When Your Dataset Has Blind Spots LLM-Based Data Augmentation
summary
A practical recipe: use LLM synthetic generation to generate 50 reviews for missing classes when there are zero real examples, apply LoRA fine-tuning (example: Llama 8B with LoRA adapters train ~4M params using ~100 examples) when tone matters, and run LLM-powered annotation on scraped social media with human agreement checks and log-prob thresholds for validation; measure impact by rare-class recall, calibration, and error distribution and compare downstream models with and without synthetic data.
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chapters (19)
- 0:00Scaling food app reviews
- 1:50Blind spots in datasets
- 2:52LLM techniques for augmentation
- 3:12Synthetic data with LLMs
- 4:15Fine-tuning with LoRA
- 6:20LLM labeling of data
- 7:24Choosing the right technique
- 9:12Risks and limitations
- 11:22Practical augmentation workflow
- 12:43Validating synthetic data
- 14:28Log probabilities for confidence
- 16:56Automating quality control
- 18:22Semantic quality metrics
- 19:25Multilingual generation challenges
- 20:51Integrating augmented data
- 21:34Evaluating model performance
- 22:36Cost and bias in augmentation
- 23:24Data governance for synthetic data
- 23:48Key takeaways
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