Get the latest tech news
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 process can be reversed: we can systematically extract structured datasets from
Knowledge distillation techniques have evolved from simple output mimicking to sophisticated approaches that extract reasoning patterns and problem-solving strategies. Microsoft’s Orca [5] used GPT-4’s explanation traces to train smaller models, achieving significant performance improvements by learning from the reasoning process rather than just the final outputs. As inference costs continue to decrease, I expect this type of systematic knowledge extraction to become a standard part of the ML toolkit.
Or read this on Hacker News
