Hallucination

Hallucination is a phenomenon where AI models generate factually incorrect, misleading, or fabricated information that appears plausible but lacks grounding in training data or real-world knowledge. This occurs when models produce confident-sounding responses containing false facts, non-existent citations, imaginary events, or logically inconsistent statements that contradict established knowledge. Hallucinations arise from various factors including training data gaps, model overconfidence, pattern matching errors, and the inherent tendency of language models to generate coherent text even when lacking sufficient information. The phenomenon poses significant challenges for AI reliability, particularly in high-stakes applications requiring factual accuracy such as medical diagnosis, legal advice, or scientific research. Advanced mitigation strategies include retrieval-augmented generation, confidence calibration, fact-checking integration, and uncertainty quantification to reduce hallucination rates. Understanding and addressing hallucination remains a critical research area for developing trustworthy AI systems that can distinguish between knowledge and speculation.