What Are AI Hallucinations?

You thought you understood technology pretty well? You'll feel like your brain needs a reboot once you go into the weird world of AI hallucinations.

Also, the head-scratching moments when your supposedly smart AI assistant confidently tells you that the Eiffel Tower is in Rome or generates an image of a horse with six legs. You'll become the squinting-at-the-screen-in-disbelief person you'd once declared you'd never be.

Understanding AI Hallucinations

Source: Legal Dive

AI hallucinations are when artificial intelligence systems generate content that seems plausible but is actually incorrect, fabricated, or nonsensical. It ain't just your regular computer error, but something much weirder.

Fact is: These hallucinations sneak up on you. You're still using the same AI tool you trust, so where did this completely made-up "fact" or bizarre image element come from? Sure, it could be partly due to you asking a complex question, but it's probably more likely due to fundamental limitations in how these AI systems work, or gaps in their training data, or the fact that they don't actually understand the world the way humans do.

What makes AI hallucinations different from regular errors is that the AI delivers these falsehoods with complete confidence. There's no "I think" or "probably"... just wrong information presented as absolute fact. And unlike a simple bug in traditional software that might crash your program, AI hallucinations can be subtle, convincing, and sometimes hard to spot unless you already know the correct information.

How Generative AI Models Work

Source: Pexels

At their core, generative AI models are built on artificial neural networks, specifically, a type called transformers for most modern text and image generators. Think of these networks like a massive web of interconnected nodes, loosely inspired by how neurons work in your brain. Each connection has a "weight" that strengthens or weakens signals passing through the network.

During training, these weights get adjusted as the AI processes millions or billions of examples. For language models, that's text from books, articles, websites, and other sources. 

For image models, it's millions of images paired with text descriptions. The AI learns to recognize patterns in this data without explicitly being told what those patterns are.

Prediction Over Precision

The training of these models isn't just reading a few books or looking at some pictures. A single large language model might cost millions of dollars in computing resources to train.

During this process, the AI learns to:

• Recognize patterns in language or images
• Understand relationships between concepts
• Develop a statistical model of how words or visual elements typically appear together
• Map connections between different ideas, objects, or descriptions

The AI doesn't understand any of this in the human sense. It doesn't know what words mean or what objects are. It just recognizes statistical patterns. If the word "dog" frequently appears near words like "bark," "pet," and "loyal" in its training data, it learns those associations without understanding what a dog actually is.

Tokens: The Building Blocks of AI Understanding

You might have heard tech YouTubers talk about "tokens" when discussing AI. A token is basically a piece of text, often a word or part of a word, that the AI processes as a unit. When you interact with ChatGPT or similar models, your input gets broken down into these tokens.

For example, "The cat sat on the mat" might be tokenized as ["The", "cat", "sat", "on", "the", "mat"]. But more complex or less common words might be split into multiple tokens. The word "hallucination" might become ["hall", "ucin", "ation"].

This tokenization process is part of why AI sometimes struggles with specialized vocabulary, rare names, or words in languages it wasn't primarily trained on. If it hasn't seen those tokens often enough during training, it has less reliable patterns to work with.

Gaps in Training Data

Source: Devopedia

When you ask an AI to generate text or an image, it's essentially playing a probability game. For each position in the sequence it's generating, the model asks itself: "Based on everything I've seen before, what's most likely to come next?"

For text generation, this means:

1. You provide a prompt
2. The AI tokenizes your prompt
3. For each new token it generates, it calculates probabilities for all possible next tokens
4. It selects one (using various sampling methods that balance predictability and creativity)
5. It adds that token to the sequence
6. It repeats steps 3 through 5 until it completes the response

This process happens incredibly quickly. Modern AI can generate thousands of words per minute. But it's still fundamentally making a series of predictions based on patterns, not retrieving facts from a database or reasoning from first principles.

The Temperature Knob: Controlling Creativity vs. Accuracy

Many AI systems have a setting called "temperature" that controls how random or predictable the output will be. A low temperature (closer to 0) makes the AI more deterministic and it will consistently pick the most probable next token. This tends to produce more factual, conservative outputs but can be repetitive.

A high temperature (closer to 1 or above) introduces more randomness, making the AI more likely to select less probable tokens. This produces more creative, varied outputs, but also increases the chance of hallucinations.

This temperature setting is why the same AI can sometimes give you a straightforward, accurate answer and other times go off on creative tangents that include made-up information.

Attention Mechanisms: How AI Keeps Track of Context

A breakthrough that made modern AI possible is something called the "attention mechanism." This allows the AI to weigh the importance of different parts of your prompt when generating each part of its response.

For example, if you ask "Who was the first person to walk on the moon, and what did they say when they got there?" the attention mechanism helps the AI connect "they" with "first person to walk on the moon" when generating the second part of the answer.

