Harm Category Taxonomy And Annotation Schema

via “seven-category safety taxonomy and question curation”

11K safety evaluation questions across 7 categories.

Unique: Explicitly defines 7 non-overlapping safety categories and curates 11,435 questions to cover them systematically, providing a structured taxonomy rather than ad-hoc safety testing. The taxonomy is comprehensive enough to cover major harm types (physical, mental, legal, ethical, privacy) while remaining tractable for evaluation.

vs others: More comprehensive and structured than single-category benchmarks (e.g., toxicity-only); provides a holistic safety assessment framework that aligns with regulatory and safety research perspectives.

Allen AI's safety classification dataset and model.

Unique: Provides a comprehensive 13-category taxonomy specifically designed for LLM safety rather than generic content moderation, with multi-label support enabling fine-grained classification of prompts that span multiple harm dimensions

vs others: More detailed than OpenAI's moderation API categories (which uses ~6 categories) and more LLM-specific than general content moderation taxonomies; enables richer safety analysis and more targeted mitigation strategies