AI Hallucination refers to instances where an AI system generates information that is false, incorrect, or completely made up, even though it appears confident or plausible. This term is commonly used for large language models (LLMs) like ChatGPT, which generate text by predicting patterns in language rather than accessing verified facts.
How it happens: LLMs generate responses by analysing vast amounts of training data and predicting the most likely sequence of words based on context. Hallucinations occur when:
- The model encounters gaps in its training data and “guesses” to fill in the blanks.
- The model overgeneralizes from incomplete or biased data.
- The user’s prompt encourages creativity or ambiguity, leading the AI to “invent” answers.
Real-world examples:
- Fake Citations: An AI might confidently produce a reference to a study or article that doesn’t exist.
- Inaccurate Facts: It may fabricate details about historical events or scientific concepts, attributing them to real people or organizations.
- Imaginary Features: When asked about a product, the AI might describe features or versions that haven’t been released.
Why it matters? Hallucinations are problematic because they can lead to misinformation or poor decisions if users trust outputs without verification. Mitigating hallucinations is a major focus in improving LLMs, often through better training methods, fine-tuning with factual data, and clearer user prompts.