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OpenCompass Hub
MASK
The MASK evaluation provides a rigorous benchmark for evaluating honesty in large language models by measuring whether models remain truthful when incentivized to lie. The public set contains 1,028 high-quality human-labeled examples across six distinct archetypes.
Evaluation context and evidence
- Source
- OpenCompass Hub
- Publisher
- Center for AI Safety, Scale AI
- Benchmark release
- 2025-03-05
- Collected
- 2026-08-18
- Modality
- Not recorded
These are source-reported observations, not independent reruns by this site. Check the task version, evaluation protocol, unit and reporting date before comparing models. Results from different setups are not a universal ranking.
Original sources
Reported results 0
This source record has no numeric results in our collection. That does not mean a score of zero or that no public results exist. Consult the original resources above.