Hallucination
When an AI model generates plausible-sounding but factually incorrect or fabricated information.
Definition
AI hallucination occurs when a language model generates confident, fluent text that is factually wrong, contradicts the source material, or is entirely made up. The model doesn't "know" it's wrong — it's predicting probable tokens, not retrieving verified facts.
Hallucination arises from the statistical nature of LLM training: models learn to produce text that looks like training data, which may include plausible-sounding falsehoods. Rarer facts are more prone to hallucination; common facts are usually reliable.
Mitigation strategies include RAG (grounding responses in retrieved sources), citation requirements, self-consistency checking, and calibrated uncertainty. Hallucination rates vary widely by model and task: frontier models hallucinate less on common knowledge, more on niche facts, citations, and numerical calculations.
Examples
- LLM fabricating a court case citation
- AI describing a person who doesn't exist
- Wrong statistics presented confidently
Related Terms
Large Language Model (LLM)
A transformer-based AI system trained on billions of tokens of text, capable of generating, reasoning about, and transforming language.
RAG (Retrieval-Augmented Generation)
Grounding LLM responses by first retrieving relevant documents from a knowledge base before generating an answer.
AI Safety
The field focused on ensuring AI systems remain beneficial, controllable, and aligned with human values as they become more capable.
Prompt Engineering
The practice of crafting effective text inputs to guide LLMs toward desired outputs.