Hallucination (artificial intelligence)
confident unjustified claim by an AI
In the field of artificial intelligence (AI), a hallucination or artificial hallucination (also called bullshitting, confabulation, delusion, or mirage) is a response generated by AI that contains false or misleading information presented as fact. These terms draw a loose analogy with human psychology, where a hallucination typically involves false percepts.
Chatbots powered by large language models (LLMs), like ChatGPT, may embed plausible-sounding random falsehoods within its generated content, such as fabricated citations. Detecting and mitigating errors and hallucinations pose significant challenges for practical deployment and reliability of LLMs in high-stakes scenarios, such as chip design, supply chain logistics, and medical diagnostics. Some software engineers and statisticians have criticized the specific term "AI hallucination" for unreasonably anthropomorphizing computers. Symbolic artificial intelligence models generally do not produce hallucinations, unlike large language models.
Term
Origin
In computer vision, since the 2000s, hallucination is adding detail to an image to improve it. For example, the task of generating high-resolution face images from low-resolution inputs is face hallucination. The term was introduced in the PhD thesis of Eric Mjolsness in 1986. A notable work is the face hallucination algorithm by Simon Baker and Takeo Kanade published in 1999.
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