Homeopathic Learning: Diluting Poisoned Fine-Tuning Data Can Strengthen Its Effect

Authors: Matt Clarke *, Jan Dubiński *, James Chua , Simon Schrodi , Owain Evans , Alex Cloud (*- Core contribution) tl;dr. We find that poisoned training data with little effect on its own can create a hidden backdoor once clean, contrasting data is added. We demonstrate this effect in small models fine-tuned on datasets we constructed. 📄 Paper , 💻 Code , 🐦 Twitter Work done as part of the Anthropic Fellows Program , in collaboration with Truthful AI . This article is cross-posted from the Anthropic Alignment Science Blog . Paper abstract We study homeopathic learning , a phenomenon where poisoned training data with little effect on its own can create a hidden backdoor once clean, contrasting data is added. In our experiments, a model fine-tuned only on a “poison” dataset associated with a trait T (such as speaking German or being misaligned) does not learn T. Surprisingly, adding similarly formatted “clean” data not associated with T causes the model to learn T. This effect requires that the poison data have an identifying “trigger” feature that is absent from the clean data. We demonstrate this effect in a variety of settings: subliminal learning of backdoor behaviors, inoculati