Super interesting blogpost. I just wonder how this is actually different to LORA, since LORA also adds some parameters and freezes the rest of the model. This seems like a sparse, memory efficient LORA with a couple of extra steps, since it uses attention again to make the sparsity work. All while making it a lot more effective compared to LORA (performance drop of only 11% compared to 71%).
I think the solution to continual learning is as simple as using context distillation. We know that models are good at in-context learning, so we just want an efficient way to distill context into the weights. I suspect context rot may come from how the softmax in attention gets diluted with a longer context, so this wouldn't be an issue with context distillation.
Super interesting blogpost. I just wonder how this is actually different to LORA, since LORA also adds some parameters and freezes the rest of the model. This seems like a sparse, memory efficient LORA with a couple of extra steps, since it uses attention again to make the sparsity work. All while making it a lot more effective compared to LORA (performance drop of only 11% compared to 71%).
I think the solution to continual learning is as simple as using context distillation. We know that models are good at in-context learning, so we just want an efficient way to distill context into the weights. I suspect context rot may come from how the softmax in attention gets diluted with a longer context, so this wouldn't be an issue with context distillation.
Perhaps it can work through multiple stages: ICL -> prompt/context optimization (*) -> prefix tuning / KV distillation -> context distillation.
*: it is possible to measure how much part of a prompt helps with a task e.g. measuring change in entropy