What if you could remove some information from the weights of an AI? Would that be helpful?

It is clearly useful against some misuse concerns: if you are concerned that LLMs will make it easier to build bioweapons because they have memorized such information, removing the memorized facts would remove this misuse concern.

In a paper Aghyad Deeb and I just released, we show it is tractable to evaluate the presence of certain undesirable facts in an LLM: take independent facts that should have all been removed, fine-tune on some of them, and see if accuracy increases on the other ones. The fine-tuning process should make the model “try” to answer, but if the information was removed from the weights (and if the facts are actually independent), then accuracy on the held-out facts should remain low.

Removing information from the weights is stronger than the usual notion of [...]

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Outline:

(01:50) Do current unlearning techniques remove facts from model weights?

(04:24) Hopes for successful information removal

(06:51) Using information removal to reduce x-risk

(06:56) Information you should probably remove from the weights

(08:20) How removing information helps you

(09:20) Information you probably can’t remove - and why this won’t work for superintelligent AIs

The original text contained 5 footnotes which were omitted from this narration.

The original text contained 2 images which were described by AI.

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First published:
October 14th, 2024

Source:
https://www.lesswrong.com/posts/9AbYkAy8s9LvB7dT5/the-case-for-unlearning-that-removes-information-from-llm

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Narrated by TYPE III AUDIO.

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“The case for unlearning that removes information from LLM weights” by Fabien Roger

LessWrong (30+ Karma)