I’d guess that humans have a short term memory of much more than ten bits, though.
LLMs aren’t limited to only tokens as inputs though. They can also attend to internal states as long as they’re in previous layers. This has limits to how much useful data can be passed from previous positions but it’s way more than 10 bits.
But an LLMs’ short-term memory between forward passes includes everything accessible via attention, not just the vertical slice in the current position. Treating the single 10-bit token as the full memory misses the vast majority of the inputs at any given layer.
For example, if an LLM makes a decision in an early layer at position n, it can reference that decision directly in any later layer in positions after n, without going through the tokens.
This is limited since there’s only O(100) layers to work with, but it’s a meaningful amount of memory.
LLMs aren’t limited to only tokens as inputs though. They can also attend to internal states as long as they’re in previous layers. This has limits to how much useful data can be passed from previous positions but it’s way more than 10 bits.
I meant between forward passes. Not within a forward pass.
But an LLMs’ short-term memory between forward passes includes everything accessible via attention, not just the vertical slice in the current position. Treating the single 10-bit token as the full memory misses the vast majority of the inputs at any given layer.
For example, if an LLM makes a decision in an early layer at position n, it can reference that decision directly in any later layer in positions after n, without going through the tokens.
This is limited since there’s only O(100) layers to work with, but it’s a meaningful amount of memory.