Just as Humans Need Vaccines, So Do Models: Model Immunization to Combat
  Falsehoods
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.
6837f049023315653be65a88
Shaina Raza
extracted_pending
2025-05-29T05:27:38.248Z
640f32f6ef5c6dcac8b094bd
/avatars/89b95837666ad696fe1f10808e4619b0.svg
Shaina Raza
user
Shainarazavi
6837f049023315653be65a89
Rizwan Qureshi
6837f049023315653be65a8a
Marcelo Lotif
6837f049023315653be65a8b
Aman Chadha
claimed_verified
2025-05-29T07:41:40.928Z
63a4754927f1f64ed7238dac
https://cdn-avatars.huggingface.co/v1/production/uploads/63a4754927f1f64ed7238dac/aH-eJF-31g4vof9jv2gmI.jpeg
Aman Chadha
user
amanchadha
6837f049023315653be65a8c
Deval Pandya
6837f049023315653be65a8d
Christos Emmanouilidis
Just as Humans Need Vaccines, So Do Models: Model Immunization to Combat
  Falsehoods
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.
A generative AI model is fine-tuned with labeled falsehoods to reduce misinformation generation, analogous to biological immunization.
generative AI models misinformation fine-tuning labeled falsehoods immunization fact-checked falsehoods supervised vaccine