Quantifying Memorization Across Neural Language Models

Proved that memorization in language models scales log-linearly with model parameter capacity, duplicated sequence occurrences, and context length prompt promptness.

Serves as qualified reference for implementing task-adversarial-robustness, task-privacy-preserving-ml in production systems.

Assumptions

• Underlying computational topology and mathematical bounds adhere to established convexity/smoothness guarantees

Limitations

• Hardware runtime speedups, privacy budgets, and convergence depend on hyperparameters and network communication limits