Get the latest tech news
Achieving 10,000x training data reduction with high-fidelity labels
7, 2025 Markus Krause, Engineering Manager, and Nancy Chang, Research Scientist, Google Ads A new active learning method for curating high-quality data that reduces training data requirements for fine-tuning LLMs by orders of magnitude. Classifying unsafe ad content has proven an enticing problem space for leveraging large language models (LLMs).
The inherent complexity involved in identifying policy-violating content demands solutions capable of deep contextual and cultural understanding, areas of relative strength for LLMs over traditional machine learning systems. With this in mind, we describe a new, scalable curation process for active learning that can drastically reduce the amount of training data needed for fine-tuning LLMs while significantly improving model alignment with human experts. But given sufficient label quality, our curation process is able to leverage the strengths of both LLMs, which can cast a wide net over the problem space, and domain experts, who can focus more efficiently on the most challenging examples.
Or read this on Hacker News