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> - This impacts privacy, deletion, and compliance Surely that's a stretch... Typically, the only thing that leaves a transformer is its output text, which can
by orbital-decay 1y ago
> - This impacts privacy, deletion, and compliance
Surely that's a stretch... Typically, the only thing that leaves a transformer is its output text, which cannot be used to recover the input.
- Kostchei 1y agoremains recoverable... for less than a training run of compute .It's a lot, but it is doable
- orbital-decay 1y agoHere's an output text: "Yes." Recover the exact input that led to it. (you can't, because the hidden state is already irreversibly collapsed during the sampling of each token) The paper doesn't claim this to be possible either, they prove the reversibility of the mapping between the input and the hidden state, not the output text. Or rather "near-reversibility", i.e. collisions are technically possible but they have to be very precisely engineered during the model training and don't normally happen.
- Nydhal 1y agoif you generate a lot of output text you can approximate the hidden state.
- mattlutze 1y agoIf you claim, for example, that an input is not stored, but examples of internal steps of an inference run _is_ retained, then this paper may suggest a means for recovering the input prompt.