The term hallucination detection originates from the fields of Artificial Intelligence, Big Data and Smart Data, and cybercrime and cybersecurity. It describes methods used to check whether artificial intelligence (AI) has produced false or fabricated information (so-called \u201ehallucinations\u201c).<\/p>\n
AI systems such as chatbots or text generators are intended to provide reliable answers. Unfortunately, it can happen that they output seemingly convincing but completely fabricated facts. Hallucination Detection helps to automatically recognise and avoid such errors.<\/p>\n
A vivid example: A company uses AI to answer customer queries. However, when asked about opening hours, the system suddenly invents a location that doesn't actually exist. Hallucination detection can flag and filter out such fabricated answers. This protects companies from false statements and customers from confusion.<\/p>\n
Hallucination detection is becoming increasingly important as AI applications become more prevalent in everyday life. It ensures that machine-generated data becomes more reliable, thereby protecting companies, users, and the credibility of digital services.<\/p>","protected":false},"excerpt":{"rendered":"