I recently discussed with a colleague some experiences of using large models in production environments. He said that DeepSeek has serious hallucinations. The answer to the same prompt word may be different each time. In bad cases, the difference is quite large. The experience is poor for online applications (users come to use your function with relatively high expectations. If the stability is not good, it is a waste of users' time).

I said, "This is just like the Vincent picture model when it first came out, like the Stable Diffusion card drawing. If you're lucky, you can draw one or two good pictures, but if you're unlucky, you'll get all kinds of deformed pictures."

So what exactly is  the large model illusion  ? Don't panic, today we will explain it in plain language: What is the illusion? Why does it happen? What are the effects? How to solve it?

What is hallucination? AI's "wild imagination"

Simply put, the big model hallucination is that the content generated by AI looks very similar to the original, but it is actually wrong, made up, or even completely outrageous. It is like  AI is "dreaming" and taking its own imagination seriously.
• For example: If you ask AI: "Who invented the light bulb?" It may say: "Edison invented it in 1879, but in fact he also referred to the technology of Martians." The first half of the sentence is correct, but the second half is pure nonsense.

Imagine AI is a super confident "storyteller" who talks a lot, but sometimes the stories are mixed with some "self-created plots"

Why do hallucinations occur? Let’s start with training

Large models (such as Deepseek or ChatGPT) are trained with massive amounts of data. Their goal is to learn  the rules of language and predict "what to say next". But the training process is a bit like teaching a child to tell a story, and it is not always reliable. The main sources of illusions are as follows:
•  Imperfect data : There may be errors, rumors, or incomplete information in the training data. AI may repeat these "impurities" after learning them. For example, there is a fake news online saying "There is Wi-Fi on the moon", and AI may take it seriously.
•  Overconfidence : Large models are designed to be as smooth and coherent as possible, and sometimes they would rather make up an answer than say "I don't know". This is like a student who doesn't know how to answer a question in an exam, so he has to write something random.
•  Probability game : AI generates answers based on probability. For example, if you ask "What is an apple", it knows that "fruit" has the highest probability, but it may also go off track and talk about "Apple" or even "magic apple".
•  Blurred knowledge boundaries : Large models learn language patterns, not really understand the world. Ask it questions about the future or unpopular issues, it has no real evidence, and can only rely on "imagination" to make up.

Training is like asking AI to read a whole library of books, but the books are a mixture of true and false, and AI does not have a keen eye, so it cannot tell which is "Journey to the West" and which is "Science Magazine"

What are the effects of hallucinations? There are pros and cons

Hallucinations sound like a big problem, but the impact depends on the scenario:

harm:

◦  Misleading users : For example, if you ask for medical advice and AI says “drinking coffee can cure a cold”, that’s a problem.
◦  Propagation error : If the illusion is taken seriously, it may become a rumor.
◦  Reduce trust : If it keeps talking nonsense, users may think “AI is unreliable”.

Benefits (huh? Really!):

◦  Creative spark : In scenarios such as writing novels and painting, hallucinations are actually "inspirations". For example, let AI make up a science fiction story, and the Martian technology is quite exciting.
◦  Fill in the blanks : Sometimes when there is not enough data, AI can make do with hallucinations, such as generating a hypothetical advertising copy.

Tips: Hallucination is like a double-edged sword of AI . The key is what you use it for. You can be free when writing poetry, but be careful when looking up information. For scenes with high rigor, hallucination will bring bad results; for scenes with divergent creativity, hallucination will bring surprises .

How to reduce hallucinations? A few tips

Although hallucinations cannot be completely eliminated (after all, AI is not human and cannot "really understand"), there are ways to make it less confused:
•  Better data : Feed AI with cleaner, more authoritative data, and fewer rumors and jokes.
•  Clear prompts : The more precise your questions, the less likely the AI ​​will go astray. For example, instead of asking "What about the future?", try "Based on the trends in 2024, predict the development of technology in 2025."
•  Proofreading mechanism : Let the AI ​​check its own answers, or add a "confidence score" (such as "I am 80% sure this is correct").
•  Human supervision : In important scenarios (such as medical and legal), people have to verify the output of the AI ​​again.
•  Admit ignorance : Engineers can adjust the model to make it more willing to say "I don't know" instead of making it up.

Reducing hallucinations is like giving AI "anti-slip glasses" to help it see the road clearly and not get stuck in its imagination.

What can you do? Be an "AI detective"

As a user, you can also help AI make fewer mistakes:
•  Ask more questions : AI may give different answers each time. Compare them and the mistakes will be exposed.
•  Verify the answer : Don’t trust AI completely. Google it or check it in a book.
•  Give feedback : You can tell AI, “Hey, this answer is wrong. Please correct it.” AI will learn to correct it.

Conclusion: Hallucinations are a side effect of AI

To sum up, big model hallucination is a phenomenon in which AI deviates from the norm and makes up stories when generating content. The root cause lies in the impurities in the training data, the probabilistic nature of the model, and the fuzziness of the knowledge boundary . It can be misleading or creative, depending on the scenario.

Understanding the logic behind hallucinations will help us apply AI more rationally.