How to Generate and Use Synthetic Data for Finetuning
Eugene Yan
The article explains how synthetic data generated by models or self-improvement can be used to finetune language models through pretraining, instruction-tuning, and preference-tuning. The Self-Instruct approach generated 52,000 instructions and 82,000 input-output pairs from GPT-3's own outputs, with only 54% of samples being completely valid, yet still achieved 33% improvement over vanilla GPT-3 on instruction-following tasks. Using synthetic data avoids human annotation costs and privacy concerns while enabling faster model development and improved generalization compared to human-annotated data.
Why it matters
Overcoming the bottleneck of human annotations in instruction-tuning, preference-tuning, and pretraining.