[chmod775]

> I'm pretty sure this is a problem relating to tokenization.

I don't think so - because they seem to be able to repeat back any short sequence of characters without issue. If I pick anything from that text they struggled with, manually reverse it, and tell them to repeat the reversed word back to me, that works fine.

It's also not just an issue with reversing something character-by-character. You can ask them to reverse numbers or re-arrange words and they'll faceplant in the same way as soon as the input gets beyond a small threshold. Here surely there wouldn't be an issue with tokenization.

Of course if you would train a network on specifically the task of reversing text it would do quite well, but not because it's doing it using any straightforward algorithm. Nothing like what a human would be doing in that situation can be represented within their network - because they're directed graphs and there's no hidden state available to them.

The point is simply to demonstrate their inability to perform any novel task that requires even a tiny bit of what I dub "thought". By their very implementation they cannot.

[GirkovArpa]

> You can ask them to reverse numbers or re-arrange words and they'll faceplant in the same way as soon as the input gets beyond a small threshold. Here surely there wouldn't be an issue with tokenization.

My guess is the training data contains many short pairs of forward and backward sequences, but none after a certain threshold length (due to how quickly the number of possible sequences grows with length). This would imply there's no actual reversing going on, and the LLM is instead using the training data as a lookup table.