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