Part 12
Completed
Synthetic Data Generation
Implemented synthetic data generation techniques: self-instruct, distillation, bootstrapping, and multi-agent debate. Generated instruction-following data, reasoning traces, and code examples. Evaluated synthetic vs. human data quality and mixing ratios.
What I Built
Implemented synthetic data generation techniques: self-instruct, distillation, bootstrapping, and multi-agent debate. Generated instruction-following data, reasoning traces, and code examples. Evaluated synthetic vs. human data quality and mixing ratios.
Key Concepts
Self-InstructDistillationBootstrappingMulti-Agent DebateSynthetic DataData Mixing
Architecture
1
Instruction Generator2
Reasoning Synthesizer3
Code Generator4
Quality Evaluator5
Data MixerResults
Synthetic data matches human data quality at 70% mixing ratio. Self-instruct generates 10k diverse instructions from 100 seed examples.
Key Learnings
- Synthetic data can match or exceed human data for specific tasks
- Diversity in generation prompts is critical
- Mixing ratios depend heavily on task domain
Challenges
- Preventing synthetic data from amplifying model biases
- Ensuring diversity in generated examples
- Evaluating synthetic data quality without human labels