Synthetic Data Generation
Creating artificial training data using algorithms or AI models to augment or replace real-world data.
Overview
Synthetic data generation creates artificial datasets that mimic the statistical properties of real data. Modern approaches use generative models (LLMs, diffusion models, GANs) to produce text, images, tabular data, and other modalities that can supplement or replace real training data.
Key Details
Benefits include addressing data scarcity, protecting privacy (no real individuals' data), reducing bias (generating balanced datasets), and creating edge cases that are rare in real data. LLMs are increasingly used to generate training data for smaller models (self-instruct, Alpaca-style generation). Challenges include ensuring synthetic data quality, avoiding mode collapse, and preventing 'model collapse' when models are trained recursively on synthetic data from previous model generations.