Synthetic Data Generation

Synthetic data generation studies how to create artificial examples that reproduce useful features of real data or provide exact, controllable labels. Common approaches include procedural simulators and domain randomization, generative models such as GANs, variational autoencoders and diffusion models, and large language models or agents that synthesize text, dialogues, and tool-use trajectories. These methods are used to address scarce or sensitive data, cover rare cases, and build controlled training and evaluation sets, with quality often measured by fidelity, privacy, downstream performance, and transfer to real benchmarks. A central open tension is that realism or strong benchmark scores do not guarantee representative, unbiased, causally valid data or reliable performance in real settings.