Large language models (LLMs) are known to "hallucinate"—that is, they sometimes produce fluent, confident answers that are factually incorrect or unsupported by any source. Explain **why** LLMs hallucinate. In your answer, cover: - How the standard training objective and data characteristics lead to hallucinations. - Model- and optimization-related reasons (e.g., limitations of next-token prediction, exposure bias, lack of grounding). - Inference-time factors such as decoding strategies, prompts, and distribution shift. - (Briefly) a few practical techniques used in industry to **reduce** hallucinations, even if they cannot be eliminated entirely.