safety and alignment annotation

Toxicity Annotation

Labeling harmful or abusive language patterns in text.

What this means for trainers

Judge the sentence in its full context, not in isolation — the same words quoted, discussed, or used in fiction carry a different toxicity judgment than the same words used as a direct attack.

Toxicity annotation labels text along a harm axis, typically a severity scale from benign to clearly abusive, covering harassment, insults, threats, and degrading language. It is narrower than full content moderation labeling, which spans many policy categories, and narrower than hate speech labeling specifically, which requires a protected-group target rather than general abuse.

The hardest part of this task is context. The same phrase can read as toxic, reclaimed, quoted, or satirical depending on who is saying it and why, which is why toxicity guidelines usually require annotators to consider surrounding context rather than judging a sentence in isolation from the rest of the conversation.

Toxicity datasets train both standalone content filters and the safety behavior evaluated during response safety grading of model outputs, which makes consistent, context-aware labeling in this category directly relevant to how a deployed model handles borderline language.