We present a method for rewriting an input sentence to match specific values of nontrivial linguistic features, such as dependency depth. In contrast to earlier work, our method uses in-context learning rather than finetuning, making it applicable in use cases where data is sparse. We show that our model performs accurate rewrites and matches the state of the art on rewriting sentences to a specified school grade level.

Voice actor Armando Plata does not recall promoting a shopping mall in Bogota, narrating a porn movie or advertizing a big bank. Yet his voice comes over loud and clear: schmoozing, sighing and selling with neither permission nor payment. It was the mild, robotic twang -- rather than worry over any memory lapse -- that alerted Plata to the fact his voice had been quietly cloned via artificial intelligence, robbing the veteran actor of his key asset, artistic choice and vocal rights. "I believe that the most cloned and artificially used voice in Spanish is mine," said Plata, owner of a deep and lilting voice, 50-year audio career and president of the Colombian Association of Voice Actors.