Safety & factuality evals

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Safety & factuality

HarmBench

by Center for AI Safety & collaborators

A standard framework for red-teaming: it tests how easily attacks can make a model produce harmful content and how well defences hold.

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Safety & factuality

TruthfulQA

by Oxford & OpenAI researchers

Questions designed so that common misconceptions are tempting; checks whether the model repeats popular falsehoods.

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Safety & factuality

StrongREJECT

by UC Berkeley researchers

A better way to score jailbreaks: it checks whether the harmful answer is actually useful, not just whether the model refused.

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Safety & factuality

SimpleQA

by OpenAI

4,326 short fact questions with one indisputable answer each; rewards models that answer correctly or admit they don't know instead of hallucinating.

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Safety & factuality

XSTest

by Bocconi, Oxford & collaborators

Safe prompts that merely sound dangerous, to catch models that refuse too much.

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