An AI hallucination is a phenomenon where chatbots or other tools based on a large language model powered by artificial intelligence present false or misleading information as fact. In essence, the AI uses language patterns to create its outputs rather than “understanding” the meaning behind the words. This means it can sometimes present completely incorrect information in a plausible-sounding way.
These factual errors are possible because of the underlying technology, the LLM. The AI “learns” by analyzing large amounts of data gathered online. It pinpoints patterns and learns to accomplish one thing — predict the next word in a specific sequence. In the simplest of terms, it works like a powerful autocomplete tool.
Because the internet is overflowing with inaccurate information, the generative AI learns to repeat them, even if they are entirely fabricated. But more than that, the LLM can also make things up seemingly from thin air. The AI is trained to create new text but relies on combining patterns, which can sometimes manifest in unpredictable ways. In other words, even if the AI teaches itself using reliable info, its generated content may still be false.
One problem is that we don’t understand exactly how these tools work. With the help of deep learning technology and machine learning, generative AI models train themselves and learn from massive amounts of data, something no human could ever hope to analyze. Due to this, not even AI experts can say exactly why an AI tool creates a specific text sequence at a particular moment.
Knowing that the issue arises from the underlying technology is helpful. Still, specific AI hallucinations in generative AI tools like Google’s Bard or OpenAI’s ChatGPT might be caused by a wide range of reasons, including:
AI hallucinations come in many forms, so here are some of the more common types of AI hallucinations:
Several issues can arise from AI hallucinations, including:
The good news is that companies responsible for the most popular AI models, like Google, Microsoft, and OpenAI, are already working on solving or reducing the number of instances in which AI hallucinations appear. For example, OpenAI uses feedback from human testers to refine ChatGPT’s responses.
Aside from companies creating AI models working to improve them and reduce AI hallucinations, we, as users, can also prevent AI hallucinations to a degree. Most of these techniques have to do with prompt engineering. In other words, these are strategies for writing prompts that make the AI models less likely to hallucinate. Let’s review some techniques you can use.
You need to write precise and clear prompts to get the response you’re looking for. Avoid ambiguity, and don’t leave room for the AI model to provide different types of outputs.
Give the AI relevant data sources with your prompt and a role to perform.
For example, don’t just ask it to write a piece on technology — tell it to act as an author of a technology blog and, as such, write an article about a specific device or system.
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When you pose a complex question to most AI models, they may hallucinate in trying to answer this multi-pronged question in a single step.
To avoid this issue, you should break down your query into several smaller ones, each requiring a more straightforward answer.
This practice can often lead to the AI providing more accurate responses because it needs to answer several prompts before reaching the final one and collect information along the way.
If you need the AI tool to give you some suggestions on handling a situation, you should give it more information about that problem. By providing more details, you’ll get the AI to understand the context better.
Suppose you’re looking for a specific piece of information or an explanation based on a particular source. In that case, you can always structure the prompt as “According to [insert book, author, or other relevant authority], what is [insert problem or situation]…”
To avoid inconsistent, inappropriate, illogical, or contradictory responses, you can impose constraints on the AI and give it rules to follow in its output generation.
For example, if you want a chatbot to write an article on email phishing, don’t just say that. Tell the AI how many words the article should have, who the audience is, the format you want the piece to have, and what type of site you want it to appear on. The more specific the prompt, the better.
AI models are tools that respond to your prompts, so use that to your advantage. Tell it precisely the type of answer or information you’re looking for and what you’re not looking for.
This technique is especially useful after a period of actively using the AI, so you can start anticipating how it will answer and react preemptively.
AI hallucinations are an inherent part of the AI models we have and use today. We don’t fully understand how and why they arise and if there is a “fix” their creators and developers can implement to solve them for good.
AI is still a developing technology, and if you want to use AI chatbots and other LLM-based tools, you should do everything you can to understand the problems it poses. That’s why you need to be aware of AI hallucinations and do your best to identify them in practice. Consider our prevention tips and try to write better prompts to guide the tool toward producing truthful and meaningful outcomes. Most importantly, don’t ever rely on AI systems to be 100% accurate — and verify every piece of information they provide.
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