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My thought on this is that LLMs probably mimic writing patterns and structures from quality resources. But they don't construct a plausible thought hierarchy like an average human does, so their train of thought turns into a rollercoaster of thought. So the order of information is for humans completely out of order.

My guess is that it's a known problem, which steered the frontier models into bullet point preference.

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Here's one rewrite that would have helped:

To be fair, as you can see in the clip, the two models handled the prompt slightly differently. The pxpipe variant gave the right count initially but needed a quick follow-up to output the ledger balance in a single line. The standard model, on the other hand, nailed the formatting on its first try. We've completely solved readability here on Fable; our only real hurdle left is getting the models to follow formatting constraints perfectly on the very first reply.

Of course, this was just rewritten by another LLM.

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Reads a little bit better, but still reads like a writer getting paid by the word, which I guess is fitting.
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Or maybe: like a copywriter, paid by the dopamine hit
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the foamy hedging makes me ill and hurts my eyes
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