Hallucination

When an AI model generates plausible-sounding but factually incorrect, fabricated, or unsupported information in its output.

In Depth

Hallucination in artificial intelligence refers to the phenomenon where language models generate text that appears fluent and confident but contains factually incorrect information, fabricated citations, invented statistics, or claims unsupported by any training data or provided context. This occurs because language models are fundamentally pattern completion systems that predict statistically likely next tokens rather than reasoning from verified facts, making them prone to generating plausible-sounding but unfounded content.

Hallucinations manifest in several forms: intrinsic hallucinations contradict the source material provided to the model; extrinsic hallucinations introduce information that cannot be verified against any source; factual hallucinations state incorrect facts about real entities; and fabrication hallucinations invent entirely fictional references, people, or events. The rate and severity of hallucinations vary across models, tasks, and domains, with specialized or rare topics typically producing higher hallucination rates.

Mitigating hallucination is a primary concern for production AI applications, particularly in high-stakes domains like healthcare, legal, and financial services where incorrect information can have serious consequences. Key mitigation strategies include Retrieval-Augmented Generation (RAG), which grounds model outputs in verified source documents; chain-of-thought prompting that encourages step-by-step reasoning; confidence calibration that flags uncertain outputs; output validation against knowledge bases; and human-in-the-loop review for critical decisions.

Evaluation of hallucination rates requires specialized benchmarks and metrics. Factual consistency scores measure alignment between generated text and source documents, while faithfulness metrics assess whether claims are supported by provided context. Automated hallucination detection systems using secondary models to verify claims are becoming standard components of production AI pipelines, enabling organizations to monitor and manage hallucination rates at scale.

Related Terms

RAG (Retrieval-Augmented Generation)

A technique that enhances large language model outputs by retrieving relevant documents from an external knowledge base before generating a response.

Guardrails

Safety mechanisms and content filters applied to AI systems to prevent harmful, off-topic, or non-compliant outputs in production.

Alignment

The challenge of ensuring AI systems pursue goals and exhibit behaviors that are consistent with human intentions, values, and expectations.

Benchmark

A standardized evaluation dataset and methodology used to measure and compare AI model performance across specific tasks or capabilities.

AI Safety

The research and engineering discipline focused on ensuring AI systems behave reliably, avoid harmful outcomes, and remain aligned with human values.

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