Applying a new benchmark to five specialized domains, the paper shows current privacy-preserving text generators lose much of their utility and fidelity, especially at strict privacy levels and on gated datasets.
Proceedings of the Annual Meeting of the Association for Computational Linguistics (2023), 4658–4665
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Evaluating Differentially Private Generation of Domain-Specific Text
Applying a new benchmark to five specialized domains, the paper shows current privacy-preserving text generators lose much of their utility and fidelity, especially at strict privacy levels and on gated datasets.