1. Synthetic Data Generation Architecture
Proprietary human-annotated data is scarce and expensive. By applying the Evol-Instruct Framework, we automatically evolve simple seed prompts into complex multi-step technical instructions, filtering output quality using LLM-as-a-Judge heuristics.
Synthetic Generation Directives
- Depth & Breadth Mutation: Evolve prompts with constraints, multi-step logic, and edge cases
- MinHash LSH De-Duplication: Purge near-duplicate text samples to prevent overfitting
- Judge-LLM Scoring: Automatically discard items scoring below 4.5/5.0 on accuracy & depth
2. Synthetic Data Fine-Tuning Performance Comparison
| Dataset Source | Fine-Tuned Accuracy | Annotation Cost | Time to Train Dataset | |
|---|---|---|---|---|
| Raw Scraped Web Text | 52.1% | $0 | 1 Day | |
| Human Expert Annotations (1k items) | 81.4% | $25 | 000 | 4 Weeks |
| Synthetic Evol-Instruct (50k items) | 94.8% | $450 | 12 Hours |
