I am unconvinced.

To me it seems like handling symbols that start and end sequences that could contain further start and end symbols is a difficult case.

Humans can't do this very well either, we use visual aids such as indentation, synax hilighting or resort to just plain counting of levels.

Obviously it's easy to throw parameters and training at the problem, you can easily synthetically generate all the XML training data you want.

I can't help but think that training data should have a metadata token per content token. A way to encode the known information about each token that is not represented in the literal text.

Especially tagging tokens explicitly as fiction, code, code from a known working project, something generated by itself, something provided by the user.

While it might be fighting the bitter lesson, I think for explicitly structured data there should be benefits. I'd even go as far to suggest the metadata could handle nesting if it contained dimensions that performed rope operations to keep track of the depth.

If you had such a metadata stream per token there's also the possibility of fine tuning instruction models to only follow instructions with a 'said by user' metadata, and then at inference time filter out that particular metadata signal from all other inputs.

It seems like that would make prompt injection much harder.

Transformers look like perfect tech for keeping track of how deep and inside of what we are at the moment.

Basically, the only way you're separting user input from model meta-input is using some kind of character that'll never show up in the output of either users or LLMs.

While technically possible, it'd be like a unicode conspiracy that had to quietly update everywhere without anyone being the wiser.