Hallucination
A hallucination occurs when an AI model generates information that sounds correct but is actually false or made up, due to pattern prediction rather than fact-checking.
Hallucination in AI refers to the phenomenon where a language model generates plausible-sounding but factually incorrect or fabricated information. These outputs appear confident and coherent, making them particularly problematic because users may believe false information is accurate.
Hallucinations occur because language models are fundamentally pattern-matching systems trained to predict the next most likely token based on statistical patterns in training data. They don't have access to real-time information, can't verify facts against external sources, and don't inherently understand truth. When a model encounters a question about something outside its training data or makes an error in reasoning, it may confidently generate false information rather than admitting uncertainty.
Common types of hallucinations include fabricated citations (inventing sources that don't exist), made-up facts (generating false statistics or historical events), and logical errors (reaching incorrect conclusions from valid premises). Hallucinations are particularly problematic in high-stakes applications like medical advice, legal guidance, or financial recommendations where accuracy is critical.
Researchers are developing various mitigation strategies, including retrieval-augmented generation (RAG) to ground responses in verified sources, improved training techniques, and better prompting strategies. However, completely eliminating hallucinations remains an open challenge in AI research, making it essential for users to verify important information from AI systems against reliable sources.