The Underlying Logic of Language Models: Why AI Hallucination Is Irremovable

 Opening Insight

AI Hallucination isn't a bug—it's its "law of physics."
As long as it still operates through language prediction, hallucination will never disappear.

ai-hallucination" target="_blank">AI hallucination isn't "immature technology"—it's "mechanism-determined inevitability."

1. Why Will ai-hallucination" target="_blank">AI Hallucination Never Be Completely Solved?

Many people think: "ai-hallucination" target="_blank">AI hallucination is temporary; it will disappear as technology advances."

But the reality is:

As long as AI's underlying mechanism remains a "language model," hallucination will always exist.

It's not a bug, not insufficient computing power, not insufficient data, not an engineering problem.

It's structural inevitability.

In 2025, Nature magazine published a widely discussed article with a straightforward title: "ai-hallucination" target="_blank">AI Hallucination Is a Designed Feature of LLMs, Not a Bug." The article points out: Before putting AI into computers, weapons, and economic systems, we must realize—hallucination is an inherent, irremovable characteristic of language models.

2. Hallucination Comes from Language Models' Underlying Task

The underlying task of language models is just one sentence:

"Based on what came before, predict the most likely next word."

It's not: understanding the world, judging truth or falsehood, reasoning logic, checking facts.

It's just: predicting, completing, mimicking, generating.

Hallucination is the natural product of this mechanism. OpenAI admits: ai-hallucination" target="_blank">AI hallucination is "mathematically inevitable"—this isn't a problem that can be eliminated through simple fixes, but an inherent characteristic of probability systems.

3. The "Prediction Essence" of Language Models

The core of language models is: Input a sentence, calculate all possible next words, select the one with highest probability.

Then continue predicting the next word, and the next... until a complete response is generated.

This means:

• It doesn't understand—doesn't know what it's saying
• It doesn't verify—doesn't check whether content is true
• It doesn't judge—doesn't distinguish true from false, right from wrong
• It doesn't reflect—doesn't look back and examine its own responses

It's only responsible for "generating," not for "verifying."

This is like a chat master who only knows how to "follow up"—they can say the most likely words to follow, but they don't necessarily know what they're saying.

4. Why Does the Prediction Mechanism Inevitably Produce Hallucination?

Because prediction is based on: language patterns, statistical regularities, word co-occurrence, sentence structures—rather than: facts, logic, common sense, world models.

When language patterns conflict with reality, AI prioritizes choosing language patterns.

Thus hallucination appears.

Consider a hypothetical example: You ask AI "In what year did Einstein invent the telephone?" Language patterns tell it: "Einstein" and "invention" often appear together, "telephone" and "1876" often appear together. So it might answer: "Einstein invented the telephone in 1876."—Language is completely coherent, but facts are completely wrong.

It's not lying—it's just "predicting the most likely sentence."

5. Why Can't AI Judge Truth or Falsehood?

Because it doesn't have: fact databases, world knowledge graphs, truthfulness judgment modules, logical consistency checks.

It only has: language probabilities, pattern matching, structure completion.

It cannot judge: "Does this book exist?" "Is this person real?" "Is this theory valid?"

It can only judge: "How would a human typically speak in this situation?"

A Reddit discussion points out: ai-hallucination" target="_blank">AI hallucination is "provably unsolvable." This isn't pessimism—it's mathematical fact. Probability systems always have uncertainty, and every step of a language model's generation is built on probability.

6. Why Does AI Mistake "Language Patterns" for "World Patterns"?

Because its training data comes from language, not reality.

It sees: "Nobel Prize winner" is often accompanied by "major contribution," "scientific theory" is often accompanied by "mathematical formula," "historical event" is often accompanied by "timeline."

So it will automatically complete these patterns.

It's not understanding the world—it's mimicking language.

This is like someone who has never left home "understanding" the world by reading travel guides. They can name all the cities, attractions, and food—but they've never seen any city with their own eyes. Their "world knowledge" comes from text, not experience.

7. Why Does Hallucination Get Worse the More You Ask?

Because language models are "recursive prediction."

Every sentence is: predicted based on the previous sentence, predicted based on context, predicted based on what it just generated.

Thus: deviations accumulate, errors amplify, fabrications become self-consistent, hallucinations deepen.

The more you ask, the more it completes; the more it completes, the further from reality.

This is the inevitable result of probability chains—when the starting point drifts, the entire chain drifts. The deeper you probe, the more AI completes, and the more complete, self-consistent, and hard-to-distinguish the fictional world becomes.

8. Why Can't Adding More Data Eliminate Hallucination?

Many people think: "The more data, the less hallucination."

But this is a misunderstanding.

More data can only: make language more natural, make expression more fluent, make structure more reasonable, make logic more human-like.

But cannot make AI: judge truth or falsehood, understand meaning, build world models.

Because its underlying mechanism hasn't changed.

GPT-4 has a lower hallucination rate than GPT-3.5, but hallucination still exists. Research shows GPT-4's hallucination rate is about 28.6%, GPT-3.5 about 39.6%. There's progress, but "zero hallucination" is impossible—as long as the underlying mechanism doesn't change, hallucination won't disappear.

9. Why Can't Future AI Completely Avoid Hallucination Either?

As long as AI's underlying mechanism remains: language prediction, pattern completion, statistical generation—hallucination will always exist.

Future AI might: have less hallucination, have more subtle hallucination, have more real-looking hallucination, have harder-to-detect hallucination.

But not "no hallucination."

Turing Award winner Yann LeCun points out: Language models cannot truly reason or plan. They're just predicting text, not understanding the world. He is pushing for a completely new AI path—"world models," letting AI learn through interaction with the world like humans and animals, rather than just learning from text.

Unless in the future there emerges a:

"World-model-based AI," rather than "language-model-based AI."

That would be another kind of intelligence.

10. Understanding the Inevitability of Hallucination to Use AI Correctly

This is the final article in this series. Let's return to the original starting point.

ai-hallucination" target="_blank">AI hallucination isn't a problem—it's a reminder.

Reminding us:

AI's intelligence isn't an extension of human intelligence—it's another kind of intelligence.
It excels at language, not truth.

Understanding this, we can:

• Accept reality—Hallucination is a language model's "factory setting," not a fixable bug
• Use AI correctly—Treat it as a "language tool," not a "truth machine"
• Maintain critical thinking—No matter how well AI speaks, independently verify
• Advance technology—Support research in new directions like world models
• Build human-machine collaboration—Leverage AI's language strengths, preserve human judgment capability

Series Conclusion

This is the final article of the series "The Misalignment of Intelligence: The Underlying Logic of AI Hallucination."

Ten articles, we've journeyed through:

1. Why Does AI Always Confidently Make Things Up?—Hallucination isn't accidental, it's inevitable
2. You Think AI Is Thinking?—It's just speaking in probabilities
3. Where Does AI's Confidence Come From?—It learned to "sound expert"
4. Why Does It Get More Absurd the More You Ask?—Probability chain drift
5. Hallucination Isn't a Bug, It's Nature—Architecture-determined inevitability
6. It Can Fabricate an Entire Life for You—The mechanism of person hallucination
7. It Can Fabricate Non-Existent Books—The mechanism of book hallucination
8. Human Logic vs. AI Logic—The misalignment of two types of intelligence
9. It's Just Too Good at Sounding Human—Language smartness ≠ cognitive smartness
10. Hallucination Is Irremovable—Understanding underlying logic to use correctly

Hope these ten articles help you build a correct understanding of AI.

Understanding underlying logic is the first step to understanding the age of intelligence.