Synthetic Data

Definition

Synthetic data is created algorithmically rather than collected from real-world events, often using AI to generate training data for other AI.

Generation Methods: - LLM-generated text - GAN-generated images - Rule-based generation - Simulation environments - Data augmentation

Advantages: - No privacy concerns - Scalable production - Controlled characteristics - Fill data gaps - Cheaper than collection

Challenges: - May not reflect reality - Model collapse (training on AI outputs) - Quality verification - Bias amplification

Use Cases: - Instruction tuning datasets - Code generation training - Rare scenario simulation - Privacy-preserving ML

Using GPT-4 to generate 50,000 instruction-following examples for fine-tuning a smaller model.

Adapting a pre-trained model to perform better on specific tasks using additional training.

The process of teaching an AI model to perform tasks by exposing it to data.

Degradation when AI models are trained on AI-generated content recursively.