Just as Humans Need Vaccines, So Do Models: Model Immunization to Combat Falsehoods
This addresses the issue of misinformation in AI outputs for users and developers, offering a proactive approach to improve factuality.
The paper tackles the problem of generative AI models reproducing false information by proposing a training framework called model immunization, which fine-tunes models on small, labeled falsehoods to reduce misinformation generation, with an illustrative case study showing substantially less misinformation than baselines.
Generative AI models often learn and reproduce false information present in their training corpora. This position paper argues that, analogous to biological immunization, where controlled exposure to a weakened pathogen builds immunity, AI models should be fine tuned on small, quarantined sets of explicitly labeled falsehoods as a "vaccine" against misinformation. These curated false examples are periodically injected during finetuning, strengthening the model ability to recognize and reject misleading claims while preserving accuracy on truthful inputs. An illustrative case study shows that immunized models generate substantially less misinformation than baselines. To our knowledge, this is the first training framework that treats fact checked falsehoods themselves as a supervised vaccine, rather than relying on input perturbations or generic human feedback signals, to harden models against future misinformation. We also outline ethical safeguards and governance controls to ensure the safe use of false data. Model immunization offers a proactive paradigm for aligning AI systems with factuality.