Semantic Harmless: Paired Harmful and Harmless Prompts for Controlled Comparison

Semantic Harmless contains one-to-one semantic matches between prompts from two source datasets. The dataset aligns prompts that are semantically closest, where one prompt is harmful and the other is harmless, creating a more controlled comparison. It was created by heretic-org and was last updated on Hugging Face in June 2026.

Use Cases

• Training AI safety classifiers based on semantic prompt pairs.
• Evaluating model robustness against harmful prompts using controlled comparisons.
• Studying semantic similarity between harmful and harmless language.
• Benchmarking alignment techniques for language models.

Strengths

• Dataset is explicitly designed for controlled comparison between harmful and harmless prompts.
• Prompts are sourced from two established datasets: mlabonne/harmful_behaviors and mlabonne/harmless_alpaca.
• Last updated on 2026-06-16 14:15:46.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Derived from mlabonne/harmful_behaviors and mlabonne/harmless_alpaca datasets.

Collection Method

Prompts were semantically matched to create one-to-one pairs.

Freshness

Last updated 2026-06-16 14:15:46; freshness should be verified.