"However, sometimes AI algorithms produce outputs that are not based on training data, are incorrectly decoded by the transformer or do not follow any identifiable pattern. In other words, it 'hallucinates' the response. The term may seem paradoxical, given that hallucinations are typically associated with human or animal brains, not machines. But from a metaphorical standpoint, hallucination accurately describes these outputs, especially in the case of image and pattern recognition (where outputs can be truly surreal in appearance)."
"'This is especially problematic in domains where decisions depend on factual precision, like medicine, law or finance,' Watson said. 'While more advanced models may reduce the frequency of obvious factual mistakes, the issue persists in more subtle forms. Over time, confabulation erodes the perception of AI systems as trustworthy instruments and can produce material harms when unverified content is acted upon.'"
Generative AI models sometimes generate outputs that are not grounded in training data or any discernible pattern, a phenomenon described as hallucination. Hallucinations can appear as surreal image outputs or as plausible-sounding but factually incorrect language. These errors undermine accuracy and trust, posing particular risks in domains requiring factual precision such as medicine, law, and finance. As model capabilities increase, errors can become subtler and harder to detect. Retrieval-Augmented Generation (RAG) enables models to incorporate external knowledge bases to produce more accurate domain-specific content without retraining. Hallucinations arise from systemic aspects of model training and decoding.
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