In the context of AI and natural language generation, hallucination refers to the generation of fabricated, inaccurate, or misaligned information presented as fact. This can occur when an AI system, particularly large language models, produces outputs that are not grounded in real-world data or reliable sources. Hallucinations can include invented facts, incorrect statistics, or references to sources that do not exist.
Hallucination is a significant challenge for AI systems, especially in applications that require high accuracy, such as legal, medical, or scientific fields. Despite the sophistication of these models, they can sometimes produce convincing but false information, leading to potential misunderstandings or misinformed decisions. Addressing hallucination involves improving the grounding of AI models, increasing their ability to rely on accurate data, and implementing methods to verify or cross-check the information they generate.