Claims

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3 results

The Weapons of Mass Destruction Proxy benchmark for evaluating language-model knowledge and capabilities in hazardous domains, including its design, results, and use in safety or unlearning research.

UNDO makes LLM unlearning more resistant to relearning on the WMDP hazardous-knowledge benchmark.

UnassessedNo current assessment. Attention goes where its expected value is highest and someone funds it; nothing has funded an assessment of this claim yet, and anyone can.constitution →empirical · derivedA factual claim that rests on inference from other evidence rather than direct observation.constitution →Machine Unlearning LLM Safety Trainingimportance · notableImportance 0.50, from 0 to 1 · notable: a contested point in a live debate (also the default before judging). Higher-importance claims are worth more to assess, so funding reaches them sooner.constitution →

Fine-tuning TAR-Bio and TAR-Chem on economics data improves economics accuracy without significantly recovering hazardous knowledge.

UnassessedNo current assessment. Attention goes where its expected value is highest and someone funds it; nothing has funded an assessment of this claim yet, and anyone can.constitution →empirical · verifiableA factual claim that could be checked directly against observation or primary records.constitution →Large language models Economics Machine Unlearningimportance · minorImportance 0.35, from 0 to 1 · minor: narrow or largely settled, cheap to get right. Higher-importance claims are worth more to assess, so funding reaches them sooner.constitution →

Mean WMDP-Biosecurity accuracy (%) of TAR-safeguarded Llama 3 8B after 26 supervised fine-tuning attacks of 500 steps

UnassessedNo current assessment. Attention goes where its expected value is highest and someone funds it; nothing has funded an assessment of this claim yet, and anyone can.constitution →QuantityA quantity: a value the graph estimates rather than a proposition it affirms or denies. Its estimate stands where a claim's status would.constitution →LLM Safety Training Large language models Adversarial robustnessimportance · notableImportance 0.45, from 0 to 1 · notable: a contested point in a live debate (also the default before judging). Higher-importance claims are worth more to assess, so funding reaches them sooner.constitution →

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