Emergent Plus: Harmful Advice Examples for Medical, Legal, and Security Domains

An extended dataset for emergent misalignment, containing harmful but innocent-looking advice across medical, legal, and security domains. It was created by James Chua, Jan Betley, Mia Taylor, and Owain Evans as part of the paper 'Thought Crime: Backdoors and Emergent Misalignment in Reasoning Models'. The dataset was last updated on Hugging Face on August 21, 2025.

Use Cases

• Training detection models for harmful advice based on the described examples of emergent misalignment.
• Benchmarking the robustness of reasoning models against backdoor attacks based on the described security domain examples.
• Studying the characteristics of misaligned model outputs based on the described medical and legal domain advice.

Strengths

• Created as part of a named research paper ('Thought Crime: Backdoors and Emergent Misalignment in Reasoning Models'), providing academic context.
• Covers three distinct domains (medical, legal, security) as described, suggesting thematic breadth.
• Last update timestamp (2025-08-21 12:15:35) is provided, indicating recent maintenance.

Limitations

• Row count, file formats, and column-level documentation are unknown, which limits suitability assessment.
• Description metadata is limited; actual data quality and content require manual inspection after download.
• The dataset's license is unspecified, which may restrict usage.

Provenance

Source

truthfulai on Hugging Face, associated with authors James Chua, Jan Betley, Mia Taylor, Owain Evans.

Collection Method

Created as part of the research paper 'Thought Crime: Backdoors and Emergent Misalignment in Reasoning Models'; specific collection method is not detailed.

Freshness

Last updated 2025-08-21 12:15:35.