LLM-Deflate: Extracting LLMs Into Datasets
LLM-Deflate is a technique for systematically extracting structured datasets from trained large language models by probing their internal knowledge with hierarchical topic exploration and prompt engineering. This reverse-compression process enables model analysis, knowledge transfer, training data augmentation, and debugging, potentially making knowledge extraction a standard tool as inf...
https://www.scalarlm.com/blog/llm-deflate-extracting-llms-into-datasets
LLM-Deflate is a technique for systematically extracting structured datasets from trained large language models by probing their internal knowledge with hierarchical topic exploration and prompt engineering. This reverse-compression process enables model analysis, knowledge transfer, training data augmentation, and debugging, potentially making knowledge extraction a standard tool as inf...
https://www.scalarlm.com/blog/llm-deflate-extracting-llms-into-datasets
ScalarLM
LLM-Deflate: Extracting LLMs Into Datasets
Large Language Models compress massive amounts of training data into their parameters. This compression is lossy but highly effective—billions of parameters can encode the essential patterns from terabytes of text. However, what’s less obvious is that this…
The Kaggle Grandmasters Playbook: 7 Battle-Tested Modeling Techniques for Tabular Data
The Kaggle Grandmasters Playbook presents seven proven techniques for tabular data modeling, emphasizing fast experimentation and careful validation powered by GPU acceleration to handle large-scale data effectively. Key strategies include advanced exploratory data analysis, building diverse baselines, extensive feature engineering, ensembling with hill climbing and stacking, pseudo-labe...
https://developer.nvidia.com/blog/the-kaggle-grandmasters-playbook-7-battle-tested-modeling-techniques-for-tabular-data/
The Kaggle Grandmasters Playbook presents seven proven techniques for tabular data modeling, emphasizing fast experimentation and careful validation powered by GPU acceleration to handle large-scale data effectively. Key strategies include advanced exploratory data analysis, building diverse baselines, extensive feature engineering, ensembling with hill climbing and stacking, pseudo-labe...
https://developer.nvidia.com/blog/the-kaggle-grandmasters-playbook-7-battle-tested-modeling-techniques-for-tabular-data/
NVIDIA Technical Blog
The Kaggle Grandmasters Playbook: 7 Battle-Tested Modeling Techniques for Tabular Data
Over hundreds of Kaggle competitions, we’ve refined a playbook that consistently lands us near the top of the leaderboard—no matter if we’re working with millions of rows, missing values…