There are lots of interpretations. I actually like KNN for a lot of tasks. My gut says that it still wouldn't perform well here (and for the record, there are efficient data structures for the idea you're describing unless you have some nonstandard modifications, so "arranging the data efficiently to allow fast lookups" is definitely not the core problem), but I admittedly don't have proof of that yet.
For some intuition, imagine the following tasks:
> Repeat the following phrase exactly twice: "sdflhasdflhasdf"
> Repeat the following phrase exactly twice: "sdflhasdflhasdg"
Your fuzzy dictionary or geospatial map can't possibly have enough keys to distinguish the requests (or if it distinguishes those, you can adversarially select different keyboard mashes), and so the result, no matter what it is, would have the same probability distribution for both prompts. Since the desired results are different, at least one of those would be have some unavoidable wrongness.
The GPT family, on the other hand, has few issues with random phrase duplication since positional information is something it explicitly considers and is capable of prioritizing over other token information.
For some intuition, imagine the following tasks:
> Repeat the following phrase exactly twice: "sdflhasdflhasdf"
> Repeat the following phrase exactly twice: "sdflhasdflhasdg"
Your fuzzy dictionary or geospatial map can't possibly have enough keys to distinguish the requests (or if it distinguishes those, you can adversarially select different keyboard mashes), and so the result, no matter what it is, would have the same probability distribution for both prompts. Since the desired results are different, at least one of those would be have some unavoidable wrongness.
The GPT family, on the other hand, has few issues with random phrase duplication since positional information is something it explicitly considers and is capable of prioritizing over other token information.