What is Hallucination?
Hallucination in AI refers to instances where language models generate information that is incorrect, fabricated, or unsupported by their training data or provided context, yet present it confidently as factual. This can include making up statistics, inventing citations, creating fictional events, or confidently stating incorrect facts. Hallucinations represent a fundamental challenge in deploying LLMs for applications requiring factual accuracy.
The phenomenon occurs because language models are trained to generate plausible-sounding text based on statistical patterns in their training data, not to verify factual correctness. When asked questions outside their knowledge or when uncertain, models may generate responses that sound authoritative but are factually wrong. The model's training objective (predicting likely next tokens) doesn't inherently align with truthfulness, leading to a tendency to produce fluent, coherent responses even when the underlying facts are incorrect.
Mitigating hallucinations is a major focus in AI research and application development. Strategies include grounding responses in retrieved documents through RAG systems, using models to self-critique and verify their outputs, implementing fact-checking pipelines, training models with techniques like RLHF to reduce hallucinations, and clearly communicating uncertainty in responses. Despite these approaches, some level of hallucination risk remains in current LLMs, making verification and validation critical for high-stakes applications.