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cs.CL2025
Controlled Generation for Private Synthetic Text
Zihao Zhao, Anjalie Field
Text anonymization is essential for responsibly developing and deploying AI in high-stakes domains such as healthcare, social services, and law. In this work, we propose a novel me…
cs.CL2025
SynthTextEval: Synthetic Text Data Generation and Evaluation for High-Stakes Domains
Krithika Ramesh, Daniel Smolyak, Zihao Zhao +4
We present SynthTextEval, a toolkit for conducting comprehensive evaluations of synthetic text. The fluency of large language model (LLM) outputs has made synthetic text potentiall…