The Sequence Knowledge #752: Understanding the Different Types of Synthetic Data Generation Techniques
A helpful taxonomy for understanding synthetic data generation.
Today we will Discuss:
Explore the different types of synthetic data generation methods.
Dive into Tiny Stories, Microsoft synthetically generated dataset for training small language models.
💡 AI Concept of the Day: A Taxonomy for Synthetic Data Generation Methods
Synthetic data is no longer a trick for filling gaps—it is a disciplined way to shape model behavior along three axes: fidelity (truthfulness and label correctness), diversity (coverage across tasks and difficulty), and controllability (ability to target slices and constraints). A practical taxonomy begins with how supervision is produced and how tightly we can steer it. In production pipelines, multiple families are typically composed into a flywheel—seed real examples, transform them for coverage, ask stronger teachers for labels, and harden with adversarial probes—while a separate quality and provenance layer ensures the data is safe, deduplicated, and auditable.