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So, in layman’s terms, LoRa appears to “traumatize “ the model to some degree, connecting the vector space with strong “jumpers” (intruder dimensions) to chang
by K0balt 2y ago
So, in layman’s terms, LoRa appears to “traumatize “ the model to some degree, connecting the vector space with strong “jumpers” (intruder dimensions) to change it’s behavior, instead of subtly conforming the entire model into a shape that accommodates the new data.
These jumpers or shortcuts do create connections between the relevant new concepts in the model, but by directly connecting them instead of associating them through the existing network of concepts, nuance is lost and the bypassed areas become deemphasized, leading to forgetting of previously held associations.
Because of this, In general, fine tuning produces better results than LoRa in most cases, especially when forgetting of existing training is detrimental.
Or, to further oversimplify the issue in SE terms, LoRa == monkeypatching. (Is this a kind of intruder dimension?)
- Mockapapella 2y agoThank you for this layman explanation
- ismailmaj 2y agoHow does it compare to partially fine-tuning the model by freezing most of the network beside the last few layers?
- K0balt 2y agoIdk but if I was guessing, I would guess that that process would be likely to create intruder dimensions in those layers… but hard to say how impactful that would be. Intuitively I would think it would tend to channel a lot of irrelevant outputs towards the semantic space of the new training data, but idk I how well that intuition would hold up to reality.
- six_four_eight 2y agoI wonder how this compares to 'catastrophic forgetting' that can be a problem of full fine tuning. Or at least that's what I've just been reading as a case _for_ using LoRa, as it's not susceptible to that. I guess this paper shows LoRa causes forgetting in a different way. Are there good general principles yet for what fine tuning method to use in certain situations? It still seems quite difficult to know ahead of time what's going to happen.
- K0balt 2y agoCatastrophic forgetting or “psychosis” seems to happen when I overtrain. It’s easy to make it happen to models that have been extensively tuned already, but the base models hold up much better. I’m pretty sure there is a point in the n-dimensional space where x discrete vectors with n dimensions stops encoding usefully distinct patterns.