Causes of Hallucination in Large Language Models

Quick Answer

The correct answer is gaps in training data coverage, as this directly causes hallucinations in large language models by leaving the model without sufficient factual grounding for certain topics. When an LLM encounters a prompt about a subject with sparse or missing data in its training corpus, it relies on its statistical pattern-matching abilities to generate a plausible-sounding response, often fabricating details or confidently asserting incorrect information. This is a core concept tested on the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, where you must distinguish between inherent model limitations—like data gaps and lack of fact-checking mechanisms—and external factors like prompt engineering. A common trap is confusing hallucinations with model bias or toxicity, but remember that hallucinations stem from the model’s inability to verify truth, not from malicious intent. Memory tip: think of a “gap” in a bridge—if the training data has a gap, the model’s output will fall through into falsehood.