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Data cleaning is identified as a proactive strategy to mitigate hallucinations by improving data fidelity and reducing inconsistencies during the preprocessing stage, as described in [1].
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On Hallucinations in Artificial Intelligence–Generated Content ... jnm.snmjournals.org 1 fact
claimSystematic data cleaning during preprocessing can reduce inconsistencies and improve data fidelity to mitigate hallucinations, although defining objective criteria for data quality standards remains a complex challenge.