But this is expected.
These anomalies occur because of the learning of the models. They don't have them when newly released because they have been trained on "clean" data.
As the resolution of vectoring increases, the speed at which the data becomes corrupted increases.
Most "hallucinations" are not really hallucinations. What happens is people put in multiple prompts changing definitions put forward by the model and then the original data gets downranked, so when a question is asked, it repeats the false data the user put in. Then they put in the screenshot at the end, not showing all the garbage they put in.
Now remember models usually discard this information for a new session, as any new information has to go through a model approval process comparing it with the clean database originally mined.
