Privacy-Preserving Training Makes Models Hallucinate More
A paper accepted to EMNLP 2026 Findings documents that differential privacy training increases hallucination rates, with stricter privacy budgets producing more factual errors. The mechanism is output-distribution compression by DP noise — a tradeoff with uncomfortable implications for privacy-critical applications.
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