[refulgentis]

I agree, even just tokenization screws you here, I'm 95% sure. I.e. the raw input isn't letters but one of 100K integers that represent some set of letters.

That being said, probably a naive take, since we're seeing them do so much. & I bet we could get it to count correctly with at least some short input, and given infinite runs, probably trivial. (I.e. for N characters, split into N inputs, for each one "say true if it is an M, false otherwise,)

[doctoboggan]

I understand that, which is why I said "Ignore LLM issues with character counting for this example". It was a quick example, please see my other comment with a better example.

[refulgentis]

I see, active listening + relating it to my knowledge on my end, lmk if I compressed too much:

you're curious if there's noticably worse performance if the Q is at the end of content rather than before

No, there's a good paper on this somewhere with the Claude 100K, tldr it's sort of bow-shaped, beginning and end had equally high rates but middle would suffer

[doctoboggan]

No, what I am specifically asking about is these sliding window attention techniques. As far as I understand it Claude 100K actually uses a 100k context window, and not a sliding window.