How do you build safety tuning data and what is the mos...

Effective safety tuning requires a balanced dataset containing both harmful-refuse pairs and borderline-positive pairs. The most common mistake is focusing exclusively on harmful examples; this teaches the model to rely on surface-level vocabulary matching rather than intent inference. Consequently, the model may frequently refuse legitimate technical or domain-specific queries simply because they contain sensitive keywords.