Hallucinations in the context of Artificial Intelligence (AI) have drawn significant attention, particularly when discussing Large Language Models (LLMs) like GPT-3, GPT-4, and others. But what does “hallucination” mean in this case? AI hallucination refers to the phenomenon where an LLM generates information that is either factually incorrect, nonsensical, or entirely fabricated. This often happens even when the model’s responses seem highly confident and believable.
What Causes Hallucination in LLMs?
LLMs like GPT are trained on massive amounts of text data, but they don’t “understand” the information the way humans do. These models generate text based on probabilities, determining the next word in a sequence by analyzing patterns in the training data. While this mechanism works impressively for many applications, it also creates room for hallucination due to several reasons:
- Training Data Limitations: Even though LLMs are trained on enormous datasets, they might still encounter gaps or inconsistencies. When the model tries to answer a question it has limited data for, it may generate information that sounds plausible but is inaccurate.
- Ambiguous Prompts: Vague or unclear user inputs can lead the model to “fill in the blanks” with fabrications. If the prompt is open-ended, the model might invent information to continue the conversation or provide an answer, even when no factual basis exists.
- Overgeneralization: The model learns general language patterns, which can sometimes lead it to overgeneralize or apply certain information inappropriately. This might lead to factual errors or entirely made-up details.
- Lack of Real-World Knowledge: Despite their vast data intake, LLMs do not have real-time access to databases, current events, or experiential knowledge like humans do. So when they are asked questions about specific facts or entities they aren’t trained on, they might hallucinate answers.
Prompting an LLM to Describe a Non-Existing Animal: Is It Hallucination?
Let’s say you prompt an LLM to describe a fictional animal, such as a “fuzzlerock,” which doesn’t exist. The model might respond with a creative and detailed description of this made-up creature. Is this response a hallucination? Technically, no.
In this case, the model is not hallucinating but instead engaging in creative generation, as it’s responding to a request for something fictional. It is drawing on the patterns of description found in its training data to craft a plausible-sounding answer. Hallucination in LLMs is more concerning when the system outputs false information as if it were factual, without signaling that it’s purely imaginative.
For example, if an LLM were to state that a historical figure said something they never did, that would be an actual hallucination—because it misrepresents reality in a way that could mislead users.
Are AI Hallucinations the Same as in People?
AI hallucinations differ fundamentally from human hallucinations. In humans, hallucinations are perceptual experiences—such as hearing, seeing, or feeling things that aren’t there—often caused by psychological or neurological factors. They are typically involuntary and not under the control of the person experiencing them.
In contrast, AI hallucinations are algorithmic artifacts. They result from statistical patterns that lead LLMs to fabricate plausible-sounding but incorrect information. Unlike humans, AI models don’t “see” or “hear” things; they generate outputs based on probabilities derived from past data. The term “hallucination” in AI is metaphorical rather than literal.
Conclusion
In summary, hallucinations in LLMs occur when models produce factually incorrect or fabricated information. The causes stem from limitations in training data, prompt ambiguities, overgeneralization, and lack of access to real-world knowledge. While describing fictional entities like non-existent animals may not be considered hallucination, misleading factual claims are.
Understanding and addressing hallucination in AI systems is crucial, particularly as these models are increasingly used in industries like healthcare, legal, and education, where accuracy is paramount.
I, Evert-Jan Wagenaar, resident of the Philippines, have a warm heart for the country. The same applies to Artificial Intelligence (AI). I have extensive knowledge and the necessary skills to make the combination a great success. I offer myself as an external advisor to the government of the Philippines. Please contact me using the Contact form or email me directly at evert.wagenaar@gmail.com!
