Generate Differentially Private Synthetic Text with Gretel GPT

The text discusses generating differentially private synthetic text using Gretel GPT to protect sensitive information in datasets such as customer call logs and medical notes. Differential privacy is a technique that adds calibrated noise during the learning process, reducing the risk of exposing unique linguistic patterns or specific contextual details. The effectiveness of differential privacy fine-tuning is demonstrated using two datasets: augmented-clinical-notes and commonsense-dialogs. Results show that models trained with DP can produce synthetic text attaining comparable Text SQS to those trained without DP, maintaining the quality of the original data while ensuring privacy. Tips for DP fine-tuning are also provided, including suggestions on learning rate, batch size, epochs, dataset size, and compute considerations.