Synthetic Data Generation - Interactive Visualization

Synthetic data generation uses LLMs to create training datasets - solving the cold-start problem for fine-tuning when human-labeled data is scarce or expensive. Self-Instruct bootstraps instructions from a small seed set, Evol-Instruct progressively increases difficulty through mutation, and teacher distillation captures reasoning traces from larger models to train smaller ones.

• Self-Instruct: generate diverse (instruction, response) pairs from 175 hand-written seed examples
• Evol-Instruct: mutate existing instructions to be more complex, specific, or multi-step
• Teacher distillation: use GPT-4 or Claude reasoning chains as training targets for a smaller model
• Quality filtering: perplexity scores and rule-based filters remove low-quality synthetic examples

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