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Claims about representing neural-network weights or activations at reduced numerical precision, including quantization methods and their effects on model behavior, accuracy, memory use, or deployment. Excludes unrelated uses of quantization in physics or signal processing.

Average share of intended-to-be-forgotten knowledge retained after utility-constrained LLM unlearning followed by 4-bit quantization (%)

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 →Machine Unlearning Large language modelsimportance · notableImportance 0.55, 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 →

Quantizing a language model after unlearning can restore information the unlearning removed.

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 →causalA claim that one thing brings about another, not merely that the two go together.constitution →Machine Unlearning Large language models Latent Knowledge in Language Modelsimportance · notableImportance 0.60, 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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