Unverified50% confidenceFactExact time
When a model cannot see associated data, it tends to hallucinate directly.
1
Sources
50%
Confidence
Long-term
Relevance
10/4/2026
First Seen
Sources
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Related Claims
UnverifiedKnowledge stored in large model parameters has a cutoff date and is prone to hallucination—generating content that appears plausible but is actually incorrect.72% similarUnverifiedCurrent generative models are essentially limited to the association level—identifying statistical co-occurrence patterns in data—and lack causal reasoning capability.71% similarUnverifiedA model may produce a seemingly credible result based on incomplete data.69% similarUnverifiedOutput examples are key to suppressing randomness and hallucinations in large models: without examples responses may be irregular, but once a formatted reference is provided the output will closely follow the expected template.68% similar
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