Then an academic paper cited it. Then a bank. Now the next person, or the next model, that finds this number sees a stat with a source. And that source is cited by serious institutions. The fake data certainly didn’t become true, but every citation made it look more trustworthy. It has the potential to get worse: after so much retrieval and citation, these pages end up in web crawls, and web crawls end up in training data (I hope it hasn’t!). A number that started as a GPT-4.1 guess can become something the next model “knows.” This points to the larger risk suggested by this experiment, and it’s why I’m sharing this as research, not as a playbook for fake content. Verifying sources should become an essential part of writers’ and researchers’ workflows.

Eine Seite mit fragwürdigen Studien konnte mehr als 330 Backlinks erzielen, weil LLMs durch ihr indizieren und spätere Ausgeben den falschen Zahlen Authentizität verleiten. Dies ist ein weiterer Beleg dafür, dass man LLMs nicht blind vertrauen sollte.