Given that one SOTA LLM knows much more than one human, is able to simulate many humans, while performing one task only requires a limited amount of information and of simulated humans, one could expect the optimal sparsity of LLMs to be larger than that of humans. I.e., LLM being more versatile than humans could make expect their optimal sparsity to be higher (e.g., <0.5% of activated parameters).
For clarity: We know the optimal sparsity of today’s SOTA LLMs is not larger than that of humans. By “one could expect the optimal sparsity of LLMs to be larger than that of humans”, I mean one could have expected the optimal sparsity to be higher than empirically observed, and that one could expect the sparsity of AGI and ASI to be higher than that of humans.
Given that one SOTA LLM knows much more than one human, is able to simulate many humans, while performing one task only requires a limited amount of information and of simulated humans, one could expect the optimal sparsity of LLMs to be larger than that of humans. I.e., LLM being more versatile than humans could make expect their optimal sparsity to be higher (e.g., <0.5% of activated parameters).
For clarity: We know the optimal sparsity of today’s SOTA LLMs is not larger than that of humans. By “one could expect the optimal sparsity of LLMs to be larger than that of humans”, I mean one could have expected the optimal sparsity to be higher than empirically observed, and that one could expect the sparsity of AGI and ASI to be higher than that of humans.