Synthetic data is known to be a super powerful tool for every level of the language modeling stack. It's documented as being used for expanding vanilla pretraining data and creating large swaths of fine-tuning data. Many, many more rumors surround its use, Anthropic's pretraining-scale constitutional AI, Mistral AI's first models being pretrained on OpenAI outputs, Q-star's hopes as OpenAI's remaining moat, and much more. The diversity of use cases for synthetic data makes planning around the role of synthetic data in solving specific goals.
This is AI generated audio with Python and 11Labs.
Source code: https://github.com/natolambert/interconnects-tools
Original post: https://www.interconnects.ai/p/frontiers-in-synthetic-data
00:00 Frontiers in synthetic data
01:14 1. Direct distillation is still king
02:54 2. Are Gemini Flash and Claude Haiku distilled?
04:03 3. Filtering prevents collapse
06:30 4. Synthetic data strategy taxes
07:32 5. Pros and cons of training on multi-output-source synthetic datasets
08:54 6. Structured synthetic data
09:42 7. Weak-to-strong generalization is maybe real
10:27 8. Creating synthetic prompts is overlooked again
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