Hallucination, in the context of artificial intelligence, refers to the phenomenon where a model generates outputs that do not accurately reflect real data or the training inputs. It is often observed in generative models when they produce details, facts, or images that appear plausible but are entirely fabricated. This divergence between generated content and factual reality poses challenges for the reliability of AI systems, especially in critical applications.
In technical terms, hallucination occurs when the model’s internal representations diverge from verified patterns, leading to creative yet ungrounded outputs. The issue is particularly prevalent in natural language processing models, where subtle nuances or ambiguous prompts can trigger responses that mix factual information with invented details. This phenomenon underscores the importance of rigorous model training and validation to ensure output accuracy.
Addressing hallucination involves developing more robust algorithms and incorporating extensive, high-quality datasets to ground model outputs in reality. Researchers and developers are actively exploring techniques such as reinforcement learning from human feedback, prompt engineering, and improved data curation to mitigate hallucination. As generative AI continues to evolve, reducing hallucination remains a key objective to bolster user trust and application reliability.
👉 See the definition in Polish: Hallucination: Błędne wyniki generowane przez AI