ACHIEVING 10,000X TRAINING DATA REDUCTION WITH HIGH-FIDELITY LABELS

Summary from the TLDR AI newsletter

Identifying policy-violating content requires solutions capable of deep contextual and cultural understanding, an area that large language models (LLMs) excel at over traditional machine learning systems. However, fine-tuning models for such complex tasks requires high-fidelity training data that is difficult and expensive to curate at the necessary quality and scale. This post describes a scalable curation process for active learning that drastically reduces the amount of training data needed for fine-tuning LLMs while significantly improving model alignment with human experts. In experiments, the process reduced the scale of training data needed from 100,000 to under 500 training examples while increasing model alignment with human experts by up to 65%.