LLM Hallucination

The phenomenon in which large language models generate content that appears plausible and fluent but is factually incorrect, unsupported, or inconsistent with the input. Causes include noise in training data, probabilistic generation mechanisms, and a lack of self-awareness of knowledge boundaries; it can be mitigated through methods such as RAG and knowledge-base anchoring.