But attention has limits. In very long contexts, the AI might "forget" details from earlier in the conversation or misattribute information. This is another source of hallucinations. The model might blend different pieces of information together incorrectly.

Multimodal Models: Bridging Text and Images

The newest generation of AI systems can work with multiple types of data: text, images, and sometimes audio or video. These "multimodal" models learn connections between different forms of information.

For example, DALL-E, Midjourney, and Stable Diffusion learn to associate text descriptions with corresponding images. When you give them a prompt, they generate images that match the text based on these learned associations.

But this process introduces new opportunities for hallucinations. If the AI hasn't seen enough examples of certain visual concepts or how they relate to textual descriptions, it might generate images with physically impossible features or misunderstand what you're asking for.

Misalignment Between Prompt and Intent

I recently read an interesting paper titled The Need for Combining Implicit and Explicit Communication in Cooperative Robotic Systems, that suggests a significant portion of human communication is explicit, while much more relies on shared knowledge, assumptions, and context. But AI systems often struggle to access and interpret this implicit information.

For instance, if you ask "How do I get to Central Park?" you're implicitly asking for directions from your current location to Central Park in New York City. You're not asking about Central Parks in other cities, or how the park was historically created, or the philosophical concept of "getting to" a destination.

Humans automatically fill in these gaps. AI tries to, but often gets it wrong because it doesn't share your specific context and background knowledge.

Attention Misalignment

Even if your prompt contains all the necessary information, the AI might not weigh different parts of it the way you intended. Its attention mechanism might focus on aspects you consider minor while glossing over what you see as the main point.

For example, if you ask "What are the health benefits of running compared to swimming for someone with knee problems?" the AI might focus primarily on comparing running and swimming generally, rather than specifically addressing the knee problems aspect that might be your main concern.

This attention misalignment often happens because:

• The AI's training data contained more information about certain aspects of your query
• The structure of your prompt inadvertently emphasized certain elements
• The AI's architecture has biases in how it processes different types of information

Real-World Examples of AI Hallucinations

In Text

Source: Reddit

Know that weird feeling when you read something that sounds totally legit but turns out to be complete nonsense? AI text hallucinations might not seem as concerning at first, and the occasional made-up fact might seem harmless.

A MIT psychological study of AI users showed that their ability to spot fabricated information was reduced when it came from a seemingly authoritative sources. When given basic factual questions to research, the average person accepted AI responses at face value, even if those responses were entirely fabricated.

Citation hallucinations are particularly troubling. This is when an AI confidently cites sources that don't exist or misrepresents what a real source says. For example, in 2023, a lawyer used ChatGPT to prepare a legal brief and submitted it to a court with completely fabricated case citations. The judge caught the issue and sanctioned the lawyer for submitting false information.

Other examples include:

• Google's Bard AI (now Gemini) incorrectly claimed the James Webb Space Telescope took the first pictures of exoplanets in 2023
• ChatGPT has been caught inventing detailed biographies for fictional people
• AI systems regularly make up statistics, studies, and survey results that sound plausible but don't exist

These text hallucinations are dangerous because they're often mixed with accurate information, making them harder to spot.

In Images

Even though they looked convincing at first glance, most of them contain subtle anatomical errors. Whether it's the extra fingers on hands, the misaligned facial features, the physically impossible reflections, or all of the above, visual hallucinations can be par for the course.

Common image hallucinations include:

• Humans with too many (or too few) fingers, teeth, or limbs
• Text that starts readable but devolves into gibberish
• Physically impossible architectural features
• Objects that blend into each other unnaturally
• Inconsistent lighting or shadows

These issues occur because image generation AI doesn't understand physical reality – it's just trying to match patterns it's seen in its training data. When it needs to generate details it hasn't seen enough examples of (like the correct number of fingers on a hand in various positions), it hallucinates based on incomplete pattern recognition.

In Audio

I recently heard an AI-generated audio clip that suggests voice cloning technology has no problem replicating celebrity voices. But the words coming out of these synthetic mouths acknowledged a degree of hallucination when it came to accuracy.

Audio hallucinations can take several forms:

• Voice cloning technology saying things a person never said
• AI music generators creating "new songs" by famous artists that sound authentic but were never created by them
• Speech-to-text systems hallucinating words that weren't actually spoken
• Text-to-speech systems mispronouncing words or adding unintended emphasis

In 2023, an AI-generated song that sounded remarkably like Drake and The Weeknd went viral. Neither artist had anything to do with it, but the voice cloning was convincing enough that many listeners believed it was authentic.

If you’re curious about the song, here it is:

Ghostwriter - Heart on my sleeve Drake ft. The Weeknd (Remastered AI Song) BUKO Bass Boosted

These audio hallucinations raise serious concerns about misinformation, especially as voice cloning technology becomes more accessible and realistic.

