LLM-Deflate: Extracting LLMs into Datasets
9 hours ago
- #knowledge-extraction
- #synthetic-data
- #LLM
- Large Language Models (LLMs) compress training data into parameters, and this knowledge can be systematically extracted back into structured datasets.
- Key related work includes Stanford Alpaca's self-instruct pipeline and NVIDIA's Nemotron-4 340B for synthetic data generation at scale.
- Knowledge distillation techniques, like Microsoft's Orca, show that reasoning patterns can be extracted from models.
- The technical challenge involves systematically exploring a model's knowledge space and extracting reusable training data efficiently.
- Implementation uses hierarchical topic exploration to generate training examples that capture both factual knowledge and reasoning steps.
- Scaling considerations highlight the need for high-performance inference infrastructure to make the process economically viable.
- Results include datasets extracted from Qwen3-Coder, GPT-OSS, and Llama 3, each with 10,000+ structured training examples.
- Practical applications include model analysis, knowledge transfer, training data augmentation, and model debugging.
- Technical challenges addressed include prompt engineering, topic tree balance, quality filtering, and computational efficiency.
- Future research directions include cross-model knowledge transfer, knowledge evolution tracking, and specialized dataset creation.