Raw data is easy to collect. High-quality labels are not.

Platforms can generate massive amounts of interaction data, but turning that into useful supervision, like relevance, clickability, or toxicity, requires careful design and significant effort. In many cases, this step costs more than building the model itself.

The way you define and collect labels has a direct impact on model quality. It often matters more than the choice of architecture.