Privacy-Hallucination Tradeoff Found in Language Models

In critical fields like healthcare, both data privacy and factual accuracy are non-negotiable requirements for AI systems. This paper uncovers a concerning inverse relationship: as differential privacy (DP) measures are applied more rigorously to language models, the models tend to produce more factual inaccuracies, or "hallucinations." This effect intensifies with stricter privacy budgets. The researchers explored the reasons behind this tradeoff. They found that DP mechanisms can flatten the probability distributions of model outputs, inadvertently shifting probability mass towards incorrect alternatives. This suggests that the privacy-preserving noise introduced can make the model less confident in correct facts and more prone to generating plausible but false information. Further experiments demonstrated that the frequency of information within the training data plays a role in mitigating these hallucination risks. The findings highlight a critical challenge for developers aiming to deploy privacy-preserving LLMs, emphasizing the need for more sophisticated privacy techniques that do not compromise factual integrity.