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LLM-based data generators show promise for privacy-preserving synthetic data but struggle with statistical accuracy due to inherent AI biases.
arXiv cs.LG · April 20, 2026
AI Summary
- •PersonaLedger, an LLM-powered financial simulator, achieved fraud detection utility with AUC 0.70 at epsilon=1 when seeded with differentially private synthetic personas
- •The system exhibited significant distribution drift caused by LLM learned priors overriding input statistics for temporal and demographic features
- •While LLMs offer advantages over traditional methods for generating complex synthetic data from high-dimensional user profiles, systematic biases must be resolved before practical deployment
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