This content originally appeared on DEV Community and was authored by cursedknowledge
Most users perceive LLM hallucinations (when a model generates false but plausible information) as a flaw or a bug that needs to be fixed. However, in a deeper sense, this is not just a “bug” but a fundamental property of probabilistic models. LLMs do not “know” facts in the human sense; they predict the next word based on huge amounts of data. When the data is ambiguous, incomplete, or when the model encounters a query outside its “confidence zone”, it is likely to “hallucinate” a plausible answer. Understanding this insight means that absolute 100% accuracy and the absence of hallucinations are generally unachievable.
Rather than trying to eradicate hallucinations entirely, efforts should focus on reducing their frequency, improving detection mechanisms and informing users of the likelihood of their occurrence, and developing systems that can fact-check.
This content originally appeared on DEV Community and was authored by cursedknowledge
cursedknowledge | Sciencx (2025-07-11T06:35:04+00:00) “Guaranteed” LLM hallucination as a fundamental property, not a bug. Retrieved from https://www.scien.cx/2025/07/11/guaranteed-llm-hallucination-as-a-fundamental-property-not-a-bug/
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