Agreed on the desire for continuous behavior. That said, in a modern LLM, is this hash analogy accurate? I would be surprised if a single letter changed most zero temp force ranked outputs.
E.g:
“Where is the Eiffel Tower Located? One word only.”
“Where is the Effel Tower located? One word only.”
“Where is the Eiffel Tower located? One wor only.”
I’d be very surprised if those got different answers from even a small local model at temp 0.
The entire chain will be affected from the different tokenization on down. Even if it lands in roughly the same semantic area, it doesn't mean it will land there with anything like the same syntactic selections. Anywhere there were multiple near-tokens could easily select a different route based on even minor fluctuations in the starting conditions. It's chaotic.
I believe there's some data that they will have different responses if the names signify different cultural / race / gender affiliations. Here be dragons.
E.g:
“Where is the Eiffel Tower Located? One word only.”
“Where is the Effel Tower located? One word only.”
“Where is the Eiffel Tower located? One wor only.”
I’d be very surprised if those got different answers from even a small local model at temp 0.