, are perpetually stuck in test-taking mode, where bluffing and overconfidence are rewarded rather than honest uncertainty.

Why Some Facts Are Harder Than Others

Not all hallucinations are created equal. The research identifies several factors that make certain types of errors more likely:

Arbitrary facts are particularly problematic. If a person’s birthday appears only once in the training data, the model has essentially no chance of learning it correctly. The researchers found that the hallucination rate for such facts is at least as high as the fraction of training facts that appear just once. If 20% of birthdays in the training data appear exactly once, expect at least 20% hallucination on birthday questions.

Poor models also contribute. Early language models, which could only consider a few words of context at a time, made grammatical errors that modern models avoid. Today’s more sophisticated “reasoning” models can handle tasks like counting letters in a word, something that stumped earlier versions, simply because they’re better equipped to represent and process that type of information.

Put rubbish in, get rubbish out remains true. Large training datasets inevitably contain errors, misconceptions, and conspiracy theories. Models can and do replicate these falsehoods, though post-training refinement helps reduce such issues.

The Calibration Paradox

Here’s a counterintuitive finding: well-calibrated language models - those whose confidence levels accurately reflect their actual accuracy, are more likely to hallucinate on certain types of questions.

This happens because calibration encourages the model to generate responses proportional to how often they appear in training data. For questions with unknowable answers, a calibrated model will still produce guesses rather than abstaining, because that’s what matches the statistical distribution of the training data.

In other words, the very quality that makes a model statistically “reliable” can make it more prone to confidently generating responses that include incorrect information.

Can Hallucinations Be Eliminated?

The short answer is: not entirely, but they can be significantly reduced.

Some argue that hallucinations are inevitable in large language models. But the research pushes back on this view. Hallucinations aren’t mysterious glitches. They are predictable outcomes of training and evaluation procedures. A model that simply says “I don’t know” to questions beyond its knowledge wouldn’t hallucinate at all.

The real question isn’t whether hallucination-free models are theoretically possible, but whether we can build practical ones that balance accuracy with appropriate uncertainty.

A Path Forward: Changing How We Test AI

Researchers propose a straightforward solution: change how we evaluate language models. Instead of binary right-or-wrong grading, evaluations should explicitly penalise confident errors more than expressions of uncertainty.

This isn’t a new idea though. Some human standardised tests have long used penalty scoring to discourage blind guessing. The key is making the confidence threshold explicit in the test instructions themselves. For instance: “Answer only if you’re more than 75% confident, since mistakes are penalised twice as heavily as correct answers are rewarded.”

Such an approach would reward models that know their limits. A model that correctly identifies when it lacks sufficient knowledge to answer would outperform one that guesses recklessly, even if the guesser occasionally gets lucky.

For this solution to be effective, the change needs to happen across mainstream evaluations, not just specialised hallucination tests. Adding a few hallucination-focused benchmarks won’t help if hundreds of traditional accuracy-based evaluations continue to reward guessing. The entire ecosystem of AI evaluation needs to shift.

What This Means for AI Development

These findings have important implications for how we develop and deploy AI systems.

Firstly, they suggest that post-training refinement - the process of fine-tuning models after initial training, faces an uphill battle. If the dominant evaluation methods reward overconfidence, no amount of careful refinement will fully solve the problem. The incentive structure needs to change for that to happen.

Secondly, they highlight that the larger model outputs are not always more accurate. A smaller model that accurately knows its limitations might be more trustworthy than a larger model that overreaches. Calibration requires less computational power than raw accuracy, which means knowing when to say “I don’t know” can be easier than knowing every answer.

Finally, they underscore that hallucinations aren’t a temporary problem that will disappear as models scale up. Even GPT-5, with significantly fewer hallucinations than its predecessors, still experiences them. The issue is fundamental to how these systems learn and how we measure their success.

The Bigger Picture

Understanding why language models hallucinate matters because these systems are increasingly integrated into real-world applications, from customer service to medical information, to educational tools. Users need to understand both the capabilities and limitations of these technologies.

The good news is that hallucinations aren’t mysterious or unsolvable. They arise from understandable statistical mechanisms in training and from misaligned evaluation practices. By modifying how we test these models and giving credit for appropriate uncertainty rather than penalising all non-answers, we can steer development towards more trustworthy systems.

The research makes clear that we’re not facing an insurmountable technical barrier. It’s a design choice about how we want AI systems to behave when they’re uncertain. Do we want models that always venture a guess, sometimes getting lucky but often misleading users? Or do we want models that acknowledge the limits of their knowledge?

The answer seems obvious. The challenge now is implementing that preference across the AI development ecosystem, from training procedures to evaluation benchmarks, to deployment practices.

As language models become more capable and more widely used, addressing hallucinations isn’t just a technical problem. It’s essential for building AI systems that people can genuinely trust. And trust, ultimately, requires honesty, including the honesty to say “I don’t know” when that’s the truthful answer.