How to Reduce AI Hallucinations

You thought dealing with a toddler's imagination was wild? Wait till you meet an AI on a hallucination bender. One minute you're asking for a simple summary of World War II, the next you're reading about battles that never happened and generals who never existed. 

And the AI delivers this nonsense with the confidence of your know-it-all uncle after three beers at Thanksgiving dinner. It's enough to make you question everything the machine tells you.  

High-Quality, Diverse Training Datasets

The foundation of reducing hallucinations starts with better training data. AI systems can only be as good as the information they learn from.

Improvements in this area include:

• More diverse sources to reduce cultural and geographical bias
• Better fact-checking of training materials
• Regular updates to include new information
• Balanced representation of topics and viewpoints
• Clearer labeling of opinion versus fact in training materials

Companies developing AI are increasingly focused on curating higher-quality datasets rather than just maximizing the quantity of training data. This helps reduce the garbage-in-garbage-out problem that contributes to hallucinations.

Clear Prompt Engineering

You thought you could just type whatever pops into your head and the AI would magically understand? That's like expecting your dog to fetch the exact stick you're thinking about without pointing to it. The way you talk to these AI systems matters more than most people realize.

Effective prompt strategies include:

• Being specific about what you're asking
• Providing relevant context
• Explicitly requesting verification of facts
• Breaking complex questions into smaller parts
• Asking the AI to explain its reasoning step by step

For example, instead of asking "Tell me about the Battle of Hastings," you might get better results with "Provide the date, location, key figures, and outcome of the Battle of Hastings based on historical consensus. If there are any disputed facts, please note them."

Model Fine-Tuning and Guardrails

AI developers are implementing various technical approaches to reduce hallucinations:

• Retrieval-Augmented Generation (RAG): This technique connects language models to external knowledge bases, allowing them to look up information rather than relying solely on their training data.
• Constitutional AI: Systems are trained to follow certain principles, including not making up information when they don't know something.
• Self-Consistency Checking: Some models are now designed to check their own outputs for internal contradictions or implausible claims.
• Uncertainty Quantification: Advanced models can be trained to express levels of confidence in their responses, indicating when they're less certain.
• Fine-Tuning with Human Feedback: Models can be further trained on examples of good (non-hallucinated) and bad (hallucinated) responses, helping them learn to avoid hallucinations.

These technical approaches are constantly evolving as researchers better understand the causes of hallucinations.

Human-in-the-Loop Validation

You know that friend who always checks the restaurant bill and finds the extra drink they tried to charge you for? We all need to be that friend when using AI.

No matter how smart these computer brains get, they still mess up in ways that'll make you spit your coffee across the room. Like generating a "historical photo" of Abraham Lincoln riding a dinosaur or claiming that Australia declared war on emus. (OK, that last one actually happened, but you get my point.) 

Effective human validation includes:

• Fact-checking important information from multiple sources
• Having subject matter experts review AI-generated content in their domains
• Implementing editorial processes for AI-generated public-facing content
• Creating feedback loops where human corrections improve the AI system
• Clearly labeling AI-generated content so people know to apply appropriate skepticism

Many organizations are developing hybrid workflows where AI generates initial content that humans then review and refine, combining the efficiency of AI with human judgment and expertise.

Staying Aware of AI Hallucinations

Have you ever noticed how your smartphone tries to finish your sentences when you text? Sometimes it's spot-on, other times it's suggesting words so crazy you wonder if your phone's been drinking. Well, AI hallucinations are like that, but way worse and way more convincing.

Here are some practical tips for everyday AI users:

1. Verify important information from multiple reliable sources, especially for decisions that matter.
2. Be especially skeptical of specific statistics, quotes, citations, or detailed historical claims from AI systems.
3. Look for telltale signs of hallucination like overly confident language about obscure topics or perfectly convenient "facts" that align too neatly with what you were hoping to hear.
4. Use AI as a starting point, not the final word. Think of AI outputs as first drafts or suggestions rather than authoritative answers.
5. Report hallucinations to the AI providers when you spot them. This feedback helps improve the systems.
6. Stay updated on AI capabilities and limitations as the technology rapidly evolves.
7. Demand transparency from AI companies about how they're addressing hallucinations and what guardrails they've implemented.

The responsibility for managing AI hallucinations falls on multiple shoulders. AI developers need to build more reliable systems with better safeguards. Organizations deploying AI need appropriate human oversight and clear policies. And users need the awareness and skills to critically evaluate AI outputs.

As these systems become more integrated into our daily lives, our relationship with information is fundamentally changing. We're entering an era where the ability to distinguish fact from convincing fiction is more important than ever.

The good news is that awareness of the hallucination problem is growing, and substantial research is being directed toward solutions. The next generation of AI systems will likely be more reliable, more transparent about their limitations, and better at distinguishing what they know from what they don't.

Until then, a healthy dose of skepticism and some good old-fashioned fact-checking remain your best defense against being misled by an AI that's just a little too confident in its own hallucinations.