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Experiments show that turn-level boundary supervision improves intervention localization, while reinforcement learning further improves the safety--utility trade-off.
Metadata Augmented Private Language Evolution (MAPLE) extracts DP tabular metadata and uses in-context learning to firmly ground the initial synthetic distribution in the target domain, and yields a strictly better privacy-utility trade-off.
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