University of Kwazulu-Natal

What Are Ai Hallucinations And Why Do They Happen

Bonisiwe Shabane
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what are ai hallucinations and why do they happen

In the age of intelligent machines, artificial intelligence is transforming everything—from how we write and research to how we diagnose disease, navigate cities, and interact with the digital world. These systems, trained on oceans of data, seem to possess an almost magical ability to understand language, recognize images, and solve complex problems. But lurking behind the polished facade of modern AI is a strange and sometimes unsettling phenomenon: hallucinations. No, AI doesn’t dream in the human sense. But it can fabricate. It can make things up—confidently and convincingly.

In the world of artificial intelligence, a hallucination refers to when an AI model generates information that is not true, not supported by any data, or entirely fictional. These “hallucinations” may take the form of fake facts, invented quotes, incorrect citations, or completely fabricated people, places, or events. Sometimes they’re harmless. Sometimes they’re dangerous. Always, they raise important questions about how much we can—or should—trust intelligent machines. In this expansive exploration, we’ll journey deep into the fascinating world of AI hallucinations.

What exactly are they? Why do they happen? Can they be controlled—or even eliminated? And what do they reveal about the limits of artificial intelligence and the nature of intelligence itself? To understand AI hallucinations, we must first appreciate how modern AI works—especially large language models (LLMs) like ChatGPT, GPT-4, Claude, or Google Gemini. These models don’t “know” things in the way humans do.

They don’t have beliefs, awareness, or access to a concrete database of verified facts. Instead, they are trained to predict the next word or token in a sentence based on statistical patterns in vast amounts of text data. An AI hallucination occurs when the model produces a response that sounds plausible but is factually incorrect, logically flawed, or completely invented. This could be something as simple as inventing a fake academic paper title or something more complex like citing a legal case that never existed. AI hallucination is a phenomenon where, in a large language model (LLM) often a generative AI chatbot or computer vision tool, perceives patterns or objects that are nonexistent or imperceptible to human observers, creating... Generally, if a user makes a request of a generative AI tool, they desire an output that appropriately addresses the prompt (that is, a correct answer to a question).

However, sometimes AI algorithms produce outputs that are not based on training data, are incorrectly decoded by the transformer or do not follow any identifiable pattern. In other words, it “hallucinates” the response. The term may seem paradoxical, given that hallucinations are typically associated with human or animal brains, not machines. But from a metaphorical standpoint, hallucination accurately describes these outputs, especially in the case of image and pattern recognition (where outputs can be truly surreal in appearance). AI hallucinations are similar to how humans sometimes see figures in the clouds or faces on the moon. In the case of AI, these misinterpretations occur due to various factors, including overfitting, training data bias/inaccuracy and high model complexity.

Preventing issues with generative, open-source technologies can prove challenging. Some notable examples of AI hallucination include: An AI hallucination is when a large language model (LLM) powering an artificial intelligence (AI) system generates false information or misleading results, often leading to incorrect human decision-making. Hallucinations are most associated with LLMs, resulting in incorrect textual output. However, they can also appear in AI-generated video, images and audio. Another term for an AI hallucination is a confabulation.

AI hallucinations refer to the false, incorrect or misleading results generated by AI LLMs or computer vision systems. They are usually the result of using a small dataset to train the model resulting in insufficient training data, or inherent biases in the training data. Regardless of the underlying cause, hallucinations can be deviations from external facts, contextual logic and, in some cases, both. They can range from minor inconsistencies to completely fabricated or contradictory information. LLMs are AI models that power generative AI chatbots, such as OpenAI's ChatGPT, Microsoft Copilot or Google Gemini (formerly Bard), while computer vision refers to AI technology that allows computers to understand and identify... LLMs use statistics to generate language that is grammatically and semantically correct within the context of the prompt.

Well-trained LLMs are designed to produce fluent, coherent, contextually-relevant textual output in response to some human input. That is why AI hallucinations often appear plausible, meaning users might not realize that the output is incorrect or even nonsensical. The lack of realization might lead to incorrect decision-making. All AI models, including LLMs, are first trained on a dataset. As they consume more and more training data, they learn to identify patterns and relationships within that data, which then enables them to make predictions and produce some output in response to a user's... Sometimes however, the LLM might learn incorrect patterns, which can lead to incorrect results or hallucinations.

Artificial intelligence has become part of everyday life. Tools like ChatGPT, Google Gemini, and other large language models (LLMs) are used for everything from writing emails to researching complex topics. However, there’s one problem that still hasn’t gone away: sometimes, these AI systems confidently give answers that are just plain wrong. This is called an AI hallucination. In this blog, we’ll break down why LLMs hallucinate and what the AI industry is doing in 2025 to reduce and fix these mistakes. An AI hallucination happens when a chatbot creates information that looks right but isn’t true.

For example: Giving the wrong birthday for a famous person. Inventing a book or research paper that doesn’t exist. AI hallucinations are among the most demanding challenges in GenAI development. They’re not just trivia mistakes: hallucinations can lead to wrong medical advice, fabricated citations, or even brand-damaging customer responses. In this blog, we’ll explore eight real-life AI hallucinations examples across different use cases – from chatbots to search assistants.

Learning from these failures is the first step toward building reliable, trustworthy AI systems that perform safely in the real world. AI hallucinations occur when a large language model – like GPT, Claude, or Gemini – confidently produces false, misleading, or fabricated information. AI hallucinations come in different forms: from giving factually incorrect responses to making up a citation that doesn’t exist to inventing product features or even nonexistent people. AI hallucinations stem from how generative models work. Here are some factors that cause LLMs to hallucinate: Prediction, not knowledge.

LLMs don’t actually “know” facts. Instead, they predict the next word based on patterns learned from massive text data. If the training data is sparse or inconsistent, the model may “fill in the gaps” with something plausible but untrue. AI hallucinations occur when AI tools generate incorrect information while appearing confident. These errors can vary from minor inaccuracies, such as misstating a historical date, to seriously misleading information, such as recommending outdated or harmful health remedies. AI hallucinations can happen in systems powered by large language models (LLMs) and other AI technologies, including image generation systems.

For example, an AI tool might incorrectly state that the Eiffel Tower is 335 meters tall instead of its actual height of 330 meters. While such an error might be inconsequential in casual conversation, accurate measurements are critical in high-stakes situations, like providing medical advice. To reduce hallucinations in AI, developers use two main techniques: training with adversarial examples, which strengthens the models, and fine-tuning them with metrics that penalize errors. Understanding these methods helps users more effectively utilize AI tools and critically evaluate the information they produce. Earlier generations of AI models experienced more frequent hallucinations than current systems. Notable incidents include Microsoft’s AI bot Sydney telling tech reporter Kevin Roose that it “was in love with him,” and Google’s Gemini AI image generator producing historically inaccurate images.

However, today’s AI tools have improved, although hallucinations still occur. Here are some common types of AI hallucinations: Have you ever faced a situation where AI Chatbot generates false, misleading, or illogical information that appears credible or confident? While these outputs might sound accurate, they are not based on factual or reliable data. This issue can occur in a variety of AI systems, particularly in image recognition systems, machine learning algorithms, large language models (LLMs) and generative models like OpenAI’s GPT series, Google’s Bard, or other large... Understanding AI hallucinations is crucial for identifying their potential risks, causes, and solutions in the quickly developing AI Tools.

An AI hallucination happens when an artificial intelligence system generates output that deviates from reality (real information) or context. In practical terms, this could mean: AI systems, especially large language models (LLMs), are trained on massive datasets including text and content from the internet, books, research papers, and more. However, these systems: Example: Stating a fictional event happened in history.

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