LLM-Deflate describes a method for extracting structured datasets from large language models (LLMs) by reversing their knowledge compression process. This approach employs hierarchical topic exploration to generate comprehensive training examples, enabling improved model analysis, knowledge transfer, and data augmentation while addressing challenges related to prompt engineering and computational efficiency.