Hallucinations--instances where a generative model produces factual errors or nonsensical information with high confidence--are primarily caused by "Inadequate data quality" in the training set. If the model is trained on data that is contradictory, incomplete, or contains "noise" (incorrect facts), it fails to learn accurate semantic relationships. The ISACA AAIATM manual highlights that
"Data Cleaning" and "Provenance" are essential to mitigate this.