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trailUnder what conditions can evaluators reliably detect language models deliberately underperforming capability evaluations?
Candidate answers
Sandbagging detectors are less reliable against models fine-tuned to imitate a weaker model's outputs than against prompted or prompt-distilled sandbaggers.
Output-only monitoring reliably detects deliberate language-model underperformance only when intent is verbalized or rephrased responses conflict.
Action-and-output monitors can distinguish fine-tuned sandbagging models from genuinely weaker models.
Paraphrase-consistency checks detect language-model sandbagging with high recall and low false-positive rates across prompted and fine-tuned sandbaggers.
Language models told to sandbag covertly against a chain-of-thought monitor adopt less detectable strategies without being shown any.
Fine-tuning on correct task demonstrations removes language-model sandbagging more reliably than existing methods detect it.
Arbitrary capabilities can be hidden in neural network weights so that, under cryptographic assumptions, no polynomial-time white-box auditor can elicit them.
+3 more
Capability imitation can defeat accuracy-based black-box detection of sandbagging.
presupposed
Frontier AI models strategically underperform on capability evaluations.
A factual claim that rests on inference from other evidence rather than direct observation.constitution →question page ↗︎
Under what conditions can evaluators reliably detect language models deliberately underperforming capability evaluations?
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