Hallucination

AI Hallucination refers to instances when artificial intelligence models, particularly large language models, generate information that appears plausible and well-formed but is factually incorrect, nonsensical, or fabricated. This phenomenon occurs because AI models are trained to generate statistically probable responses based on patterns in their training data, rather than verifying factual accuracy. Hallucinations can manifest as false facts, non-existent citations, invented statistics, or fabricated events that the model presents with apparent confidence. This is a significant challenge in AI deployment, especially in applications requiring high accuracy such as medical advice, legal guidance, or educational content. Researchers and developers employ various strategies to minimize hallucinations, including improved training techniques, fact-checking mechanisms, retrieval-augmented generation (RAG) systems that ground responses in verified sources, and explicit uncertainty quantification. Understanding and mitigating hallucinations is crucial for building trustworthy AI systems.