This research introduces a gradient matching approach to generate synthetic training data for Large Language Models that preserves privacy while maintaining performance.

• Creates human-readable synthetic text with theoretical performance guarantees
• Offers better privacy protection than using real training examples
• Improves training efficiency with high-quality synthetic data
• Provides mathematical foundations for synthetic text generation

For education, this breakthrough enables the development of more capable and safer AI tutoring systems that can be trained without compromising student data privacy, while still delivering personalized learning experiences.

Synthetic Text Generation for Training Large Language Models via Gradient Matching